An introduction to R - cran.r

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An introduction to R Longhow Lam Under Construction Feb-2010 some sections are unfinished!

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Contents 1 Introduction 1.1 What is R? . . . . . . . . . . . . . . . . . . . 1.2 The R environment . . . . . . . . . . . . . . . 1.3 Obtaining and installing R . . . . . . . . . . . 1.4 Your first R session . . . . . . . . . . . . . . . 1.5 The available help . . . . . . . . . . . . . . . . 1.5.1 The on line help . . . . . . . . . . . . . 1.5.2 The R mailing lists and the R Journal 1.6 The R workspace, managing objects . . . . . . 1.7 R Packages . . . . . . . . . . . . . . . . . . . 1.8 Conflicting objects . . . . . . . . . . . . . . . 1.9 Editors for R scripts . . . . . . . . . . . . . . 1.9.1 The editor in RGui . . . . . . . . . . . 1.9.2 Other editors . . . . . . . . . . . . . .

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7 7 8 8 9 11 11 12 12 13 15 16 16 16

2 Data Objects 2.1 Data types . . . . . . . . . . . . 2.1.1 Double . . . . . . . . . . 2.1.2 Integer . . . . . . . . . . 2.1.3 Complex . . . . . . . . . 2.1.4 Logical . . . . . . . . . . 2.1.5 Character . . . . . . . . 2.1.6 Factor . . . . . . . . . . 2.1.7 Dates and Times . . . . 2.1.8 Missing data and Infinite 2.2 Data structures . . . . . . . . . 2.2.1 Vectors . . . . . . . . . . 2.2.2 Matrices . . . . . . . . . 2.2.3 Arrays . . . . . . . . . . 2.2.4 Data frames . . . . . . . 2.2.5 Time-series objects . . . 2.2.6 Lists . . . . . . . . . . . 2.2.7 The str function . . . .

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3 Importing data 42 3.1 Text files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

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3.2 3.3 3.4

3.1.1 The scan function Excel files . . . . . . . . . Databases . . . . . . . . . The Foreign package . . .

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4 Data Manipulation 4.1 Vector subscripts . . . . . . . . . . . . . . . . . . . 4.2 Matrix subscripts . . . . . . . . . . . . . . . . . . . 4.3 Manipulating Data frames . . . . . . . . . . . . . . 4.3.1 Extracting data from data frames . . . . . . 4.3.2 Adding columns to a data frame . . . . . . . 4.3.3 Combining data frames . . . . . . . . . . . . 4.3.4 Merging data frames . . . . . . . . . . . . . 4.3.5 Aggregating data frames . . . . . . . . . . . 4.3.6 Stacking columns of data frames . . . . . . . 4.3.7 Reshaping data . . . . . . . . . . . . . . . . 4.4 Attributes . . . . . . . . . . . . . . . . . . . . . . . 4.5 Character manipulation . . . . . . . . . . . . . . . 4.5.1 The functions nchar, substring and paste 4.5.2 Finding patterns in character objects . . . . 4.5.3 Replacing characters . . . . . . . . . . . . . 4.5.4 Splitting characters . . . . . . . . . . . . . . 4.6 Creating factors from continuous data . . . . . . . . 5 Writing functions 5.1 Introduction . . . . . . . . . . . . . . . . . . 5.2 Arguments and variables . . . . . . . . . . . 5.2.1 Required and optional arguments . . 5.2.2 The ‘...’ argument . . . . . . . . . 5.2.3 Local variables . . . . . . . . . . . . 5.2.4 Returning an object . . . . . . . . . 5.2.5 The Scoping rules . . . . . . . . . . . 5.2.6 Lazy evaluation . . . . . . . . . . . . 5.3 Control flow . . . . . . . . . . . . . . . . . . 5.3.1 Tests with if and switch . . . . . . 5.3.2 Looping with for, while and repeat 5.4 Debugging your R functions . . . . . . . . . 5.4.1 The traceback function . . . . . . . 5.4.2 The warning and stop functions . . 5.4.3 Stepping through a function . . . . . 5.4.4 The browser function . . . . . . . .

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47 47 51 54 54 57 57 59 60 61 61 62 63 64 65 67 68 68

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70 70 72 72 73 73 74 75 76 77 77 79 80 80 81 82 83

6 Efficient calculations 84 6.1 Vectorized computations . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

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6.2

6.3

6.4

The apply and outer functions . . . . . . . . 6.2.1 the apply function . . . . . . . . . . 6.2.2 the lapply and sapply functions . . 6.2.3 The tapply function . . . . . . . . . 6.2.4 The by function . . . . . . . . . . . . 6.2.5 The outer function . . . . . . . . . . Using Compiled code . . . . . . . . . . . . . 6.3.1 The .C and .Fortran interfaces . . . 6.3.2 The .Call and .External interfaces Some Compiled Code examples . . . . . . . 6.4.1 The arsim example . . . . . . . . . . 6.4.2 Using #include . . . . . . . . 6.4.3 Evaluating R expressions in C . . . .

7 Graphics 7.1 Introduction . . . . . . . . . . . . . . . 7.2 More plot functions . . . . . . . . . . . 7.2.1 The plot function . . . . . . . 7.2.2 Distribution plots . . . . . . . . 7.2.3 Two or more variables . . . . . 7.2.4 Graphical Devices . . . . . . . . 7.3 Modifying a graph . . . . . . . . . . . 7.3.1 Graphical parameters . . . . . . 7.3.2 Some handy low-level functions 7.3.3 Controlling the axes . . . . . . 7.4 Trellis Graphics . . . . . . . . . . . . . 7.4.1 Introduction . . . . . . . . . . . 7.4.2 Multi panel graphs . . . . . . . 7.4.3 Trellis panel functions . . . . . 7.4.4 Conditioning plots . . . . . . . 7.5 The ggplot2 package . . . . . . . . . . 7.5.1 The qplot function . . . . . . . 7.5.2 Facetting . . . . . . . . . . . . 7.5.3 Plots with several layers . . . .

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8 Statistics 8.1 Basic statistical functions . . . . . . . . . . . . . . . 8.1.1 Statistical summaries and tests . . . . . . . . 8.1.2 Probability distributions and random numbers 8.2 Regression models . . . . . . . . . . . . . . . . . . . . 8.2.1 Formula objects . . . . . . . . . . . . . . . . . 8.3 Linear regression models . . . . . . . . . . . . . . . . 8.3.1 Formula objects . . . . . . . . . . . . . . . . . 8.3.2 Modeling functions . . . . . . . . . . . . . . .

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86 86 87 89 90 91 92 93 94 94 94 96 98

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103 103 104 105 106 109 111 112 112 119 121 123 123 125 128 130 131 131 133 134

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135 135 135 139 141 141 142 142 144

8.4

8.5

8.6

8.7

8.3.3 Multicollinearity . . . . . . . . . . . . . . . . . . . 8.3.4 Factor (categorical) variables as regression variables Logistic regression . . . . . . . . . . . . . . . . . . . . . . 8.4.1 The modeling function glm . . . . . . . . . . . . . . 8.4.2 Performance measures . . . . . . . . . . . . . . . . 8.4.3 Predictive ability of a logistic regression . . . . . . Tree models . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 An example of a tree model . . . . . . . . . . . . . 8.5.2 Coarse classification and binning . . . . . . . . . . Survival analysis . . . . . . . . . . . . . . . . . . . . . . . 8.6.1 The Cox proportional hazards model . . . . . . . . 8.6.2 Parametric models for survival analysis . . . . . . . Non linear regression . . . . . . . . . . . . . . . . . . . . . 8.7.1 Ill-conditioned models . . . . . . . . . . . . . . . . 8.7.2 Singular value decomposition . . . . . . . . . . . .

9 Miscellaneous Stuff 9.1 Object Oriented Programming . . . . . . . 9.1.1 Introduction . . . . . . . . . . . . . 9.1.2 Old style classes . . . . . . . . . . . 9.1.3 New Style classes . . . . . . . . . . 9.2 R Language objects . . . . . . . . . . . . . 9.2.1 Calls and Expressions . . . . . . . 9.2.2 Expressions as Lists . . . . . . . . . 9.2.3 Functions as lists . . . . . . . . . . 9.3 Calling R from SAS . . . . . . . . . . . . . 9.3.1 The call system and X functions . 9.3.2 Using SAS data sets and SAS ODS 9.4 Defaults and preferences in R, Starting R, 9.4.1 Defaults and preferences . . . . . . 9.4.2 Starting R . . . . . . . . . . . . . . 9.5 Creating an R package . . . . . . . . . . . 9.5.1 A ‘private’ package . . . . . . . . . 9.5.2 A ‘real’ R package . . . . . . . . . 9.6 Calling R from Java . . . . . . . . . . . . 9.7 Creating fancy output and reports . . . . . 9.7.1 A simple LATEX-table . . . . . . . . 9.7.2 An simple HTML report . . . . . .

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149 152 154 155 157 158 160 161 162 164 165 169 170 174 177

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180 180 180 180 184 189 189 190 192 193 193 194 196 196 197 198 198 199 202 204 205 206

Bibliography

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Index

208

4

List of Figures 1.1 1.2 1.3

The R system on Windows . . . . . . . . . . . . . . . . . . . . . . . . . . R integrated in the Eclipse development environment . . . . . . . . . . . The Tinn-R and an the R Console environment . . . . . . . . . . . . . .

9 17 18

6.1 6.2

A surface plot created with the function persp . . . . . . . . . . . . . . . Calculation times of arsimR (solid line) and arsimC (dashed line) for increasing vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

92 97

7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10 7.11 7.12 7.13 7.14 7.15 7.16 7.17 7.18

A scatterplot with a title . . . . . . . . . . . . . . . . . . . . . . . . . . . Line plot with title, can be created with type="l" or the curve function. Different uses of the function plot . . . . . . . . . . . . . . . . . . . . . Example distribution plot in R . . . . . . . . . . . . . . . . . . . . . . . Example barplot where the first argument is a matrix . . . . . . . . . . . Example graphs of multi dimensional data sets . . . . . . . . . . . . . . . The different regions of a plot . . . . . . . . . . . . . . . . . . . . . . . . The plotting area of this graph is divided with the layout function. . . . Examples of different symbols and colors in plots . . . . . . . . . . . . . The graph that results from the previous low-level plot functions. . . . . Graphs resulting from previous code examples of customizing axes. . . . Trellis plot Price versus Weight for different types . . . . . . . . . . . . . A trellis plot with two conditioning variables . . . . . . . . . . . . . . . . Histogram of mileage for different weight classes . . . . . . . . . . . . . . Trellis plot with modified panel function . . . . . . . . . . . . . . . . . . Trellis plot adding a least squares line in each panel . . . . . . . . . . . . A coplot with two conditioning variables . . . . . . . . . . . . . . . . . . A coplot with a smoothing line . . . . . . . . . . . . . . . . . . . . . . .

104 105 106 107 108 110 114 116 119 121 124 126 127 128 130 131 132 133

8.1

A histogram and a qq-plot of the model residuals to check normality of the residuals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diagnostic plots to check for linearity and for outliers. . . . . . . . . . . . Explorative plots giving a first impression of the relation between the binary y variable and x variables. . . . . . . . . . . . . . . . . . . . . . . The ROC curve to assess the quality of a logistic regression model . . . . Plot of the tree: Type is predicted based on Mileage and Price . . . . . . Binning the age variable, two intervals in this case . . . . . . . . . . . . .

8.2 8.3 8.4 8.5 8.6

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8.7

Survival curve: 10% will develop AIDS before 45 76 months. . . . . . . . . . . . . . . . . . . . . . 8.8 Scatter plot of the martingale residuals . . . . . 8.9 Three subjects with age 10, 30 and 60 . . . . . 8.10 Scatter plot of our simulated data for nls . . . 8.11 Simulated data and nls predictions . . . . . . . 8.12 Hill curves for two sets of parameters . . . . . . 9.1 9.2 9.3

months and 20% before . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

166 168 169 172 175 176

Result of the specific plot method for class bigMatrix. . . . . . . . . . . . 185 Some Lissajous plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 A small java gui that can call R functions. . . . . . . . . . . . . . . . . . 204

6

1 Introduction 1.1 What is R? While the commercial implementation of S, S-PLUS, is struggling to keep its existing users, the open source version of S, R, has received a lot of attention in the last five years. Not only because the R system is a free tool, the system has proven to be a very effective tool in data manipulation, data analysis, graphing and developing new functionality. The user community has grown enormously the last years, and it is an active user community writing new R packages that are made available to others. If you have any questions or comments on this document please do not hesitate to contact me. The best explanation of R is given on the R web site http://www.r-project.org. The remainder of this section and the following section are taken from the R web site. R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and non linear modeling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

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CHAPTER 1. INTRODUCTION

1.2. THE R ENVIRONMENT

1.2 The R environment R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes • an effective data handling and storage facility, • a suite of operators for calculations on arrays, in particular matrices, • a large, coherent, integrated collection of intermediate tools for data analysis, • graphical facilities for data analysis and display either on-screen or on hardcopy, and • a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities. The term ‘environment’ is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software. R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly. Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics. R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.

1.3 Obtaining and installing R R can be downloaded from the ‘Comprehensive R Archive Network’ (CRAN). You can download the complete source code of R, but more likely as a beginning R user you want to download the precompiled binary distribution of R. Go to the R web site http://www.r-project.org and select a CRAN mirror site and download the base distribution file, under Windows: R-2.7.0-win32.exe. At the time of writing the latest version is 2.7.0. We will mention user contributed packages in the next section. The base file has a size of around 29MB, which you can execute to install R. The installation wizard will guide you through the installation process. It may be useful to

8

CHAPTER 1. INTRODUCTION

1.4. YOUR FIRST R SESSION

install the R reference manual as well, by default it is not installed. You can select it in the installation wizard.

1.4 Your first R session Start the R system, the main window (RGui) with a sub window (R Console) will appear as in figure 1.1.

Figure 1.1: The R system on Windows In the ‘Console’ window the cursor is waiting for you to type in some R commands. For example, use R as a simple calculator: > print("Hello world!") [1] "Hello world!" > 1 + sin(9) [1] 1.412118 > 234/87754 [1] 0.002666545

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CHAPTER 1. INTRODUCTION

1.4. YOUR FIRST R SESSION

> (1 + 0.05)^8 [1] 1.477455 > 23.76*log(8)/(23 + atan(9)) [1] 2.019920

Results of calculations can be stored in objects using the assignment operators: • An arrow (<-) formed by a smaller than character and a hyphen without a space! • The equal character (=). These objects can then be used in other calculations. To print the object just enter the name of the object. There are some restrictions when giving an object a name: • Object names cannot contain ‘strange’ symbols like !, +, -, #. • A dot (.) and an underscore ( ) are allowed, also a name starting with a dot. • Object names can contain a number but cannot start with a number. • R is case sensitive, X and x are two different objects, as well as temp and temP. > x = sin(9)/75 > y = log(x) + x^2 > x [1] 0.005494913 > y [1] -5.203902 > m <- matrix(c(1,2,4,1), ncol=2) > m > [,1] [,2] [1,] 1 4 [2,] 2 1 > solve(m) [,1] [,2] [1,] -0.1428571 0.5714286 [2,] 0.2857143 -0.1428571

To list the objects that you have in your current R session use the function ls or the function objects. > ls() [1] "x" "y"

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So to run the function ls we need to enter the name followed by an opening ( and and a closing ). Entering only ls will just print the object, you will see the underlying R code of the the function ls. Most functions in R accept certain arguments. For example, one of the arguments of the function ls is pattern. To list all objects starting with the letter x: > x2 = 9 > y2 = 10 > ls(pattern="x") [1] "x" "x2"

If you assign a value to an object that already exists then the contents of the object will be overwritten with the new value (without a warning!). Use the function rm to remove one or more objects from your session. > rm(x, x2)

To conclude your first session, we create two small vectors with data and a scatterplot. z2 <- c(1,2,3,4,5,6) z3 <- c(6,8,3,5,7,1) plot(z2,z3) title("My first scatterplot")

After this very short R session which barely scratched the surface, we hope you continue using the R system. The following chapters of this document will explain in detail the different data types, data structures, functions, plots and data analysis in R.

1.5 The available help 1.5.1 The on line help There is extensive on line help in the R system, the best starting point is to run the function help.start(). This will launch a local page inside your browser with links to the R manuals, R FAQ, a search engine and other links. In the R Console the function help can be used to see the help file of a specific function. help(mean)

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1.6. THE R WORKSPACE, . . .

Use the function help.search to list help files that contain a certain string. > help.search("robust") Help files with alias or concept or title matching ’robust’ using fuzzy matching: hubers(MASS) rlm(MASS) summary.rlm(MASS) line(stats) runmed(stats)

Huber Proposal 2 Robust Estimator of Location and/or Scale Robust Fitting of Linear Models Summary Method for Robust Linear Models Robust Line Fitting Running Medians -- Robust Scatter Plot Smoothing

Type ’help(FOO, package = PKG)’ to inspect entry ’FOO(PKG) TITLE’.

The R manuals are also on line available in pdf format. In the RGui window go the help menu and select ‘manuals in pdf’.

1.5.2 The R mailing lists and the R Journal There are several mailing lists on R, see the R website. The main mailing list is Rhelp, web interfaces are available where you can browse trough the postings or search for a specific key word. If you have a connection to the internet, then the function RSiteSearch in R can be used to search for a string in the archives of all the R mailing lists. RSiteSearch("MySQL")

Another very useful webpage on the internet is www.Rseek.org, a sort of R search enigine. Also take a look at the R Journal, at http://journal.r-project.org.

1.6 The R workspace, managing objects Objects that you create during an R session are hold in memory, the collection of objects that you currently have is called the workspace. This workspace is not saved on disk unless you tell R to do so. This means that your objects are lost when you close R and not save the objects, or worse when R or your system crashes on you during a session. When you close the RGui or the R console window, the system will ask if you want to save the workspace image. If you select to save the workspace image then all the objects

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in your current R session are saved in a file .RData. This is a binary file located in the working directory of R, which is by default the installation directory of R. During your R session you can also explicitly save the workspace image. Go to the ‘File’ menu and then select ‘Save Workspace...’, or use the save.image function. ## save to the current working directory save.image() ## just checking what the current working directory is getwd() ## save to a specific file and location save.image("C:\\Program Files\\R\\R-2.5.0\\bin\\.RData")

If you have saved a workspace image and you start R the next time, it will restore the workspace. So all your previously saved objects are available again. You can also explicitly load a saved workspace file, that could be the workspace image of someone else. Go the ‘File’ menu and select ‘Load workspace...’.

1.7 R Packages One of the strengths of R is that the system can easily be extended. The system allows you to write new functions and package those functions in a so called ‘R package’ (or ‘R library’). The R package may also contain other R objects, for example data sets or documentation. There is a lively R user community and many R packages have been written and made available on CRAN for other users. Just a few examples, there are packages for portfolio optimization, drawing maps, exporting objects to html, time series analysis, spatial statistics and the list goes on and on. In section 9.5.1 we’ll give a short description on writing your own package. When you download R, already a number (around 30) of packages are downloaded as well. To use a function in an R package, that package has to be attached to the system. When you start R not all of the downloaded packages are attached, only seven packages are attached to the system by default. You can use the function search to see a list of packages that are currently attached to the system, this list is also called the search path. > search() [1] ".GlobalEnv" "package:stats" [4] "package:grDevices" "package:datasets" [7] "package:methods" "Autoloads"

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The first element of the output of search is ".GlobalEnv", which is the current workspace of the user. To attach another package to the system you can use the menu or the library function. Via the menu: Select the ‘Packages’ menu and select ‘Load package...’, a list of available packages on your system will be displayed. Select one and click ‘OK’, the package is now attached to your current R session. Via the library function: > library(MASS) > shoes $A [1] 13.2 8.2 10.9 14.3 10.7

6.6

9.5 10.8

8.8 13.3

$B [1] 14.0

6.4

9.8 11.3

9.3 13.6

8.8 11.2 14.2 11.8

The function library can also be used to list all the available libraries on your system with a short description. Run the function without any arguments > library() Packages in library ’C:/PROGRA~1/R/R-25~1.0/library’: base boot class cluster codetools datasets DBI foreign graphics ... ...

The R Base Package Bootstrap R (S-Plus) Functions (Canty) Functions for Classification Cluster Analysis Extended Rousseeuw et al. Code Analysis Tools for R The R Datasets Package R Database Interface Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, ... The R Graphics Package

If you have a connection to the internet then a package on CRAN can be installed very easily. To install a new package go to the ‘Packages’ menu and select ‘Install package(s)...’. Then select a CRAN mirror near you, a (long) list with all the packages will appear where you can select one or more packages. Click ‘OK’ to install the selected packages. Note that the packages are only installed on your machine and not loaded (attached) to your current R session. As an alternative to the function search use sessionInfo to see system packages and user attached packages.

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> sessionInfo() R version 2.5.0 (2007-04-23) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252; LC_MONETARY=English_United States.1252;LC_NUMERIC=C; LC_TIME=English_United States.1252 attached base packages: [1] "stats" "graphics" [7] "utils" "methods" other attached packages: MASS svSocket svIO "7.2-33" "0.9-5" "0.9-5"

"grDevices" "datasets" "base"

R2HTML "1.58"

svMisc "0.9-5"

"tcltk"

svIDE "0.9-5"

1.8 Conflicting objects It is not recommended to do, but R allows the user to give an object a name that already exists. If you are not sure if a name already exists, just enter the name in the R console and see if R can find it. R will look for the object in all the libraries (packages) that are currently attached to the R system. R will not warn you when you use an existing name. > mean = 10 > mean [1] 10

The object mean already exists in the base package, but is now masked by your object mean. To get a list of all masked objects use the function conflicts. > conflicts() [1] "body<-" "mean"

You can safely remove the object mean with the function rm without risking deletion of the mean function. Calling rm removes only objects in your working environment by default.

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1.9 Editors for R scripts 1.9.1 The editor in RGui The console window in R is only useful when you want to enter one or two statements. It is not useful when you want to edit or write larger blocks of R code. In the RGui window you can open a new script, go to the ‘File’ menu and select ‘New Script’. An empty R editor will appear where you can enter R code. This code can be saved, it will be a normal text file, normally with a .R extension. Existing text files with R code can be opened in the RGui window. To run code in an R editor, select the code and use -R to run the selected code. You can see that the code is parsed in the console window, any results will be displayed there.

1.9.2 Other editors The built-in R editor is not the most fancy editor you can think of. It does not have much functionality. Since writing R code is just creating text files, you can do that with any text editor you like. If you have R code in a text file, you can use the source function to run the code in R. The function reads and executes all the statements in a text file. # In the console window source("C:\\Temp\\MyRfile.R")

There are free text editors that can send the R code inside the text editor to an R session. Some free editors that are worth mentioning are Eclipse (www.eclipse.org), Tinn-R (http://www.sciviews.org/Tinn-R) and JGR (speak ‘Jaguar’ http://jgr.markushelbig.org). Eclipse Eclipse is more than a text editor it is an environment to create, test manage and maintain (large) pieces of code. Built in functionality includes: • Managing different text files in a project. • Version control, recall previously saved versions of your text file. • Search in multiple files.

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The eclipse environment allows user to develop so called perspectives (or plug-ins). Such a plug-in customizes the Eclipse environment for a certain programming language. Stephan Wahlbrink has written an Eclipse plug-in for R, called ‘StatEt’. See www.walware.de/goto/statet and see [1]. This plug-in adds extra ‘R specific’ functionality: • Start an R console or terminal within Eclipse. • Color coding of key words. • Run R code in Eclipse by sending it to the R console. • Insert predefined blocks of R code (templates). • Supports writing R documentation files (*.Rd files).

Figure 1.2: R integrated in the Eclipse development environment

Tinn-R Tinn stands for Tinn is not Notepad, it is a text editor that was originally developed to replace the boring Notepad. With each new version of Tinn more features were added,

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and it has become a very nice environment to edit and maintain code. Tinn-R is the special R version of Tinn. It allows color highlighting of the R language and sending R statements to an R Console window.

Figure 1.3: The Tinn-R and an the R Console environment

JGR JGR (Java GUI for R) is a universal and unified Graphical User Interface for R. It includes among others: an integrated editor, help system ‘Type-on’ spreadsheet and an object browser.

18

2 Data Objects In this section we will discuss the different aspects of data types and structures in R. Operators such as c and : will be used in this section as an illustration and will be discussed in the next section. If you are confronted with an unknown function, you can ask for help by typing in the command: help(function name)

A help text will appear and describe the purpose of the function and how to use it.

2.1 Data types 2.1.1 Double If you do calculations on numbers, you can use the data type double to represent the numbers. Doubles are numbers like 3.1415, 8.0 and 8.1. Doubles are used to represent continuous variables like the weight or length of a person. x <- 8.14 y <- 8.0 z <- 87.0 + 12.9

Use the function is.double to check if an object is of type double. Alternatively, use the function typeof to ask R the type of the object x. typeof(x) [1] "double" is.double(8.9) [1] TRUE test <- 1223.456 is.double(test) [1] TRUE

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Keep in mind that doubles are just approximations to real numbers. Mathematically there are infinity many numbers, the computer can √ ofcourse only represent a finite number of numbers. Not only can numbers like π or 2 not be represented exactly, less exotic numbers like 0.1 for example can also not be represented exactly. One of the conseqeunces of this is that when you compare two doubles with each other you should take some care. Consider the following (surprising) result. 0.3 == 0.1 + 0.1 + 0.1 [1] FALSE

2.1.2 Integer Integers are natural numbers. They can be used to represent counting variables, for example the number of children in a household. nchild <- as.integer(3) is.integer(nchild) [1] TRUE

Note that 3.0 is not an integer, nor is 3 by default an integer! nchild <- 3.0 is.integer(nchild) [1] FALSE nchild <- 3 is.integer(nchild) [1] FALSE

So a 3 of type ‘integer’ in R is something different than a 3.0 of type ‘double’. However, you can mix objects of type ‘double’ and ‘integer’ in one calculation without any problems. x <- as.integer(7) y <- 2.0 z <- x/y

In contrast to some other programming languages, the answer is of type double and is 3.5. The maximum integer in R is 231 − 1.

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as.integer(2^31 - 1) [1] 2147483647 as.integer(2^31) [1] NA Warning message: NAs introduced by coercion

2.1.3 Complex Objects of type ‘complex’ are used to represent complex numbers. In statistical data analysis you will not need them often. Use the function as.complex or complex to create objects of type complex. test1 <- as.complex(-25+5i) sqrt(test1) [1] 0.4975427+5.024694i test2 <- complex(5,real=2,im=6) test2 [1] 2+6i 2+6i 2+6i 2+6i 2+6i typeof(test2) [1] "complex"

Note that by default calculations are done on real numbers, so sqrt(-1) results in NA. Use sqrt(as.complex(-1))

2.1.4 Logical An object of data type logical can have the value TRUE or FALSE and is used to indicate if a condition is true or false. Such objects are usually the result of logical expressions. x <- 9 y <- x > 10 y [1] FALSE

The result of the function is.double is an object of type logical (TRUE or FALSE).

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is.double(9.876) [1] TRUE

Logical expressions are often built from logical operators: < <= > >= == !=

smaller than smaller than or equal to larger than larger than or equal to is equal to is unequal to

The logical operators and, or and not are given by &, — and !, respectively. x <- c(9,166) y <- (3 < x) & (x <= 10) [1] TRUE FALSE

Calculations can also be carried out on logical objects, in which case the FALSE is replaced by a zero and a one replaces the TRUE. For example, the sum function can be used to count the number of TRUE’s in a vector or array. x <- 1:15 ## number of elements in x larger than 9 sum(x>9) [1] 6

2.1.5 Character A character object is represented by a collection of characters between double quotes ("). For example: "x", "test character" and "iuiu8ygy-iuhu". One way to create character objects is as follows. x <- c("a","b","c") x [1] "a" "b" "c" mychar1 <- "This is a test" mychar2 <- "This is another test" charvector <- c("a", "b", "c", "test")

The double quotes indicate that we are dealing with an object of type ‘character’.

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2.1.6 Factor The factor data type is used to represent categorical data (i.e. data of which the value range is a collection of codes). For example: • variable ‘sex’ with values male and female. • variable ‘blood type’ with values: A, AB and O. An individual code of the value range is also called a level of the factor variable. So the variable ‘sex’ is a factor variable with two levels, male and female. Sometimes people confuse factor type with character type. Characters are often used for labels in graphs, column names or row names. Factors must be used when you want to represent a discrete variable in a data frame and want to analyze it. Factor objects can be created from character objects or from numeric objects, using the function factor. For example, to create a vector of length five of type factor do the following: sex <- c("male","male","female","male","female")

The object sex is a character object. You need to transform it to factor. sex <- factor(sex) sex [1] male male female

male

female

Use the function levels to see the different levels a factor variable has. levels(sex) [1] "female" "male"

Note that the result of the levels function is of type character. Another way to generate the sex variable is as follows: sex <- c(1,1,2,1,2)

The object ‘sex’ is an integer variable, you need to transform it to a factor. sex <- factor(sex) sex [1] 1 1 2 1 2 Levels: 1 2

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The object ‘sex’ looks like, but is not an integer variable. The 1 represents level ”1” here. So arithmetic operations on the sex variable are not possible: sex + 7 [1] NA NA NA NA NA Warning message: + not meaningful for factors in: Ops.factor(sex, 7)

It is better to rename the levels, so level ”1” becomes male and level ”2” becomes female: levels(sex) <- c("male","female") sex [1] male male female male female

You can transform factor variables to double or integer variables using the as.double or as.integer function. sex.numeric <- as.double(sex) sex.numeric [1] 2 2 1 2 1

The 1 is assigned to the female level, only because alphabetically female comes first. If the order of the levels is of importance, you will need to use ordered factors. Use the function ordered and specify the order with the levels argument. For example: Income <- c("High","Low","Average","Low","Average","High","Low") Income <- ordered(Income, levels=c("Low","Average","High")) Income [1] High Low Average Low Average High Low Levels: Low < Average < High

The last line indicates the ordering of the levels within the factor variable. When you transform an ordered factor variable, the order is used to assign numbers to the levels. Income.numeric <- as.double(Income) Income.numeric [1] 3 1 2 1 2 3 1

The order of the levels is also used in linear models. If one or more of the regression variables are factor variables, the order of the levels is important for the interpretation of the parameter estimates see section 8.3.4.

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2.1.7 Dates and Times To represent a calendar date in R use the function as.Date to create an object of class Date. temp <- c("12-09-1973", "29-08-1974") z <- as.Date(temp, "%d-%m-%Y") z [1] "1973-09-12" "1974-08-29" data.class(z) [1] "Date" format(z, "%d-%m-%Y") [1] "12-09-1973" "29-08-1974"

You can add a number to a date object, the number is interpreted as the number of day to add to the date. z + 19 [1] "1973-10-01" "1974-09-17"

You can subtract one date from another, the result is an object of class ‘difftime’ dz = z[2] -z[1] dz data.class(dz) Time difference of 351 days [1] "difftime"

In R the classes POSIXct and POSIXlt can be used to represent calendar dates and times. You can create POSIXct objects with the function as.POSIXct. The function accepts characters as input, and it can be used to not only to specify a date but also a time within a date. t1 <- as.POSIXct("2003-01-23") t2 <- as.POSIXct("2003-04-23 15:34") t1 t2 [1] "2003-01-23 W. Europe Standard Time" [1] "2003-04-23 15:34:00 W. Europe Daylight Time"

A handy function is strptime, it is used to convert a certain character representation of a date (and time) into another character representation. You need to provide a conversion specification that starts with a % followed by a single letter.

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# first creating four characters x <- c("1jan1960", "2jan1960", "31mar1960", "30jul1960") z <- strptime(x, "%d%b%Y") zt <- as.POSIXct(z) zt [1] "1960-01-01 W. Europe Standard Time" [2] "1960-01-02 W. Europe Standard Time" [3] "1960-03-31 W. Europe Daylight Time" [4] "1960-07-30 W. Europe Daylight Time" # pasting 4 character dates and 4 character times together dates <- c("02/27/92", "02/27/92", "01/14/92", "02/28/92") times <- c("23:03:20", "22:29:56", "01:03:30", "18:21:03") x <- paste(dates, times) z <- strptime(x, "%m/%d/%y %H:%M:%S") zt <- as.POSIXct(z) zt [1] "1992-02-27 23:03:20 W. Europe Standard Time" [2] "1992-02-27 22:29:56 W. Europe Standard Time" [3] "1992-01-14 01:03:30 W. Europe Standard Time" [4] "1992-02-28 18:21:03 W. Europe Standard Time"

An object of type POSIXct can be used in certain calculations, a number can be added to a POSIXct object. This number will be the interpreted as the number of seconds to add to the POSIXct object. zt + 13 [1] "1992-02-27 [2] "1992-02-27 [3] "1992-01-14 [4] "1992-02-28

23:03:33 22:30:09 01:03:43 18:21:16

W. W. W. W.

Europe Europe Europe Europe

Standard Standard Standard Standard

Time" Time" Time" Time"

You can subtract two POSIXct objects, the result is a so called ‘difftime’ object. t2 <- as.POSIXct("2004-01-23 14:33") t1 <- as.POSIXct("2003-04-23") d <- t2-t1 d Time difference of 275.6479 days

A ‘difftime’ object can also be created using the function as.difftime, and you can add a difftime object to a POSIXct object. Due to a bug in R this can only safely be done with the function "+.POSIXt".

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CHAPTER 2. DATA OBJECTS "+.POSIXt"(zt, d) [1] "1992-11-29 14:36:20 [2] "1992-11-29 14:02:56 [3] "1992-10-15 17:36:30 [4] "1992-11-30 09:54:03

W. W. W. W.

Europe Europe Europe Europe

2.1. DATA TYPES

Standard Standard Daylight Standard

Time" Time" Time" Time"

To extract the weekday, month or quarter from a POSIXct object use the handy R functions weekdays, months and quarters. Another handy function is Sys.time, which returns the current date and time. weekdays(zt) [1] "Thursday" "Thursday" "Tuesday"

"Friday"

There are some R packages that can handle dates and time objects. For example, the packages zoo, chron, tseries, its and Rmetrics. Especially Rmetrics has a set of powerful functions to maintain and manipulate dates and times. See [2].

2.1.8 Missing data and Infinite values We have already seen the symbol NA. In R it is used to represent ‘missing’ data (Not Available). It is not really a separate data type, it could be a missing double or a missing integer. To check if data is missing, use the function is.na or use a direct comparison with the symbol NA. There is also the symbol NaN (Not a Number ), which can be detected with the function is.nan. x <- as.double( c("1", "2", "qaz")) is.na(x) [1] FALSE FALSE TRUE z <- sqrt(c(1,-1)) Warning message: NaNs produced in: sqrt(c(1, -1)) is.nan(z) [1] FALSE TRUE

Infinite values are represented by Inf or -Inf. You can check if a value is infinite with the function is.infinite. Use is.finite to check if a value is finite. x <- c(1,3,4) y <- c(1,0,4) x/y [1] 1 Inf 1

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z <- log(c(4,0,8)) is.infinite(z) [1] FALSE TRUE FALSE

In R NULL represents the null object. NULL is used mainly to represent the lists with zero length, and is often returned by expressions and functions whose value is undefined.

2.2 Data structures Before you can perform statistical analysis in R, your data has to be structured in some coherent way. To store your data R has the following structures: • vector • matrix • array • data frame • time-series • list

2.2.1 Vectors The simplest structure in R is the vector. A vector is an object that consists of a number of elements of the same type, all doubles or all logical. A vector with the name ‘x’ consisting of four elements of type ‘double’ (10, 5, 3, 6) can be constructed using the function c. x <- c(10, 5, 3, 6) x [1] 10 5 3 6

The function c merges an arbitrary number of vectors to one vector. A single number is regarded as a vector of length one. y <- c(x,0.55, x, x) y [1] 10.0 5.0 3.0 6.0 0.55 10.0 5.0 3.0 6.0 [10] 10.0 5.0 3.0 6.0

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Typing the name of an object in the commands window results in printing the object. The numbers between square brackets indicate the position of the following element in the vector. Use the function round to round the numbers in a vector. round (y,3)

# round to 3 decimals

Mathematical calculations on vectors Calculations on (numerical) vectors are usually performed on each element. For example, x*x results in a vector which contains the squared elements of x. x [1] 10 5 3 6 z <- x*x z [1] 100 25 9 36

The symbols for elementary arithmetic operations are +, -, *, /. Use the ^ symbol to raise power. Most of the standard mathematical functions are available in R. These functions also work on each element of a vector. For example the logarithm of x: log(x) [1] 2.302585 1.609438 1.098612 1.791759

Function name abs asin acos atan asinh acosh atanh exp log floor ceiling trunc gamma lgamma log10 round sin cos tan sinh cosh tanh sqrt

Operation absolute value inverse geometric functions inverse hyperbolic functions exponent and natural logarithm creates integers from floating point numbers gamma and log gamma function logarithm with basis 10 rounding geometric functions hyperbolic functions square root

Table 2.1: Some mathematical functions that can be applied on vectors

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The recycling rule It is not necessary to have vectors of the same length in an expression. If two vectors in an expression are not of the same length then the shorter one will be repeated until it has the same length as the longer one. A simple example is a vector and a number (which is a vector of length one). sqrt(x) + 2 [1] 5.162278 4.236068 3.732051 4.449490

In the above example the 2 is repeated 4 times until it has the same length as x and then the addition of the two vectors is carried out. In the next example, x has to be repeated 1.5 times in order to have the same length as y. This means the first two elements of x are added to x and then x*y is calculated. x <- c(1,2,3,4) y <- c(1,2,3,4,5,6) z <- x*y Warning message: longer object length is not a multiple of shorter object length in: x * y > z [1] 1 4 9 16 5 12

Generating vectors Regular sequences of numbers can be very handy for all sorts of reasons. Such sequences can be generated in different ways. The easiest way is to use the column operator (:). index <- 1:20 index [1] 1 2 3 4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20

A descending sequence is obtained by 20:1. The function seq together with its arguments from, to, by or length is used to generate more general sequences. Specify the beginning and end of the sequence and either specify the length of the sequence or the increment. u <- seq(from=-3,to=3,by =0.5) u [1] -3.0 -2.5 -2.0 -1.5 -1.0 -0.5

0.0

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0.5

1.0

1.5

2.0

2.5

3.0

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2.2. DATA STRUCTURES

The following commands have the same result: u <- seq(-3,3,length=13) u <- (-6):6/2

The function seq can also be used to generate vectors with POSIXct elements (a sequence of dates). The following examples speak for them selves. seq(as.POSIXct("2003-04-23"), by = "month", length = 12) [1] "2003-04-23 W. Europe Daylight Time" "2003-05-23 W. Europe Daylight Time" [3] "2003-06-23 W. Europe Daylight Time" "2003-07-23 W. Europe Daylight Time" ... seq(ISOdate(1910,1,1), ISOdate(1999,1,1), "years") [1] "1910-01-01 12:00:00 GMT" "1911-01-01 12:00:00 GMT" [3] "1912-01-01 12:00:00 GMT" "1913-01-01 12:00:00 GMT" ...

The function rep repeats a given vector. The first argument is the vector and the second argument can be a number that indicates how often the vector needs to be repeated. rep(1:4, 4) [1] 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

The second argument can also be a vector of the same length as the vector used for the first argument. In this case each element in the second vector indicates how often the corresponding element in the first vector is repeated. rep(1:4, c(2,2,2,2)) [1] 1 1 2 2 3 3 4 4 rep(1:4, 1:4) [1] 1 2 2 3 3 3 4 4 4 4

For information about other options of the function rep type help(rep). To generate vectors with random elements you can use the functions rnorm or runif. There are more of these functions. x <- rnorm(10) # 10 random standard normal numbers y <- runif(10,4,7) # 10 random numbers between 4 and 7

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2.2.2 Matrices Generating matrices A matrix can be regarded as a generalization of a vector. As with vectors, all the elements of a matrix must be of the same data type. A matrix can be generated in several ways. For example: • Use the function dim: x <- 1:8 dim(x) <- c(2,4) x [,1] [,2] [,3] [,4] [1,] 1 3 5 7 [2,] 2 4 6 8

• Use the function matrix: x <- matrix(1:8,2,4,byrow=F) x [,1] [,2] [,3] [,4] [1,] 1 3 5 7 [2,] 2 4 6 8

By default the matrix is filled by column. To fill the matrix by row specify byrow = T as argument in the matrix function. 1. Use the function cbind to create a matrix by binding two or more vectors as column vectors. The function rbind is used to create a matrix by binding two or more vectors as row vectors. cbind(c(1,2,3),c(4,5,6)) [,1] [,2] [1,] 1 4 [2,] 2 5 [3,] 3 6 rbind(c(1,2,3),c(4,5,6)) [,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6

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Calculations on matrices A matrix can be regarded as a number of equal length vectors pasted together. All the mathematical functions that apply to vectors also apply to matrices and are applied on each matrix element. x*x^2

[1,] [2,]

# All operations are applied on each matrix element [,1] [,2] [,3] [,4] 1 27 125 343 8 64 216 512

max(x) [1] 8

# returns the maximum of all matrix elements in x

You can multiply a matrix with a vector. The outcome may be surprising: x <- matrix(1:16,ncol=4) y <- 7:10 x*y

[1,] [2,] [3,] [4,]

[,1] [,2] [,3] [,4] 7 35 63 91 16 48 80 112 27 63 99 135 40 80 120 160

x <- matrix(1:28,ncol=4) y <- 7:10 x*y [,1] [,2] [,3] [,4] [1,] 7 80 135 176 [2,] 16 63 160 207 [3,] 27 80 119 240 [4,] 40 99 144 175 [5,] 35 120 171 208 [6,] 48 91 200 243 [7,] 63 112 147 280

As an exercise: try to find out what R did. To perform a matrix multiplication in the mathematical sense, use the operator: %*%. The dimensions of the two matrices must agree. In the following example the dimensions are wrong:

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x <- matrix(1:8,ncol=2) x %*% x Error in x %*% x : non-conformable arguments

A matrix multiplied with its transposed t(x) always works. x %*% t(x) [,1] [,2] [,3] [,4] [1,] 26 32 38 [2,] 32 40 48 [3,] 38 48 58 [4,] 44 56 68

44 56 68 80

R has a number of matrix specific operations, for example: Function name chol(x) col(x) diag(x) ncol(x) nrow(x) qr(x) row(x) solve(A,b) solve(x) svd(x) var(x)

Operation Choleski decomposition matrix with column numbers of the elements create a diagonal matrix from a vector returns the number of columns of a matrix returns the number of rows of a matrix QR matrix decomposition matrix with row numbers of the elements solve the system Ax=b calculate the inverse singular value decomposition covariance matrix of the columns

Table 2.2: Some functions that can be applied on matrices A detailed description of these functions can be found in the corresponding help files, which can be accessed by typing for example ?diag in the R Console.

2.2.3 Arrays Arrays are generalizations of vectors and matrices. A vector is a one-dimensional array and a matrix is a two dimensional array. As with vectors and matrices, all the elements of an array must be of the same data type. An example of an array is the threedimensional array ‘iris3’, which is a built-in data object in R. A three dimensional array can be regarded as a block of numbers. dim(iris3) # dimensions of iris [1] 50 4 3

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All basic arithmetic operations which apply to matrices are also applicable to arrays and are performed on each element. test <- iris + 2*iris

The function array is used to create an array object newarray <- array(c(1:8, 11:18, 111:118), dim = c(2,4,3)) newarray , , 1 [,1] [,2] [,3] [,4] [1,] 1 3 5 7 [2,] 2 4 6 8 , , 2 [,1] [,2] [,3] [,4] [1,] 11 13 15 17 [2,] 12 14 16 18 , , 3 [,1] [,2] [,3] [,4] [1,] 111 113 115 117 [2,] 112 114 116 118

2.2.4 Data frames Data frames can also be regarded as an extension to matrices. Data frames can have columns of different data types and are the most convenient data structure for data analysis in R. In fact, most statistical modeling routines in R require a data frame as input. One of the built-in data frames in R is ‘mtcars’. mtcars # only a small part of mtcars

Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout

mpg cyl disp hp drat wt qsec vs am gear carb 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2

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The data frame contains information on different cars. Usually each row corresponds with a case and each column represents a variable. In this example the ‘carb’ column is of data type ‘double’ and represents the number of carburetors. See the help file for more information on this data frame; ?mtcars. Data frame attributes A data frame can have the attributes names and row.names. The attribute names contains the column names of the data frame and the attribute row.names contains the row names of the data frame. The attributes of a data frame can be retrieved separately from the data frame with the functions names and row.names. The result is a character vector containing the names. rownames(mtcars)[1:5] # only the first five row names [1] "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" [4] "Hornet 4 Drive" "Hornet Sportabout" names(mtcars) [1] "mpg" "cyl" [11] "carb"

"disp" "hp"

"drat" "wt"

"qsec" "vs"

"am"

"gear"

Creating data frames You can create data frames in several ways, by importing a data file as in Chapter 3, for example, or by using the function data.frame. This function can be used to create new data frames or convert other objects into data frames. A few examples of the data.frame function: my.logical <- sample(c(T,F),size=20,replace = T) my.numeric <- rnorm(20) my.df <- data.frame(my.logical,my.numeric) my.df

1 2 3 .. .. 19 20

my.logical TRUE TRUE TRUE .. .. TRUE TRUE

my.numeric 0.63892503 -1.14575124 -1.27484164 .. .. -0.01115154 -1.07818944

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test <- matrix(rnorm(21),7,3) # create a matrix with random elements test <- data.frame(test) test X1 X2 X3 1 -0.36428978 0.63182432 0.6977597 2 -0.24943864 -1.05139082 -0.9063837 3 0.95472560 -0.46806163 1.0057703 4 0.48152529 -2.03857066 -0.7163017 5 -0.71593428 -2.18493234 -2.7043682 6 -1.20729385 -0.50772018 1.1240321 7 -0.07551876 0.06711515 0.1897599 names(test) [1] "X1" "X2" "X3"

R automatically creates column names: ‘X1’, ‘X2’ and ‘X3’. You can use the names function to change these column names. names(test) <- c("Price", "Length", "Income") row.names(test) <- c("Paul","Ian","Richard","David","Rob","Andrea","John") test Price Length Income Paul -0.36428978 0.63182432 0.6977597 Ian -0.24943864 -1.05139082 -0.9063837 Richard 0.95472560 -0.46806163 1.0057703 David 0.48152529 -2.03857066 -0.7163017 Rob -0.71593428 -2.18493234 -2.7043682 Andrea -1.20729385 -0.50772018 1.1240321 John -0.07523445 0.32454334 1.3432442

2.2.5 Time-series objects In R a time-series object (an object of class ‘ts’) is created with the function ts. It combines two components: • The data, a vector or matrix of numeric values. In case of a matrix, each column is a separate time-series. • The dates of the data, the dates are equispaced points in time. # starting from jan-87, 100 monthly intervals myts1 <- ts(data = rnorm(100), start=c(1987), freq = 12) # two time-series starting from apr-1987, 50 monthly intervals

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myts2 <- ts(data = matrix(rnorm(100),ncol=2), start=c(1987,4), freq=12) myts2 Series 1 Series 2 Apr 1987 1.66394678 1.3009008 May 1987 -0.48923748 -0.8199132 Jun 1987 0.21643666 -0.1581245 Jul 1987 -2.21148119 -0.4926389 Aug 1987 0.26117051 1.1255435 ...

The function tsp returns the start and end time, and also the frequency without printing the complete data of the time-series. tsp(myts2) [1] 1987.250 1991.333

12.000

2.2.6 Lists A list is like a vector. However, an element of a list can be an object of any type and structure. Consequently, a list can contain another list and therefore it can be used to construct arbitrary data structures. Lists are often used for output of statistical routines in R. The output object is often a collection of parameter estimates, residuals, predicted values etc. For example, consider the output of the function lsfit. In its most simple form the function fits a least square regression. x <- 1:5 y <- x + rnorm(5,0,0.25) z <- lsfit(x,y) z $coef: Intercept X 0.1391512 0.9235291 $residuals: [1] -0.006962623 -0.017924751 -0.036747141 $intercept: [1] T

38

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In this example the output value of lsfit(x,y) is assigned to object ‘z’. This is a list whose first component is a vector with the intercept and the slope. The second component is a vector with the model residuals and the third component is a logical vector of length one indicating whether or not an intercept is used. The three components have the names ‘coef’, ‘residuals’ and ‘intercept’. The components of a list can be extracted in several ways: • component number: z[[1]] means the first component of z (use double square brackets!). • component name: z$name indicates the component of z with name name. To identify the component name the first few characters will do, for example, you can use z$r instead of z$residuals. test <- z$r test [1] -0.0069626 -0.0179247 -0.0367471 0.1551190 -0.0934845 z$r[4] # fourth element of the residuals [1] 0.155119026

Creating lists A list can also be constructed by using the function list. The names of the list components and the contents of list components can be specified as arguments of the list function by using the = character. x1 <- 1:5 x2 <- c(T,T,F,F,T) y <- list(numbers=x1, wrong=x2) y $numbers [1] 1 2 3 4 5 $wrong [1] TRUE

TRUE FALSE FALSE

TRUE

So the left-hand side of the = operator in the list function is the name of the component and the right-hand side is an R object. The order of the arguments in the list function determines the order in the list that is created. In the above example the logical object ‘wrong’ is the second component of y.

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CHAPTER 2. DATA OBJECTS y[[2]] [1] TRUE

TRUE FALSE FALSE

2.2. DATA STRUCTURES

TRUE

The function names can be used to extract the names of the list components. It is also used to change the names of list components. names(y) [1] "numbers" "wrong" names(y) <- c("lots", "valid") names(y) [1] "lots" "valid"

To add extra components to a list proceed as follows: y[[3]] <- 1:50 y$test <- "hello" y $lots [1] 1 2 3 4 5 $valid [1] TRUE

TRUE FALSE FALSE

TRUE

[[3]] [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 $test: [1] "hello"

Note the difference in single square brackets and double square brackets. y[1] $numbers: [1] 1 2 3 4 5 y[[1]] [1] 1 2 3 4 5

When single square brackets are used, the component is returned as list, whereas double square brackets return the component itself.

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Transforming objects to a list Many objects can be transformed to a list with the function as.list. For example, vectors, matrices and data frames. as.list(1:6) [[1]] [1] 1 [[2]] [1] 2 [[3]] [1] 3 ... ...

2.2.7 The str function A handy function is the str function, it displays the internal structure of an R object. The function can be used to see a short summary of an object. x1 <- rnorm(1000) x2 <- matrix(rnorm(80000),ncol=80) myl1 <- list(x1,x2,my.df) str(x1) num [1:1000] 2.326 1.889 1.740 -1.008 0.916 ... str(x2) num [1:1000, 1:80] -0.0368 -0.2626 0.8323 -0.3204 0.2559 ... str(my.df) ’data.frame’: 20 obs. of 2 variables: $ my.logical: logi TRUE TRUE TRUE TRUE TRUE FALSE ... $ my.numeric: num 0.0079 -0.0480 0.4988 0.2047 -0.6340 ... str(myl1) List of 3 $ : num [1:1000] 2.326 1.889 1.740 -1.008 0.916 ... $ : num [1:1000, 1:80] -0.0368 -0.2626 0.8323 -0.3204 0.2559 ... $ :’data.frame’: 20 obs. of 2 variables: ..$ my.logical: logi [1:20] TRUE TRUE TRUE TRUE TRUE FALSE ... ..$ my.numeric: num [1:20] 0.0079 -0.0480 0.4988 0.2047 -0.6340 ...

41

3 Importing data One of the first things you want to do in a statistical data analysis system is to import data. R provides a few methods to import data, we will discuss them in this chapter.

3.1 Text files In R you can import text files with the function read.table. This function is a has many arguments. Arguments to specify the header, the column separator, the number of lines to skip, the data types of the columns, etc. The functions read.csv and read.delim are functions to read ‘comma separated values’ files and tab delimited files. These functions call read.table with specific arguments. Suppose we have a text file data.txt, that contains the following text: Author: John Davis Date: 18-05-2007 Some comments.. Col1, Col2, Col3, Col4 23, 45, A, John 34, 41, B, Jimmy 12, 99, B, Patrick

The data without the first few lines of text can be imported to an R data frame using the following R syntax: myfile <- "C:\\Temp\\R\\Data.txt" mydf <- read.table(myfile, skip=3, sep=",", header=TRUE) mydf Col1 Col2 Col3 Col4 1 23 45 A John 2 34 41 B Jimmy 3 12 99 B Patrick

By default R converts character data in text files to factor type. In the above example the third and fourth columns are of type factor. To leave character data as character data type in R, use the stringsAsFactors argument.

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mydf <- read.table( myfile, skip=3, sep=",", header=TRUE, stringsAsFactors=FALSE )

To specify that certain columns are character and other columns are not you must use the colClasses argument and provide the type for each column. mydf <- read.table( myfile, skip=3, sep=",", header=TRUE, stringsAsFactors=FALSE, colClasses = c("numeric", "numeric", "factor", "character") )

There is an advantage in using colClasses, especially when the data set is large. If you don’t use colClasses then during a data import, R will store the data as character vectors before deciding what to do with them. Character strings in a text files may be quoted and may contain the the separator symbol. To import such text files use the quote argument. Suppose we have the following comma separated text file that we want to read. Col1, Col2, Col3 12, 45, ’Davis, Joe’ 23, 78, ’White, Jimmy’

Use the read.csv function as follows to import the above text. read.csv(myfile, quote="’") Col1 Col2 Col3 1 12 45 Davis, Joe 2 23 78 White, Jimmy

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3.1.1 The scan function The read.table function uses the more low level function scan. This function may also be called directly by the user, and can sometimes be handy when read.table cannot do the job. It reads the data into a vector or list, the user can then manipulate this vector or list. For example, if we use scan to read the text file above we get: scan(myfile, what="character", sep=",", strip.white =TRUE) [1] "Col1" "122" "Col3" "12" "45" [6] "Davis, Joe" "23" "78" "White, Jimmy" Read 9 items

3.2 Excel files To read and write Excel files you can use the package xlsReadWrite. This package provides the functions read.xls and write.xls. If the data is in the first sheet and starts at row 1, where the first row represent the column headers, then the call to read.xls is simple. library("xlsReadWrite") myfile <- "C:\\RFiles\\ExcelData.xls" mydf <- read.xls(myfile) mydf Col1 Col2 Col3 Col4 1 12 A 26919 john 2 23 A 33077 martin 3 5 B 31788 adam 4 56 C 30176 clair

The function read.xls uses the R default to determine if strings (characters) in the Excel data should be converted to factors. There are two ways to import strings as character in R. # all string data is converted to character type mydf <- read.xls(myfile, stringsAsFactors = T) # specify the type of each column mydf <- read.xls(myfile, colClasses = c( "numeric", "factor", "isodatetime",

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"character" ) )

Use the arguments sheet and from to import data from different works sheets and starting rows.

3.3 Databases There are several R packages that support the import (and export) of data from databases. • Package RODBC, provides an interface to databases that are ODBC compliant. These include, MS SQLServer, MS Access, Oracle. • Package RMySQL, provides an interface to the MySQL database • Package RJDBC, provides an interface to databases that are JDBC compliant. • Package RSQLite, not only interfaces this package with SQLite, it embeds the SQLite engine in R. We give a small example to import a table in R from an MS-Access database using ODBC. An important step is to set up ‘Data Source Name’ (DSN) using the administrative tools in Windows. Once that is done, R can import data from the corresponding database. • Go to the ‘Control Panel’, select ‘Administrative Tools’ and select ‘Data Sources (ODBC)’. • In the tab ‘User DSN’ click the ‘Add’ button, select the MS Access driver and click ‘Finish’ • Now chose a name for the data source, say, ‘MyAccessData’ and select the MS Access database file. Now the DSN has been set up and we can import the data from the database into R. First make a connection object using the function odbcConnect. library(RODBC) conn <- odbcConnect("MyAccessData") conn RODB Connection 1 Details: case=nochange DSN=MyAccessData DBQ=C:\DOCUMENTS AND SETTINGS\LONGHOW LAM\My

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3.4. THE FOREIGN PACKAGE

Documents\LonghowStuff\Courses\R\MyAccess.mdb DriverId=25 FIL=MS Access MaxBufferSize=2048 PageTimeout=5

If you have established a connection successfully, the connection object will display a summary of the connection. To display table information use sqlTables(conn), which will display all tables, including system tables. To import a specific table use the function sqlFetch. sqlFetch(conn, "Table1") ID Col1 Col2 Col3 Col4 1 1 John 123 1973-09-12 A 2 2 Martin 456 1999-12-31 B 3 3 Clair 345 1978-05-22 B

Use the function sqlQuery to submit an SQL query to the database and retrieve the result. myq <- "SELECT * from Table1 where Col4 = ’A’" sqlQuery(conn, myq) ID Col1 Col2 Col3 Col4 1 1 John 123 1973-09-12 A

You can have multiple connections to multiple databases, that can be useful if you need to collect and merge data from several sources. The function odbcDataSources lists all the available data sources. Don’t forget to close a connection with odbcClose(conn).

3.4 The Foreign package

46

4 Data Manipulation The programming language in R provides many different functions and mechanisms to manipulate and extract data. Let’s look at some of those for the different data structures.

4.1 Vector subscripts A part of a vector x can be selected by a general subscripting mechanism. x[subscript]

The simplest example is to select one particular element of a vector, for example the first one or the last one. x <- c(6,7,2,4) x[1] [1] 6 x[length(x)] [1] 4

Moreover, the subscript can have one of the following forms: A vector of positive natural numbers The elements of the resulting vector are determined by the numbers in the subscript. To extract the first three numbers: x [1] 10 5 3 6 x[1:3] [1] 10 5 3

To get a vector with the fourth, first and again fourth element of x:

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x[c(4,1,4)] [1] 6 10 6

One or more elements of a vector can be changed by the subscripting mechanism. To change the third element of a vector proceed as follows: x[3] <- 4

To change the first three elements: x[1:3] <- 4

The last two constructions are examples of a so-called replacement, in which the left hand side of the assignment operator is more than a simple identifier. Note also that the recycling rule applies, so the following code works (with a warning from R). x[1:3] <- c(1,2)

A logical vector The result is a vector with only those elements of x of which the logical vector has an element TRUE. x <- c(10,4,6,7,8) y <- x >9 y [1] TRUE FALSE FALSE FALSE FALSE x[y] [1] 10

or directly x[x>9] [1] 10

To change the elements of x which are larger than 9 to the value 9 do the following: x[x>9] <- 9

Note that the logical vector does not have to be of the same length as the vector you want to extract elements from.

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A vector of negative natural numbers All elements of x are selected except those that are in the subscript. x <- c(1,2,3,6) x[-(1:2)] # gives (x[3], x[4]) [1] 3 6

Note the subscript vector may address non-existing elements of the original vector. The result will be NA (Not Available). For example, x <- c(1,2,3,4,5) x[7] [1] NA x[1:6] [1] 1 2 3 4 5 NA

Some useful functions There are several useful R functions for working with vectors. length(x); sum(x); prod(x); max(x); min(x);

These functions are used to calculate the length, the sum, the product, the minimum and the maximum of a vector, respectively. The last four functions can also be used on more than one vector, in which case the sum, product, minimum, or maximum is taken over all elements of all vectors. x <- 10:71 y <- 45:21 sum(x,y); prod(x,y); max(x,y); min(x,y) ## chop off last part of a vector x <- 10:100 length(x) = 20

Note that sum(x,y) is equal to sum(c(x,y)). The function cumsum(x) generates a vector with the same length as the input vector. The i-th element of the resulting vector is equal to the sum of the first i elements of the input vector. Example: cumsum(rep(2,10)) [1] 2 4 6 8 10 12 14 16 18 20

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To sort a vector in increasing order, use the function sort. You can also use this function to sort in decreasing order by using the argument decrease = TRUE. x <- c(2,6,4,5,5,8,8,1,3,0) length(x) [1] 10 sort(x) [1] 0 1 2 3 4 5 5 6 8 8 sort(x, decr = TRUE) [1] 8 8 6 5 5 4 3 2 1 0

With the function order you can produce a permutation vector which indicates how to sort the input vector in ascending order. If you have two vectors x and y, you can sort x and permute y in such a way that the elements have the same order as the sorted vector x. x <- rnorm(10) y <- 1:10 z <- order(x) sort(x)

# # # #

create 10 random numbers create the numbers 1,2,3,..,10 create a permutation vector sort x

[1] -1.069 -0.603 -0.554 y[z] [1]

0.872

0.942

0.972

1.083

1.924

2.194

2.456

# change the order of elements of y 8

1

3

5

7

9

6 10

2

4

Try to figure out what the result of x[order(x)] is! The function rev reverses the order of vector elements. So rev(sort(x)) is a sorted vector in descending order. x <- rnorm(10) round( rev(sort(x)),2) [1] 1.18 1.00 0.87 0.57 -0.37 -0.42 -0.49 -0.72 -0.91 -1.26

The function unique returns a vector which only contains the unique values of the input vector. The function duplicated returns for every element a TRUE or FALSE depending on whether or not that element has previously appeared in the vector. x <- c(2,6,4,5,5,8,8,1,3,0) unique(x) [1] 2 6 4 5 8 1 3 0 duplicated(x) [1] FALSE FALSE FALSE FALSE

TRUE FALSE

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Our last example of a vector manipulation function is the function diff. It returns a vector which contains the differences between the consecutive input elements. x <- c(1,3,5,8,15) diff(x) [1] 2 2 3 7

So the resulting vector of the function diff is always at least one element shorter than the input vector. An additional lag argument can be used to specify the lag of differences to be calculated. x <- c(1,3,5,8,15) diff(x, lag=2) [1] 4 5 10

So in this case with lag=2, the resulting vector is two elements shorter.

4.2 Matrix subscripts As with vectors, parts of matrices can be selected by the subscript mechanism. The general scheme for a matrix x is given by: x[subscript]

Where subscript has one of the following four forms: 1. A pair (rows, cols) where rows is a vector representing the row numbers and cols is a vector representing column numbers. Rows and/or cols can be empty or negative. The following examples will illustrate the different possibilities. x <- matrix(1:36, ncol=6) ## the element in row 2 and column 6 of x x[2,6] [1] 32 ## the third row of x x[3, ] [1] 3 9 15 21 27 33 ## the element in row 3 and column 1 and ## the element in row 3 and column 5

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x[3,c(1,5)] [1] 3 27 ## show x, except for the first column x[,-1]

[1,] [2,] [3,] [4,] [5,] [6,]

[,1] [,2] [,3] [,4] [,5] 7 13 19 25 31 8 14 20 26 32 9 15 21 27 33 10 16 22 28 34 11 17 23 29 35 12 18 24 30 36

A negative pair results in a so-called minor matrix where a column and row is omitted. x[-3,-4] [,1] [,2] [,3] [,4] [,5] [1,] 1 7 13 25 31 [2,] 2 8 14 26 32 [3,] 4 10 16 28 34 [4,] 5 11 17 29 35 [5,] 6 12 18 30 36

The matrix x remains the same, unless you assign the result back to x. x <- x[-3,4]

As with vectors, matrix elements or parts of matrices can be changed by using the matrix subscript mechanism and the assignment operator together. To change one element: x[4,5] <- 5

To change a complete column: x <- matrix(rnorm(100),ncol=10) x[ ,1] <- 1:10

2. A logical matrix with the same dimension as x

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CHAPTER 4. DATA MANIPULATION x <- matrix(1:36,ncol=6) y <- x>19 y [,1] [,2] [,3] [,4] [1,] FALSE FALSE FALSE FALSE [2,] FALSE FALSE FALSE TRUE [3,] FALSE FALSE FALSE TRUE [4,] FALSE FALSE FALSE TRUE [5,] FALSE FALSE FALSE TRUE [6,] FALSE FALSE FALSE TRUE

[,5] TRUE TRUE TRUE TRUE TRUE TRUE

4.2. MATRIX SUBSCRIPTS

[,6] TRUE TRUE TRUE TRUE TRUE TRUE

x[y] [1] 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Note that the result of subscripting with a logical matrix is a vector. This mechanism can be used to replace elements of a matrix. For example: x <- matrix(rnorm(100),ncol=10) x[x>0] <- 0

3. A matrix r with two columns A row of r consists of two numbers, each row of r selects a matrix element of x. The result is a vector with the selected elements from x. x <- matrix(1:36,ncol=6) x [,1] [,2] [,3] [,4] [,5] [,6] [1,] 1 7 13 19 25 [2,] 2 8 14 20 26 [3,] 3 9 15 21 27 [4,] 4 10 16 22 28 [5,] 5 11 17 23 29 [6,] 6 12 18 24 30

31 32 33 34 35 36

r <- cbind( c(1,2,5), c(3,4,4)) r [,1] [,2] [1,] 1 3 [2,] 2 4 [3,] 5 4 x[r] [1] 13 20 23

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4. A single number or one vector of numbers. In this case the matrix is treated like a vector where all the columns are stacked. x <- matrix(1:36,ncol=6) x[3];x[9];x[36] [1] 3 [1] 9 [1] 36 x[21:30] [1] 21 22 23 24 25 26 27 28 29 30

4.3 Manipulating Data frames 4.3.1 Extracting data from data frames A data frame can be considered as a generalized matrix, consequently all subscripting methods that work on matrices also work on data frames. However, data frames offer a few extra possibilities. Lets import the data in the file cars.csv, a comma separated text file, so that different aspects of data frame manipulation can be demonstrated. cars <- read.csv("cars.csv", row.names=1)

The argument row.names is specified in the read.csv function because the first column of the data file contains row names that we will use in our data fame. To see the column names of the cars data frame use the function names: names(cars) [1] "Price" [5] "Type"

"Country" "Weight"

"Reliability" "Mileage" "Disp." "HP"

To select a specific column from a data frame use the $ symbol or double square brackets and quotes: prices <- cars$Price prices <- cars[["Price"]]

The object prices is a vector. If you want the result to be a data frame use single square brackets:

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prices2 <- cars["Price"]

When using single brackets it is possible to select more than one column from a data frame. The result is again a data frame: test <- cars[c("Price","Type")]

To select a specific row by name of the data frame ‘cars’ use the following R code: cars["Nissan Van 4", ] Price Country Reliability Mileage Type Weight Disp. HP Nissan Van 4 14799 Japan NA 19 Van 3690 146 106

The result is a data frame with one row. To select more rows use a vector of names: cars[c("Nissan Van 4", "Dodge Grand Caravan V6"), ]

If the given row name does not exist, R will return a row with NA’s. cars["Lada",] Price Country Reliability Mileage Type Weight Disp. HP NA NA NA NA NA NA NA NA NA

Rows from a data frame can also be selected using row numbers. Select cases 10 trough 14 from the cars data frame. cars[10:14,] Price Country Reliability Mileage Type Weight Disp. HP Subaru Justy 3 5866 Japan NA 34 Small 1900 73 73 Toyota Corolla 4 8748 Japan/USA 5 29 Small 2390 97 102 Toyota Tercel 4 6488 Japan 5 35 Small 2075 89 78 Volkswagen Jetta 4 9995 Germany 3 26 Small 2330 109 100 Chevrolet Camaro V8 11545 USA 1 20 Sporty 3320 305 170

The first few rows or the last few rows can be extracted by using the functions head or tail.

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head(cars,3) Eagle Summit 4 Ford Escort 4 Ford Festiva 4

Price Country Reliability Mileage Type Weight Disp. HP 8895 USA 4 33 Small 2560 97 113 7402 USA 2 33 Small 2345 114 90 6319 Korea 4 37 Small 1845 81 63

tail(cars,2) Price Country Reliability Mileage Type Weight Disp. HP Nissan Axxess 4 13949 Japan NA 20 Van 3185 146 138 Nissan Van 4 14799 Japan NA 19 Van 3690 146 106

To subset specific cases from a data frame you can also use a logical vector. When you provide a logical vector in a data frame subscript, only the cases which correspond with a TRUE are selected. Suppose you want to get all cars from the cars data frame that have a weight of over 3500. First create a logical vector tmp: tmp <- cars$Weight > 3500

Use this vector to subset: cars[tmp, ] Ford Thunderbird V6 Chevrolet Caprice V8 Ford LTD Crown Victoria V8 Dodge Grand Caravan V6 Ford Aerostar V6 Mazda MPV V6 Nissan Van 4

Price Country Reliability Mileage Type Weight Disp. HP 14980 USA 1 23 Medium 3610 232 140 14525 USA 1 18 Large 3855 305 170 17257 USA 3 20 Large 3850 302 150 15395 USA 3 18 Van 3735 202 150 12267 USA 3 18 Van 3665 182 145 14944 Japan 5 19 Van 3735 181 150 14799 Japan NA 19 Van 3690 146 106

A handy alternative is the function subset. It returns a the subset as a data frame. The first argument is the data frame. The second argument is a logical expression. In this expression you use the variable names without proceeding them with the name of the data frame, as in the above example. subset(cars, Weight > 3500 & Price < 15000) Price Country Reliability Mileage Type Weight Disp. HP Ford Thunderbird V6 14980 USA 1 23 Medium 3610 232 140 Chevrolet Caprice V8 14525 USA 1 18 Large 3855 305 170 Ford Aerostar V6 12267 USA 3 18 Van 3665 182 145 Mazda MPV V6 14944 Japan 5 19 Van 3735 181 150 Nissan Van 4 14799 Japan NA 19 Van 3690 146 106

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4.3.2 Adding columns to a data frame The function cbind can be used to add additional columns to a data frame. For example, the vector ‘maxvel’ with the maximum velocities of the cars can be added to the ‘cars’ data frame as follows. new.cars <- cbind(cars, Max.Vel = maxvel)

The left hand side of the = specifies the column name in the ‘new.cars’ data frame and the right hand side is the vector you want to add. Or alternatively, use the following syntax cars$max.vel = maxvel

The function cbind can also be used on two or more data frames. For example cbind(dataframe1, dataframe2)

4.3.3 Combining data frames Use the function rbind to combine (or stack) two or more data frames. Consider the following two data frames ‘rand.df1’ and ‘rand.df2’. rand.df1 <- data.frame( norm = rnorm(5), binom = rbinom(5,10,0.1), unif=runif(5) ) rand.df1 norm binom unif 1 -1.1477095 2 0.6230449 2 0.6689266 0 0.9921276 3 0.3738174 2 0.7115776 4 2.2641381 2 0.9318150 5 -1.7682772 0 0.6455379 rand.df2 <- data.frame( chisq = rchisq(5,3), binom = rbinom(5,10,0.1), unif=runif(5) )

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rand.df2 chisq binom unif 1 1.955729 1 0.4543552 2 12.661964 1 0.8731595 3 7.433911 1 0.9460346 4 3.642188 0 0.6632598 5 6.134571 1 0.7688208

These two data frames have two columns in common: ‘binom’ and ‘unif’. When we only need to combine the common columns of these data frames, you can use the subscripting mechanism and the function rbind: rand.comb <- rbind( rand.df1[ , c("unif","binom")], rand.df2[ , c("unif", "binom")] ) rand.comb unif binom 1 0.6230449 2 2 0.9921276 0 3 0.7115776 2 4 0.9318150 2 5 0.6455379 0 6 0.4543552 1 7 0.8731595 1 8 0.9460346 1 9 0.6632598 0 10 0.7688208 1

The functions rbind expects that the two data frames have the same columns. The function rbind.fill in the ‘reshape’ package can stack two or more data frames with any columns. It will fill a missing column with NA. library(reshape) rbind.fill(rand.df1,rand.df2,rand.df1) norm binom unif chisq 1 -3.0309036 1 0.39182298 NA 2 1.5897306 0 0.04189106 NA 3 1.3976871 2 0.09756326 NA 4 0.4867048 0 0.70522637 NA 5 -1.7282814 0 0.42753294 NA 6 NA 0 0.98808959 5.6099156 7 NA 1 0.56966460 2.5105316 8 NA 1 0.53950251 1.0920222 9 NA 0 0.01064824 0.2301267 10 NA 1 0.87821054 3.8488757

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4.3.4 Merging data frames Two data frames can be merged into one data frame using the function merge. (The join operation in database terminology). If the original data frames contain identical columns, these columns only appear once in the merged data frame. Consider the following two data frames: test1
read.delim("test1.txt", sep =" ") year 1963 1970 1971 1974

test2
read.delim("test2.txt", sep =" ") year A FA 1963 0.42 0.12 1970 0.26 0.57 1971 0.87 0.37 1974 0.86 0.15

BA 0.12 0.53 0.53 0.81

HR 0.27 0.74 0.28 0.29

test.merge <- merge(test1,test2) test.merge name year BA HR A FA 1 Dick 1963 0.12 0.27 0.42 0.12 2 Gose 1970 0.53 0.74 0.26 0.57 3 Heleen 1974 0.81 0.29 0.86 0.15 4 Rolf 1971 0.53 0.28 0.87 0.37

By default the merge function leaves out rows that where not matched, consider the following data sets. quotes = data.frame(date=1:100, quote=runif(100)) testfr = data.frame(date=c(5,7,9, 110), position = c(45,89,14,90))

To extend the data frame testfr with the wright quote data from the data frame quotes, and to keep the last row of testfr for which there is no quote use the following code. testfr = merge(quotes,testfr,all.y=TRUE) testfr date quote position 1 5 0.6488612 45

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7 0.4995684 9 0.5242953 110 NA

4.3. MANIPULATING DATA FRAMES

89 14 90

For more complex examples see the Help file of the function merge: ?merge.

4.3.5 Aggregating data frames The function aggregate is used to aggregate data frames. It splits the data frame into groups and applies a function on each group. The first argument is the data frame, the second argument is a list of grouping variables, the third argument is a function that returns a scalar. A small example: gr <- c("A","A","B","B") x <- c(1,2,3,4) y <- c(4,3,2,1) myf <- data.frame(gr, x, y) aggregate(myf, list(myf$gr), mean) Group.1 gr x y 1 A NA 1.5 3.5 2 B NA 3.5 1.5

R will apply the function on each column of the data frame. This means also on the grouping column ‘gr’. This column is of type factor, numerical calculations can not be performed on factors hence the NA’s. You can leave out the grouping columns when calling the aggregate function. aggregate( myf[, c("x","y")], list(myf$gr), mean ) Group.1 x y 1 A 1.5 3.5 2 B 3.5 1.5

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4.3.6 Stacking columns of data frames The function stack can be used to stack columns of a data frame into one column and one grouping column. Consider the following example: group1 <- rnorm(3) group2 <- rnorm(3) group3 <- rnorm(3) df <- data.frame(group1,group2, group3) stack(df) values ind 1 0.63706989 group1 2 -0.76002786 group1 3 0.05912762 group1 4 0.20074146 group2 5 1.11071470 group2 6 0.43529956 group2 7 1.35128903 group3 8 -0.39660149 group3 9 -0.65003395 group3

So by default all the columns of a data frame are stacked. Use the select argument to stack only certain columns. stack(df,select=c("group1","group3"))

4.3.7 Reshaping data The function reshape can be used to transform a data frame in wide format into a data frame in long format. In a wide format data frame the different measurements of one ‘subject’ are in multiple columns, whereas a long format data frame has the different measurements of one subject in multiple rows. df.wide <- data.frame( Subject = c(1, 2), m1 = c(4, 5), m2 = c(5.6, 7.8), m3 = c(3.6, 6.7) ) df.wide Subject m1 m2 m3 1 1 4 5.6 3.6

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2

4.4. ATTRIBUTES

5 7.8 6.7

df.long <- reshape(df.wide, varying = list(c("m1", "m2", "m3")), idvar = "Subject", direction = "long", v.names = "Measurement" ) df.long Subject time Measurement 1.1 1 1 4.0 2.1 2 1 5.0 1.2 1 2 5.6 2.2 2 2 7.8 1.3 1 3 3.6 2.3 2 3 6.7

4.4 Attributes Vectors, matrices and other objects in general, may have attributes. These are other objects attached to the main object. Use the function attributes to get a list of all the attributes of an object. x <- rnorm(10) attributes(x) NULL

In the above example the vector x has no attributes. You can either use the function attr or the function structure to attach an attribute to an object. attr(x, "description") <- "The unit is month" x [1] 1.3453003 -1.4395975 1.0163646 -0.6566600 [6] -1.2427861 1.4967771 0.6230324 -0.5538395 attr(, "description"): [1] "The unit is month"

0.4412399 1.0781191

The first argument of the function attr is the object, the second argument is the name of the attribute. The expression on the right hand side of the assignment operator will be the attribute value. Use the structure function to attach more than one attribute to an object.

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x <- structure(x, atr1=8,atr2="test") x [1] 1.3453003 -1.4395975 1.0163646 -0.6566600 [6] -1.2427861 1.4967771 0.6230324 -0.5538395 attr(, "description"): [1] "The unit is month" attr(, "atr1"): [1] 8 attr(, "atr2"): [1] "test"

0.4412399 1.0781191

When an object is printed, the attributes (if any) are printed as well. To extract an attribute from an object use the functions attributes or attr. The function attributes returns a list of all the attributes from which you can extract a specific component. attributes(x) $description: [1] "The unit is month" $atr1: [1] 8 $atr2: [1] "test"

In order to get the description attribute of x use: attributes(x)$description [1] "The unit is month"

Or type in the following construction: attr(x,"description") [1] "The unit is month"

4.5 Character manipulation There are several functions in R to manipulate or get information from character objects.

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4.5.1 The functions nchar, substring and paste x <- c("a","b","c") mychar1 <- "This is a test" mychar2 <- "This is another test" charvector <- c("a", "b", "c", "test")

The function nchar returns the length of a character object, for example: nchar(mychar1) [1] 15 nchar(charvector) [1] 1 1 1 4

The function substring returns a substring of a character object. For example: x <- c("Gose", "Longhow", "David") substring(x,first=2,last=4) [1] "ose" "ong" "avi"

The function paste will paste two or more character objects. For example, to create a character vector with: ”number.1”, ”number.2”, ...,”number.10” proceed as follows: paste("number",1:10, sep=".") [1] "number.1" "number.2" "number.3" [5] "number.5" "number.6" "number.7" [9] "number.9" "number.10"

"number.4" "number.8"

The argument sep is used to specify the separating symbol between the two character objects. paste("number",1:10, sep="-") [1] "number-1" "number-2" "number-3" [5] "number-5" "number-6" "number-7" [9] "number-9" "number-10"

"number-4" "number-8"

Use sep="" for no space between the character objects.

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4.5.2 Finding patterns in character objects The functions regexpr and grep can be used to find specific character strings in character objects. The functions use so-called regular expressions, a handy format to specify search pattern. See the help for regexpr to find out more about regular expressions. Let’s extract the row names from our data frame ‘cars’. car.names <- row.names(cars)

We want to know if a string in ‘car.names’ starts with ‘Volvo’ and if there is, the position it has in ‘car.names’. Use the function grep as follows: grep("Volvo", car.names) [1] 37

So element 37 of the car.names vector is a name that contains the string ‘Volvo’, which is confirmed by a quick check: car.names[37] [1] "Volvo 240 4"

To find the car names with second letter ‘a’, we must use a more complicated regular expression tmp <- grep("^.a",car.names) car.names[tmp] [1] "Eagle Summit 4" "Mazda Protege 4" [3] "Mazda 626 4" "Eagle Premier V6" [5] "Mazda 929 V6" "Mazda MPV V6"

For those who are familiar with wildcards (aka globbing) there is a handy function glob2rx that transforms a wildcard to a regular expression. rg <- glob2rx("*.tmp") rg [1] "^.*\\.tmp$"

To find patterns in texts you can also use the regexpr function. This function also makes use of regular expressions, however it returns more information than grep.

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Volvo.match <- regexpr("Volvo",car.names) Volvo.match [1] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 [19] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 [37] 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 [55] -1 -1 -1 -1 -1 -1 attr(, "match.length"): [1] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 [19] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 [37] 5 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 [55] -1 -1 -1 -1 -1 -1

-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1

-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1

The result of regexpr is a numeric vector with a match.length attribute. A minus one means no match was found, a positive number means a match was found. In our example we see that element 37 of ‘Volvo.match’ equals one, which means that ‘Volvo’ is part of the character string in element 37 of ‘car.names’. Again a quick check: car.names[37] [1] "Volvo 240 4"

In the above result you could immediately see that element 37 of ‘car.names’ is a match. If character vectors become too long to see the match quickly, use the following trick: index <- 1:length(car.names) index[Volvo.match > 0] [1] 37

The result of the function regexpr contains the attribute match.length, which gives the length of the matched text. In the above example match Volvo consists of 5 characters. This attribute can be used together with the function substring to extract the found pattern from the character object. Consider the following example which uses a regular expression, the ‘match.length’ attribute, and the function substring to extract the numeric part and character part of a character vector. x <- c("10 Sept", "Oct 9th", "Jan 2", "4th of July") w <- regexpr("[0-9]+", x)

The regular expression "[0-9]+" matches an integer.

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w [1] 1 5 5 1 attr(, "match.length"): [1] 2 1 1 1 # # # #

The The The The

1 5 5 1

means means means means

there there there there

is is is is

a a a a

match match match match

on on on on

position position position position

1 5 5 1

of of of of

"10 Sept" "Oct 9th" "Jan 2" "4th of July"

In the attribute match.length the 2 indicates the length of the match in ”10 Sept”. Use the substring function to extract the integers. Note that the result of the substring function is of type character. To convert that to numeric, use the as.numeric function: as.numeric(substring(x, w, w+attr(w, "match.length")-1)) [1] 10 9 2 4

4.5.3 Replacing characters The functions sub and gsub are used to replace a certain pattern in a character object with another pattern. mychar <- c("My_test", "My_Test_3", "_qwerty_pop_") sub(pattern="[_]", replacement=".", x=mychar) [1] "My.test" "My.Test_3" ".qwerty_pop_" gsub(pattern="[_]", replacement=".", x=mychar) [1] "My.test" "My.Test.3" ".qwerty.pop."

Note that by default, the pattern argument is a regular expression. When you want to replace a certain string it may be handy to use the fixed argument as well. mychar <- c("mytest", "abctestabc", "test.po.test") gsub(pattern="test", replacement="", x=mychar, fixed=TRUE) [1] "my" "abcabc" ".po."

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4.5.4 Splitting characters A character string can be split using the function strsplit. The two main arguments are x and split. The function returns the split results in a list, each list componenent is the split result of an element of x. strsplit(x = c("Some text", "another string", split = NULL) [[1]] [1] "S" "o" "m" "e" " " "t" "e" "x" "t" [[2]] [1] "a" "n" "o" "t" "h" "e" "r" " " "s" "t" "r" "i" "n" "g"

The argument x is a vector of characters, and split is a character vector containing regular expressions that are used for the split. If it is NULL as in the above example, the character strings are split into single characters. If it is not null, R will look at the elements in x, if the split string can be matched the characters left of the match will be in the output and the characters right of the match will be in the output. strsplit( x = c("Some~text" , "another-string", "Amsterdam is a nice city"), split = "[~-]" ) [[1]] [1] "Some" "text" [[2]] [1] "another" "string" [[3]] [1] "Amsterdam is a nice city"

4.6 Creating factors from continuous data The function cut can be used to create factor variables from continuous variables. The first argument x is the continuous vector and the second argument breaks is a vector of breakpoints, specifying intervals. For each element in x the function cut returns the interval as specified by breaks that contains the element.

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x <- 1:15 breaks <- c(0,5,10,15,20) cut(x,breaks) [1] (0,5] (0,5] (0,5] (0,5] (0,5] (5,10] (5,10] [10] (5,10] (10,15] (10,15] (10,15] (10,15] (10,15] Levels: (0,5] (5,10] (10,15] (15,20]

(5,10]

(5,10]

The function cut returns a vector of tye ‘factor’, each element of this vector shows the interval to which the corresponding element of the original vector corresponds. If only one number is specified for the argument breaks, that number is used to divide x into equal length intervals. cut( x, breaks=5) [1] (0.986,3.79] (0.986,3.79] (0.986,3.79] (3.79,6.6] (3.79,6.6] [6] (3.79,6.6] (6.6,9.4] (6.6,9.4] (6.6,9.4] (9.4,12.2] [11] (9.4,12.2] (9.4,12.2] (12.2,15] (12.2,15] (12.2,15] Levels: (0.986,3.79] (3.79,6.6] (6.6,9.4] (9.4,12.2] (12.2,15]

The names of the different levels are created by R automatically, they have the form (a,b]. You can change this by specifying an extra labels argument. x <- rnorm(15) cut(x, breaks=3, labels=c("low","medium","high")) [1] high medium medium medium medium high low [11] high low low medium high Levels: low medium high

69

high

low

low

5 Writing functions 5.1 Introduction Most tasks are performed by calling a function in R. In fact, everything we have done so far is calling an existing function which then performed a certain task resulting in some kind of output. A function can be regarded as a collection of statements and is an object in R of class ‘function’. One of the strengths of R is the ability to extend R by writing new functions. The general form of a function is given by: functionname <- function(arg1, arg2,...) { Body of function: a collection of valid statements }

In the above display arg1 and arg2 in the function header are input arguments of the function. Note that a function doesn’t need to have any input arguments. The body of the function consists of valid R statements. For example, the commands, functions and expressions you type in the R console window. Normally, the last statement of the function body will be the return value of the function. This can be a vector, a matrix or any other data structure. The following short function meank calculates the mean of a vector x by removing the k percent smallest and the k percent largest elements of the vector. meank <- function(x,k){ xt <- quantile(x, c(k,1-k)) mean( x[ x > xt[1] & x < xt[2] ]) }

Once the function has been created, it can be ran. test <- rnorm(100) meank(test) [1] 0.00175423

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The function meank calls two standard functions, quantile and mean. Once meank is created it can be called from any other function. If you write a short function, a one-liner or two-liner, you can type the function directly in the console window. If you write longer functions, it is more convenient to use a script file. Type the function definition in a script file and run the script file. Note that when you run a script file with a function definition, you will only define the function (you will create a new object). To actually run it, you will need to call the function with the necessary arguments. You can use your favorite text editor to create or edit functions. Use the function source to evaluate expressions from a file. Suppose ‘meank.txt’ is a text file, saved on your hard disk, containing the function definition of meank. meank <- function(x,k){ xt <- quantile(x,c(k,1-k)) mean(x[ x>xt[1] & x
The following statement will create the function meank in R: # note the use of double slashes... source("C:\\SFunctions\\meank.txt")

Now you can run the function: meank(test) [1] 0.00175423

If you want to put a comment inside a function, use the # symbol. Anything between the # symbol and the end of the line will be ignored. test <- function(x){ # This line will be ignored # It is useful to insert code explanations for others (and yourself!) sqrt(2*x) }

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Writing large functions in R can be difficult for novice users. You may wonder where and how to begin, how to check input parameters or how to use loop structures. Fortunately, the code of many functions can be viewed directly. For example, just type the name of a function without brackets in the console window and you will get the code. Don’t be intimidated by the (lengthy) code. Learn from it, by trying to read line by line and looking at the help of the functions that you don’t know yet. Some functions call ‘internal’ functions or pre-compiled code, which can be recognized by calls like: .C, .Internal or .Call.

5.2 Arguments and variables 5.2.1 Required and optional arguments When calling functions in R, the syntax of the function definition determines whether argument values are required or optional. With optional arguments, the specification of the arguments in the function header is: argname = defaultvalue

In the following function, for example, the argument x is required and R will give an error if you don’t provide it. The argument k is optional, having the default value 2: power <- function(x, k=2){ x^k } power(5) [1] 25 power() Error in power() : argument "x" is missing, with no default

However, we can specify a different value for k: power(5,3) [1] 125

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5.2.2 The ‘...’ argument The three dots argument can be used to pass arguments from one function to another. For example, graphical parameters that are passed to plotting functions or numerical parameters that are passed to numerical routines. Suppose you write a small function to plot the sin function from zero to xup. plotsin <- function(xup = 2*pi, ...) { x <- seq(0, xup, l = 100) plot(x,sin(x), type = "l", ...) } plotsin(col="red")

The function plotsin now accepts any argument that can be passed to the plot function (like col, xlab, etc.) without needing to specify those arguments in the header of plotsin.

5.2.3 Local variables Assignments of variables inside a function are local, unless you explicitly use a global assignment (the <<- construction or the assign function). This means a normal assignment within a function will not overwrite objects outside the function. An object created within a function will be lost when the function has finished. Only if the last line of the function definition is an assignment, then the result of that assignment will be returned by the function. In the next example an object x will be defined with value zero. Inside the function functionx, xis defined with value 3. Executing the function functionx will not affect the value of the global variable ‘x’. x <- 0 functionx <- function(){ x <- 3 } functionx() [1] 3 x [1] 0

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If you want to change the global variable x with the return value of the function functionx, you can assign the function result to x. # overwriting the object x with the result of functionx x <- functionx()

The arguments of a function can be objects of any type, even functions! Consider the next example: test <- function(n, fun) { u <- runif(n) fun(u) } test(10,sin) [1] 0.28078332 0.30438298 0.55219120 0.37357375 ...

The second argument of the function test needs to be a function which will be called inside the function.

5.2.4 Returning an object Often the purpose of a function is to do some calculations on input arguments and return the result. By default the last expression of the function will be returned. myf <- function(x,y){ z1 <- sin(x) z2 <- cos(y) z1+z2 }

In the above example z1 + z2 is returned, note that the individual objects z1 and z2 will be lost. You can only return one object. If you want to return more than one object, you have to return a list where the components of the list are the objects to be returned. For example: myf <- function(x,y){ z1 <- sin(x) z2 <- cos(y) list(z1,z2) }

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To exit a function before it reaches the last line, use the return function. Any code after the return statement inside a function will be ignored. For example: myf <- function(x,y){ z1 <- sin(x) z2 <- cos(y) if(z1 < 0){ return( list(z1,z2)) else{ return( z1+z2) } }

5.2.5 The Scoping rules The scoping rules of a programming language are the rules that determine how the programming language finds a value for a variable. This is especially important for free variables inside a function and for functions defined inside a function. Let’s look at the following example function. myf <- function(x) { y = 6 z = x + y + a1 a2 = 9 insidef = function(p){ tmp = p*a2 sin(tmp) } 2*insidef(z) }

In the above function • x, p are formal arguments. • y, tmp are local variables. • a2 is a local variable in the function myf. • a2 is a free variable in the function insidef. R uses a so-called lexical scoping rule to find the value of free variables, see [3]. With lexical scoping, free variables are first resolved in the environment in which the function was created. The following calls to the function myf show this rule.

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## R tries to find a1 in the environment where myf was created ## but there is no object a1 myf(8) Error in myf(8) : object "a1" not found ## define the objects a1 and a2 but what value ## did a2 in the function insidef get? a1 <- 10 a2 <- 1000 myf(8) [1] 1.392117 ## It took a2 in myf, so a2 has the value 9

5.2.6 Lazy evaluation When writing functions in R, a function argument can be defined as an expression like: myf <- function(x, nc = length(x)) { rest of the function }

When arguments are defined in such a way you must be aware of the lazy evaluation mechanism in R. This means that arguments of a function are not evaluated until needed. Consider the following examples. myf <- function(x, nc = length(x)) { x <- c(x, x) print(nc) } xin <- 1:10 myf(xin) [1] 20

The argument nc is evaluated after x has doubled in length, it is not ten, the initial length of x when it entered the function. logplot <- function(y, ylab = deparse(substitute(y))) { y <- log(y) plot(y, ylab = ylab) }

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The plot will create a nasty label on the y axis. This is the result of lazy evaluation, ylab is evaluated after y has changed. One solution is to force an evaluation of ylab first. logplot <- function(y, ylab = deparse(substitute(y))) { ylab y <- log(y) plot(y, ylab = ylab) }

5.3 Control flow The following shows a list of constructions to perform testing and looping. These constructions can also be used outside a function to control the flow of execution.

5.3.1 Tests with if and switch The general form of the if construction has the form if(test) { ...true statements... } else { ...false statements... }

where test is a logical expression like x < 0, x < 0 & x > -8. R evaluates the logical expression if it results in TRUE then it executes the true statements. If the logical expression results in FALSE then it executes the false statements. Note that it is not neccesary to have the else block.

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Simple example Adding two vectors in R of different length will cause R to recycle the shorter vector. The following function adds the two vectors by chopping of the longer vector so that it has the same length as the shorter. myplus <- function(x, y){ n1 <- length(x) n2 <- length(y) if(n1 > n2){ z <- x[1:n2] + y } else{ z <- x + y[1:n1] } z } myplus(1:10, 1:3) [1] 2 4 6

The switch function has the following general form. switch(object, "value1" = {expr1}, "value2" = {expr2}, "value3" = {expr3}, {other expressions} )

If object has value value1 then expr1 is executed, if it has value2 then expr2 is executed and so on. If object has no match then other expressions is executed. Note that the block {other expressions} does not have to be present, the switch will return NULL in case object does not match any value. An expression expr1 in the above construction can consist of multiple statements. Each statement should be separated with a ; or on a separate line and surrounded by curly brackets. Simple example Choosing between two calculation methods. mycalc <- function(x, method="ml"){ switch(method, "ml" = { my.mlmethod(x) }, "rml" = { my.rmlmethod(x) } ) }

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5.3.2 Looping with for, while and repeat The for, while and repeat constructions are designed to perfom loops in R. They have the following forms. for (i in for_object) { some expressions }

In the for loop some expressions are evaluated for each element i in for object. Simple example A recursive filter. arsim <- function(x, phi){ for(i in 2:length(x)) { x[i] <- x[i] + phi*x[i-1] } x } arsim(1:10, 0.75) [1] 1.000000 2.750000 5.062500 7.796875 10.847656 [6] 14.135742 17.601807 21.201355 24.901016 28.675762

Note that the for object could be a vector, a matrix, a data frame or a list. while (condition) { some expressions }

In the while loop some expressions are repeatedly executed until the logical condition is FALSE. Make sure that the condition is FALSE at some stage, otherwise the loop will go on indefinitely.

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Simple example mycalc <- function(){ tmp <- 0 n <- 0 while(tmp < 100){ tmp <- tmp + rbinom(1,10,0.5) n <- n +1 } cat("It took ") cat(n) cat(" iterations to finish \n") }

repeat { some expressions }

In the repeat loop some expressions are repeated ‘infinitely’, so repeat loops will have to contain a break statement to escape them.

5.4 Debugging your R functions 5.4.1 The traceback function The R language provide the user with some tools to track down unexpected behavior during the execution of (user written) functions. For example, • A function may throw warnings at you. Although warnings do not stop the execution of a function and could be ignored, you should check out why a warning is produced. • A function stops because of an error. Now you must really fix the function if you want it to continue to run until the end. • Your function runs without warnings and errors, however the number it returns does not make any sense. The first thing you can do when an error occurs is to call the function traceback. It will list the functions that were called before the error occurred. Consider the following two functions.

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myf <- function(z) { x <- log(z) if( x > 0) { print("PPP") } else { print("QQQ") } } testf <- function(pp) { myf(pp) }

Executing the command testf(-9) will result in an error, execute traceback to see the function calls before the error. Error in if (x > 0) { : missing value where TRUE/FALSE needed In addition: Warning message: NaNs produced in: log(x) traceback() 2: myf(pp) 1: testf(-9)

Sometimes it may not be obvious where a warning is produced, in that case you may set the option options(warn = 2)

Instead of continuing the execution, R will now halt the execution if it encounters a warning.

5.4.2 The warning and stop functions You, as the writer of a function, can also produce errors and warnings. In addition to putting ordinary print statements like print("Some message") in your function, you can use the function warning. For example,

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variation <- function(x) { if(min(x) <= 0) { warning("variation only useful for positive data") } sd(x)/mean(x) } variation(rnorm(100) [1] 19.4427 Warning message: variation only useful for positive data in: variation(rnorm(100))

If you want to raise an error you can use the function stop. In the above example when we replace warning by stop R would halt the execution. variation(rnorm(100)) Error in variation(rnorm(100)) : variation only useful for positive data

R will treat your warnings and errors as normal R warnings and errors. That means for example, the function traceback can be used to see the call stack when an error occurred.

5.4.3 Stepping through a function With traceback you will now in which function the error occurred, it will not tell you where in the function the error occurred. To find the error in the function you can use the function debug, which will tell R to execute the function in debug mode. If you want to step through everything you will need to set debug flag for the main function and the functions that the main function calls: debug(testf) debug(myf)

Now execute the function testf, R will display the body of the function and a browser environment is started. testf(-9) debugging in: testf(-9) debug: { myf(pp) } Browse[1]>

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In the browser environment there are a couple of special commands you can give. • n, executes the current line and prints the next one. • c, executes the rest of the function without stopping. • Q, quits the debugging completely, so halting the execution and leaving the browser environment. • where, shows you where you are in the function call stack. In addition to these special commands, the browser environment acts like an interactive R session, that means you could enter commands like • ls(), show all objects in the local environment, the current function. • print(object) or just object, prints the value of the object. • 675/98876, just some calculations. • object <- 89, assigning a new value to an object, the debugging process will continue with this new value. If the debug process is finished remove the debug flag undebug(myf).

5.4.4 The browser function It may happen that an error occurs at the end of a lengthy function. To avoid stepping through the function line by line manually, the function browser can be used. Inside your function insert the browser() statement at a location where you want to enter the debugging environment. myf <- function(x) { ... some code ... browser() ... some code ... }

Run the function myf as normally. When R reaches the browser() statement then the normal execution is halted and the debug environment is started.

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6 Efficient calculations 6.1 Vectorized computations The efficiency of calculations depends on how you perform them. Vectorized calculations, for example, avoid going trough individual vector or matrix elements and avoid for loops. Though very efficient, vectorized calculations cannot always be used. On the other hand, users having a Pascal or C programming background often forget to apply vectorized calculations where they could be used. We therefore give a few examples to demonstrate its use. A weighted average Take advantage of the fact that most calculations and mathematical operations already act on each element of a matrix or vector. For example, log(x), sin(x) calculate the log and sin on all elements of the vector x. For example, to calculate a weighted average W P xi w i W = Pi i wi in R of the numbers in a vector x with corresponding weights in the vector w, use: ave.w <- sum(x*w)/sum(w)

The multiplication and divide operator act on the corresponding vector elements. Replacing numbers Suppose we want to replace all elements of a vector which are larger than one by the the value 1. You could use the following construction (as in C or Fortran) ## timing the calculation using Sys.time tmp <- Sys.time() x <- rnorm(15000) for (i in 1:length(x)){ if(x[i] > 1){

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x[i] <- 1 } } Sys.time - tmp Time difference of 0.2110000 secs

However, the following construction is much more efficient: tmp <- Sys.time() x <- rnorm(15000) x[x>1] <- 1 Sys.time() - tmp Time difference of 0.0400002 secs

The second construction works on the complete vector x at once instead of going through each separate element. Note that it is more reliable to time an R expression using the function system.time or proc.time. See their help files. The ifelse function Suppose we want to replace the positive elements in a vector by 1 and the negative elements by -1. When a normal ‘if- else’ construction is used, then each element must be used individually. tmp <- Sys.time() x <- rnorm(15000) for (i in 1:length(x)){ if(x[i] > 1){ x[i] <- 1 } else{ x[i] <- -1 } } Sys.time() - tmp Time difference of 0.3009999 secs

In this case the function ifelse is more efficient. tmp <- Sys.time() x <- rnorm(15000) x <- ifelse(x>1,1,-1) tmp - Sys.time() Time difference of 0.02999997 secs

The function ifelse has three arguments. The first is a test (a logical expression), the second is the value given to those elements of x which pass the test, and the third argument is the value given to those elements which fail the test.

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The cumsum function To calculate cumulative sums of vector elements use the function cumsum. For example: x <- 1:10 y <- cumsum(x) y [1] 1 3 6 10 15 21 28 36 45 55

The function cumsum also works on matrices in which case the cumulative sums are calculated per column. Use cumprod for cumulative products, cummin for cumulative minimums and cummax for cumulative maximums. Matrix multiplication In R a matrix-multiplication is performed by the operator %*%. This can sometimes be used to avoid explicit looping. An m by n matrix A can be multiplied by an n by k matrix B in the following manner: C <- A %*% B

So element C[i,j] of the matrix C is given by the formula: Ci,j =

X

Ai,k Bk,j

k

If we choose the elements of the matrices A and B ‘cleverly’ explicit for-loops could be avoided. For example, column-averages of a matrix. Suppose we want to calculate the average of each column of a matrix. Proceed as follows: A <- matrix(rnorm(1000),ncol=10) n <- dim(A)[1] mat.means <- t(A) %*% rep(1/n, n)

6.2 The apply and outer functions 6.2.1 the apply function This function is used to perform calculations on parts of arrays. Specifically, calculations on rows and columns of matrices, or on columns of a data frame. To calculate the means of all columns in a matrix, use the following syntax:

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M <- matrix(rnorm(10000),ncol=100) apply(M,1,mean)

The first argument of apply is the matrix, the second argument is either a 1 or a 2. If one chooses 1 then the mean of each column will be calculated, if one chooses 2 then the mean will be calculated for each row. The third argument is the name of a function that will be applied to the columns or rows. The function apply can also be used with a function that you have written yourself. Extra arguments to your function must now be passed trough the apply function. The following construction calculates the number of entries that is larger than a threshold d for each column in a matrix. tresh <- function(x,d){ sum(x>d) } M <- matrix(rnorm(10000),ncol=100) apply(M,1,tresh,0.6) [1] 24 26 24 26 31 26 30 27 28 29 [20] 28 25 28 30 25 28 32 23 24 27 [39] 37 36 26 23 23 28 26 28 30 25 [58] 30 37 28 22 27 20 30 24 29 21 [77] 18 27 28 33 33 25 21 35 25 33 [96] 30 27 28 21 31

26 33 23 26 27

23 29 30 26 28

33 25 20 31 20

23 26 34 26 35

27 20 29 18 23

23 31 32 26 31

27 28 34 34 25

31 29 30 29 29

22 31 29 20 20

6.2.2 the lapply and sapply functions These functions are suitable for performing calculations on the components of a list. Specifically, calculations on the columns of a data frame. If, for instance, you want to find out which columns of the data frame cars are of type numeric then proceed as follows: lapply(cars, is.numeric) $Price: [1] TRUE $Country: [1] FALSE $Reliability: [1] FALSE

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$Mileage: [1] TRUE ... ...

The function sapply can be used as well: sapply(car.test.frame, is.numeric) Price Country Reliability Mileage Type Weight Disp. HP T F F T F T T T

The function sapply can be considered as the ‘simplified’ version of lapply. The function lapply returns a list and sapply a vector (if possible). In both cases the first argument is a list (or data frame) , the second argument is the name of a function. Extra arguments that normally are passed to the function should be given as arguments of lapply or sapply. mysummary <- function(x){ if(is.numeric(x)) return(mean(x)) else return(NA) } sapply(car.test.frame,mysummary) Price Country Reliability Mileage Type Weight Disp. HP 12615.67 NA NA 24.58333 NA 2900.833 152.05 122.35

Some attention should be paid to the situation where the output of the function to be called in sapply is not constant. For instance, if the length of the output-vector depends on a certain calculation: myf <- function(x){ n<-as.integer(sum(x)) out <- 1:n out } testdf <- as.data.frame(matrix(runif(25),ncol=5)) sapply(testdf,myf) $X.1: [1] 1 2

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$X.2: [1] 1 0 $X.3: [1] 1 2 3 $X.4: [1] 1 2 $X.5: [1] 1

The result will then be an object with a list structure.

6.2.3 The tapply function This function is used to run another function on the cells of a so called ragged array. A ragged array is a pair of two vectors of the same size. One of them contains data and the other contains grouping information. The following data vector x en grouping vector y form an example of a ragged array. x <- rnorm(50) y <- as.factor(sample(c("A","B","C","D"), size=50, replace=T))

A cell of a ragged array are those data points from the data vector that have the same label in the grouping vector. The function tapply calculates a function on each cell of a ragged array. tapply(x, y, mean, trim = 0.3) A B C D -0.4492093 -0.1506878 0.4427229 -0.1265299

Combining lapply and tapply To calculate the mean per group in every column of a data frame, one can use sapply/lapply in combination with tapply. Suppose we want to calculate the mean per group of every column in the data frame cars, then we can use the following code:

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mymean <- function(x,y){ tapply(x,y,mean) } lapply(cars, mymean, cars$Country) $Price France Germany Japan Japan/USA 15930.000 14447.500 13938.053 10067.571 $Country France NA

Korea 7857.333

Mexico Sweden USA 8672.000 18450.000 12543.269

Germany NA

Japan Japan/USA NA NA

Korea NA

Mexico NA

Sweden NA

USA NA

$Reliability France Germany NA NA ...

Japan Japan/USA NA 4.857143

Korea NA

Mexico 4.000000

Sweden 3.000000

USA NA

6.2.4 The by function The by function applies a function on parts of a data.frame. Lets look at the cars data again, suppose we want to fit the linear regression model Price Weight for each type of car. First we write a small function that fits the model Price Weight for a data frame. myregr <- function(data) { lm(Price ~ Weight, data = data) }

This function is then passed to the by function outreg <- by(cars, cars$Type, FUN=myregr) outreg cars$Type: Compact Call: lm(formula = Price ~ Weight, data = data) Coefficients: (Intercept) 2254.765

Weight 3.757

------------------------------------------------------------

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cars$Type: Large Call: lm(formula = Price ~ Weight, data = data) Coefficients: (Intercept) 17881.2839 ... ...

Weight -0.5183

The output object outreg of the by function contains all the separate regressions, it is a so called ‘by’ object. Individual regression objects can be accessed by treating the ‘by’ object as a list outreg[[1]] Call: lm(formula = Price ~ Weight, data = data) Coefficients: (Intercept) 2254.765

Weight 3.757

6.2.5 The outer function The function outer performs an outer-product given two arrays (vectors). This can be especially useful for evaluating a function on a grid without explicit looping. The function has at least three input-arguments: two vectors x and y and the name of a function that needs two or more arguments for input. For every combination of the vector elements of x and y this function is evaluated. Some examples are given by the code below. x <- 1:3 y <- 1:3 z <- outer(x,y,FUN="-") z [,1] [,2] [,3] [1,] 0 -1 -2 [2,] 1 0 -1 [3,] 2 1 0 x <- c("A", "B", "C", "D") y <- 1:9

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CHAPTER 6. EFFICIENT . . . z <- outer(x, y, paste, sep = z [,1] [,2] [,3] [,4] [,5] [1,] "A1" "A2" "A3" "A4" "A5" [2,] "B1" "B2" "B3" "B4" "B5" [3,] "C1" "C2" "C3" "C4" "C5" [4,] "D1" "D2" "D3" "D4" "D5"

6.3. USING COMPILED CODE "") [,6] "A6" "B6" "C6" "D6"

[,7] "A7" "B7" "C7" "D7"

[,8] "A8" "B8" "C8" "D8"

[,9] "A9" "B9" "C9" "D9"

x <- seq(-4,4,l=50) y <- x myf <- function(x,y){ sin(x)+cos(y) } z <- outer(x,y, FUN = myf) persp(x,y,z, theta=45, phi=45, shade = 0.45)

x

y

z

Figure 6.1: A surface plot created with the function persp

6.3 Using Compiled code Sometimes the use of explicit for loops cannot be avoided. When these loops form a real bottleneck in computation time, you should consider implementing these loops in C (or Fortran) and link them to R. This feature is used a lot within the existing R functions

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already. In fact, the source code of R is available so you can see many examples. There are a couple of ways to link C or Fortran code to R. On Windows platforms the use of dynamic link libraries (dll’s) is probably the easiest solution. For a detailed description see for example, the R manual ‘Writing R Extensions’ or Chapter 6 and Appendix A of [4]. Reasons to use compiled code Compiled C or Fortran code is faster than interpreted R code. Loops and especially recursive functions run a lot faster and a lot more efficiently when they are programmed in C or Fortran. It is also possible that you already have some (tested) code at hand that performs a certain routine. Translating the entire C code to R can be cumbersome, so that it may pay off to organize the C code in such a way that it can be used within R.

6.3.1 The .C and .Fortran interfaces The .C and .Fortran interfaces are basic interfaces to C and Fortran. To call a C function that is loaded in R, use the function .C, giving it the name of the function (as a character string) and one argument for each argument of the C function. Note that if you pass a vector x to the C code, you also need to explicitly pass its length. In C it is not possible (like length(x) in R) to find out the length from only the vector x. .C("arsim", x = as.double(x), n = as.integer(length(x)))

We’ll define the C routine arsim in the examples section To return results to R, modify one or more input arguments of the C function. The value of the .C() function is a list with each component matching one argument to the C function. If you name these arguments, as we did in the preceding example, the return list has named components. Your R function can use the returned list for further computations or to construct its own return value, which generally omits those arguments, which are not altered by the C code. Thus, if we wanted to just use the returned value of x, we could call .C() as follows: .C("arsim", x = as.double(x),n = as.integer(length(x)))$x.

All arguments of the C routine called via .C() must be pointers. All such routines should be void functions; if the routine does return a value, it could cause R to crash. R has many classes that are not immediately representable in C. To simplify the interface between R and C, the types of data that R can pass to C code are restricted to the following classes:

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• single, integer • double, complex • logical, character • raw, list The following table shows the correspondence between R data types and C types. R data type logical integer double complex character raw

C data type long* long* double* Rcomplex* char** char*

6.3.2 The .Call and .External interfaces The .Call and .External interfaces are powerful interfaces that allow you to manipulate R objects from C code and evaluate R expressions from within your C code. It is more efficient than the standard .C interface, but because it allows you to work directly with R objects, without the usual R protection mechanisms, you must be careful when programming with it to avoid memory faults and corrupted data. The .Call interface provides you with several capabilities that the standard .C interface lacks, including the following • the ability to create variable-length output variables, as opposed to the pre-allocated objects the .C interface expects to write to. • a simpler mechanism for evaluating R expressions within C. • the ability to establish direct correspondence between C pointers and R objects.

6.4 Some Compiled Code examples 6.4.1 The arsim example To apply a first-order recursive filter to a data vector one could write the following R function:

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arsimR <- function(x,phi){ n <- length(x) if(n > 1){ for(i in 2:n){ x[i] <- phi*x[i-1]+x[i] } } x } tmp <- Sys.time() out1 <- arsimR(rnorm(10000), phi = 0.75) Sys.time() - tmp Time difference of 0.25 secs

We cannot avoid explicit looping in this case, the R function could be slow for large vectors. We implement the function in C and link it to R. In C we can program the arsim function and compile it to a dll as follows. First, create a text file arsim.c and insert the following code: void arsim(double *x, long*n, double *phi) { long i; for(i=1; i<*n; i++) x[i] = *phi * x[i-1] + x[i]; }

Then, create a module definition file arsim.def and insert the following text: LIBRARY arsim EXPORTS arsim

This module definition file tells which functions are to be exported by the dll. Now compile the two files to a dll. There are many (free and commercial) compilers that can create a dll: • The GNU compiler collection (free) (http://www.mingw.org) • lcc (free) (http://www.cs.virginia.edu/ lcc-win32) • Borland (compiler is free, the IDE is commercial) • Microsoft Visual studio (commercial)

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Lets use lcc to create the dll, open a DOS box and type in the following lcc arsim.c lcclink -dll -nounderscores arsim.obj arsim.def

The compiler created the file arsim.dll that can now be linked to R. In R type the following code: mydll = "C:\\DLLLocation\\arsim.dll" dyn.load(mydll) is.loaded("arsim") TRUE

The dll is now linked to R and we can use the .C interface function to call the arsim C function. For convenience, we write a wrapper function arsimC that calls the .C function arsimC <- function(x, phi) { # only return the first component of the list # because the C function only modifies x .C("arsim", as.numeric(x), length(x), as.numeric(phi) )[[1]] } tmp <- Sys.time() arsimC(rnorm(10000), phi = 0.75) Sys.time() - tmp Time difference of 0.00999999 secs

As we can see the C code is much faster than the R code, the following graph also shows that.

6.4.2 Using #include There are many useful functions in R that can be called from your C code. You need to insert #include in your code and tell your compiler where to find this file. Normally this file is located in the directory C:\Program Files\R\R-2.5.0\include. In addition to that, you need to link your code with functionality that is in R.dll. The way to do this depends on your compiler. In Microsoft Visual C proceed as follows:

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100

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0e+00

1e+06

2e+06

3e+06

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Figure 6.2: Calculation times of arsimR (solid line) and arsimC (dashed line) for increasing vectors • Open a dos box and go to the bin directory of the R installation • cd C:\Program Files\R\R-2.5.0\bin (modify as required) • pexports R.dll > R.exp • lib /def:R.exp /out:Rdll.lib Here lib is the library command that comes with Visual C++. You can download the free Visual C++ express edition from the Microsoft site. The pexports tool is part of the MinGW-utils package. Now the file Rdll.lib is created and when you create your dll the compiler needs to link this lib file as well. See [5] and the R manual ‘Writing R extensions’ for more information. The type of R functions that can be called • printing and error handling • numerical and mathematical • memory allocation As an example we slightly modify the above arsim.c file #include void arsim(double *x, long *n, double *phi)

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{ long i; Rprintf("Before the loop \n"); if( *n > 100 ) MESSAGE "vector is larger than 100" WARN for (i=1; i<*n; i++) x[i] = *phi * x[i-1] + x[i] ; Rprintf("After the loop \n"); }

Note that if you have loaded the dll with dyn.load, you must not forget to unload it with the function dyn.unload if you want to build a newer version. R has locked the dll and the compiler is not able to build a new version. After a successful build we can run arsimC again, which now gives some extra output. out2 <- arsimC(rnorm(500), phi = 0.75) Before the loop After the loop Warning message: vector is larger than 100

6.4.3 Evaluating R expressions in C A handy thing to do in C is evaluating R expressions or R functions. This enables you for example, to write a numerical optimization routine in C and pass an R function to that routine. Within the C routine, calls to the R function can then be made, this is just the way, or example, the R function optim works. To evaluate R expressions or R functions in C, it is better to use the .Call or .External interfaces, the eval function is the function in C that can be used to evaluate R expressions. SEXP eval(SEXP expr, SEXP rho);

which is the equivalent of the interpreted R code eval(expr, envir = rho). See section 5.9 of the R manual ‘Writing R Extensions’. The internal R pointer type SEXP is used to pass functions, expressions, environments and other language elements from R to C. It is defined in the file ‘Rinternals.h’.

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A small example We will give a small example first that does almost nothing, but shows some important concepts. The example takes an R function and evaluates this R function in C with input argument xinput. First the necessary C code: #include #include SEXP EvalRExpr( SEXP fn, SEXP xinput, SEXP rho) { SEXP ans, R_fcall; int n = length(xinput); PROTECT(R_fcall = lang2(fn, R_NilValue)); PROTECT(ans = allocVector(VECSXP, n)); SETCADR(R_fcall, xinput); ans = eval(R_fcall, rho); Rprintf("Length of xinput %d \n", n); UNPROTECT(2); return ans; }

When this is build into a dll that exports the function EvalRExpr, then we can load the dll in R and use .Call to run the function: z <- c(121, 144, 225) myf <- function(x) { 2* sqrt(x) } .Call("EvalRExpr", myf, as.double(z), new.env() ) Length of xinput 3 [1] 22 24 30

First, in the C code the R objects ans and R fcall of type SEXP are defined. To protect the ans object from the R garbage collection mechanism it is created with the PROTECT

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macro. Enough memory is allocated with allocVector(VECSXP, n), in our example we will return a vector of the same length as the input vector. The R fcall object is created with the function lang2, which creates an executable pair list and together with a call to SETCADR we can then call the function eval which will evaluate our R function fn. A call to PROTECT must always be accompanied with a call to UNPROTECT, in this example we had two calls to PROTECT so we call UNPROTECT(2) before we exit the C code. A numerical integration example In R the function integrate can calculate the integral Z

b

f (x)dx a

for a one dimensional function f , using a numerical integration algorithm. integrate(sin,0,pi) 2 with absolute error < 2.2e-14

As an illustration we create our own version using existing C code. Our version will also take a function name and the values a and b as input arguments. The following steps are done: Adding the interface to R function The C code (from numerical recipes) implements the Romberg adaptive method, it consists of four functions: • The function qromb, implements the Romberg method. • The functions polint and trapzd, these are auxiliary functions used by qromb. • The function func, this is the function that is going to be integrated. In addition to these four C functions we add a C function Integrate that will act as the interface to R. The dll that we will create exports this function. SEXP Integrate( SEXP fn, SEXP a, SEXP b, SEXP rho) { SEXP ans; double mys; mys = qromb(REAL(a)[0], REAL(b)[0], fn, rho); PROTECT(ans = allocVector(REALSXP, 1));

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REAL(ans)[0] = mys; UNPROTECT(1); return ans; }

The lower bound a and the upperbound b are of type SEXP and are passed from R to the C code, they are converted to double and passed to the qromb function. This function returns the result in the double variable mys, which we transform to a variable of type SEXP so that it can be passed to R. The only modification to the existing C code qromb, is the addition of two input parameters fn and rho which will be needed when we want to evaluate the R function that is given by fn. In fact, the function qromb calls polint and trapzd that will call the function fn, so these functions also need to be given fn and rho. Modifying the function func Normally, when you want to use the function qromb in a stand alone C program then the function to integrate is programmed in the function func. Now this function needs to be adjusted in such a way that it evaluates the function fn that you have given from R. double func(const double x, SEXP fn, SEXP rho) { SEXP R_fcall, fn_out, x_input; PROTECT(R_fcall = lang2(fn, R_NilValue)); PROTECT(x_input = allocVector(REALSXP, 1)); PROTECT(fn_out = allocVector(VECSXP, 1)); REAL(x_input)[0] = x; SETCADR(R_fcall, x_input); fn_out = eval(R_fcall,rho); UNPROTECT(3); return REAL(fn_out)[0]; }

The same constructions are used as in the previous example. Evaluating the R functions results in a variable of type SEXP, this is then converted to a double and returned by func. When the dll is compiled we can link it to R and run the function. mydll = "C:\\Test\\Release\\Integrate.dll" dyn.load(mydll) myf <- function(x) {

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x*sin(x) } .Call("Integrate", myf, as.double(0), as.double(2), new.env() ) [1] 1.741591

Ofcourse you could have used the R function integrate, as a comparison: integrate(myf, 0, 2) 1.741591 with absolute error < 1.9e-14

it gives the same result!

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7 Graphics 7.1 Introduction One of the strengths of R above SAS or SPSS is its graphical system, there are numerous functions. You can create ‘standard’ graphs, use the R syntax to modify existing graphs or create completely new graphs. A good overview of the different aspects of creating graphs in R can be found in [6]. In this chapter we will first discuss the graphical functions that can be found in the base R system and the lattice package. There are more R packages that contain graphical functions, one very nice package is ggplot2, http://had.co.nz/ggplot2. We will give some examples of ggplot2 in the last section of this chapter. The graphical functions in the base R system, can be divided into two groups: High level plot functions These functions produce ‘complete’ graphics and will erase existing plots if not specified otherwise. Low level plot functions These functions are used to add graphical objects like lines, points and texts to existing plots. The most elementary plot function is plot. In its simplest form it creates a scatterplot of two input vectors. x <- rnorm(50) y <- rnorm(50) plot(x,y)

To add titles to the existing plot use the low-level function title. title("Figure 1")

Use the option type="l" (l as in line) in the plot function to connect consecutive points. This option is useful to plot mathematical functions. For example:

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Figure 1 2









1

● ●



● ●





0

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y

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Figure 7.1: A scatterplot with a title u <- seq(0, 4*pi, by=0.05) v <- sin(u) plot(u,v, type="l", xlab="x axis", ylab="sin") title("figure 2")

In case of drawing functions or expressions, the function curve can be handy, it takes some work away, the above code can be replaced by the following call to produce the same graph. curve(sin(x), 0,4*pi)

7.2 More plot functions In this section we just mention some useful plot functions, we refer to the help files of the corresponding functions for detailed information.

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0.0 −1.0

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sin

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figure 2

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Figure 7.2: Line plot with title, can be created with type="l" or the curve function.

7.2.1 The plot function The plot function is very versatile function. As we will see in in section 9.1 about object oriented programming, the plot function is a so called a generic function. Depending on the class of the input object the function will call a specific plot method. Some examples: • plot(xf), creates a bar plot if xf is a vector of data type factor. • plot(xf, y), creates box-and-whisker plots of the numeric data in y for each level of xf. • plot(x.df), all columns of the data frame x.df are plotted against each other. • plot(myts), creates a time series plot if myts is a ts (time series) object. • plot(xdate, yval), if xdate is a ‘Date’ object R will plot yval with a ‘suitable’ x-axis. • plot(xpos, y), creates a scatterplot where xpos is a POSIXct object and y is a numeric vector. • plot(f, low, up), creates a graph of the function f between low and up. The code below shows some examples of the different uses of the function plot.

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1970

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Figure 7.3: Different uses of the function plot ## set a 2 by 2 layout par(mfrow=c(2,2)) plot(xf, col="blue") plot(xf,rnorm(500), col="red") plot(sin, -3,3) plot(myts)

7.2.2 Distribution plots R has a variety of plot functions to display the distribution of a data vector. Suppose the vector x is numeric data vector, for example: x <- rnorm(1000)

Then the following function calls can be used to analyze the distribution of x graphically: • hist(x), creates a histogram. • qqnorm(x), creates a quantile-quantile plot with normal quantiles on the x-axis. • qqplot(x,y), creates a qq-plot of x against y.

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• boxplot(x), creates a box-and-whisker plot of x. The above functions can take more arguments for fine tuning the graph, see the corresponding help files. The code below creates an example of each graph in the above list x <- rnorm(100) y <- rt(100,df=3) par(mfrow=c(2,2)) hist(x, col=2) qqnorm(x) qqplot(x, y) boxplot(x, col="green")

Normal Q−Q Plot 2 1 0 −2

Sample Quantiles

15 10 5 −3

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−2

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Figure 7.4: Example distribution plot in R If you have a factor variable x, you can use the functions pie or barplot in combination with the table function to get a graphical display of the distribution of the levels of x. Lets look at the ‘cars’ data it has the factor columns ‘Country’ and ‘Type’. pie(table(cars$Country)) barplot(table(cars$Type))

The first argument of barplot can also be a matrix, in that case either stacked or grouped bar plots are created. This will depend on the logical argument beside.

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8

barplot( table( cars$Country, cars$Type ), beside = T, legend.text = T )

0

2

4

6

France Germany Japan Japan/USA Korea Mexico Sweden USA

Compact

Large

Medium

Small

Sporty

Van

Figure 7.5: Example barplot where the first argument is a matrix

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7.2.3 Two or more variables When you have two or more variables (in a data frame) you can use the following functions to display their relationship. • pairs(mydf), when mydf is a data frame then each column in mydf is is plotted against each other, the same as plot(mydf). • symbols, creates a scatterplot where the symbols can vary in size. • dotchart, creates a dot plot that can be grouped by levels of a factor. • contour, image, filled.contour create contour and image plots • persp, creates surface plots. In addition, multi panel graphs (Trellis graphs) described in section 7.4 can also be used to visualize multi dimensional data. The code below demonstrate some of the above functions. ## define some data x <- y <- seq(-4*pi, 4*pi, len=27) r <- sqrt(outer(x^2, y^2, "+")) z <- cos(r^2)*exp(-r/6) ## set a 2 by 2 layout par(mfrow=c(2,2)) image( z, axes = FALSE, main = "Math can be beautiful ...", xlab = expression(cos(r^2) * e^{-r/6}) ) dotchart( t(VADeaths), xlim = c(0,100), cex = 0.6, main = "Death Rates in Virginia") ## plots ’thermometers’ where a proportion of the ## thermometer is filled based on Ozone value. symbols( airquality$Temp, airquality$Wind, thermometers = cbind( 0.07, 0.3, airquality$Ozone / max(airquality$Ozone, na.rm=TRUE) ),

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inches = 0.15, ) myf <- function(x,y) { sin(x)+cos(y) } x <- y <- seq(0,2*pi, len=25) z <- outer(x, y, myf) persp(x,y,z, theta=45, phi=45, shade=0.2)

Death Rates in Virginia

Math can be beautiful ... 50−54 Urban Female Urban Male Rural Female Rural Male 55−59 Urban Female Urban Male Rural Female Rural Male 60−64 Urban Female Urban Male Rural Female Rural Male 65−69 Urban Female Urban Male Rural Female Rural Male 70−74 Urban Female Urban Male Rural Female Rural Male

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Figure 7.6: Example graphs of multi dimensional data sets

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7.2.4 Graphical Devices Before a graph can be made a so-called graphical device has to be opened. In most cases this will be a window on screen, but it may also be an eps, or pdf file. Type ?Devices for an overview of all available devices. The devices in R are: • windows The graphics driver for Windows (on screen, to printer and to Windows metafile). • postscript Writes PostScript graphics commands to a file • pdf Write PDF graphics commands to a file • pictex Writes LaTeX/PicTeX graphics commands to a file • png PNG bitmap device • jpeg JPEG bitmap device • bmp BMP bitmap device • xfig Device for XFIG graphics file format • bitmap bitmap pseudo-device via GhostScript (if available). When a plot command is given without opening a graphical device first, then a default device is opened. Use the command options("devices") to see what the default device is, usually it is the windows device. We could, however, also open a device ourselves first. The advantages of this are • We can open the device without using the default values. • When running several high level plot commands without explicitly opening a device only the last command will result in a visible graph, since high level plot commands overwrite existing plots. This can be prevented by opening separate devices for separate plots. windows(width=8) plot(rnorm(100)) windows(width=9) hist(rnorm(100)) windows(width=10) qqnorm(rnorm(100))

Now three devices of different size are open. A list of all open devices can be obtained by using the function dev.list:

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dev.list() windows windows windows 2 3 4

When more than one device is open, there is one active device and one or more inactive devices. To find out which device is active the function dev.cur can be used. dev.cur() windows 4

Low-level plot commands are placed on the active device. In the above example the command title("qqplot") will result in a title on the qqnorm graph. Another device can be made active by using the function dev.set. dev.set(which=2) title("Scatterplot")

A device can be closed using the function dev.off. The active device is then closed. For example, to export an R graph to a jpeg file so that it can be used in a website, use the jpeg device: jpeg("C:\\Test.jpg") plot(rnorm(100)) dev.off()

7.3 Modifying a graph 7.3.1 Graphical parameters To change the layout of a plot or to change a certain aspect of a plot such as the line type or symbol type, you will need to change certain graphical parameters. We have seen some in the previous section. The graphical functions in R accept graphical parameters as extra arguments. These graphical parameters are usually three or four letter abbreviations (like col, cex or mai). The following use of the plot function, plot(x,y, xlim=c(-3.3))

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will set the minimum and maximum values of the x-axis. It is also possible to use the function par to set graphical parameters. Some graphical parameters can only be set with this function. A call to the function par has the following form: par(gp1 = value1, gp2 = value2)

In the above code the graphical parameter gp1 is set to value1, graphical parameter gp2 is set to value2 and so on. Note that some graphical parameters are read only and cannot be changed. Run the function par with no arguments to get a complete listing of the graphical parameters and their current values. par() $xlog [1] FALSE $ylog [1] FALSE $adj [1] 0.5 $ann [1] TRUE $ask [1] FALSE $bg [1] "transparent" ... etc.

We will discuss some useful graphical parameters. See the help file of par for a more detailed description and a list of all the graphical parameters. Once you set a graphical parameter with the par function, that graphical parameter will keep its value until you: • Set the graphical parameter to another value with the par function. • Close the graph. R will use the default settings when you create a new plot. When you specify a graphical parameter as an extra parameter to a graphical function, the current value of the graphical parameter will not be changed. Some example code:

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## define some data x <- rnorm(10) y <- rnorm(10) z <- rnorm(10) ## set plotting color to red par(col="red") plot(x,y) ## draw extra blue points points(x,z, col="blue") ## draw red points again points(y,z)

The Plot and figure regions, the margins A graph consists of three regions. A ‘plot region’ surrounded by a ‘figure regions’ that is in turn surrounded by four outer margins. The top, left, bottom and right margins. See figure 7.7. Usually the high level plot functions create points and lines in the plot region. Outer margin 3

Plot region

Outer margin 4

Outer margin 2

Figure region

Outer margin 1

Figure 7.7: The different regions of a plot The outer margins can be set with the oma parameter, the four default values are set to zero. The margins surrounding the plot region can be set with the mar parameter. Experiment with the mar and oma parameters to see the effects.

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## Default values par(c("mar", "oma")) $mar [1] 5.1 4.1 4.1 2.1 $oma [1] 0 0 0 0 ## set to different values par(oma=c(1, 1, 1, 1)) par(mar=c(2.5, 2.1, 2.1, 1)) plot(rnorm(100))

Multiple plots on one page Use the parameter mfrow or mfcol to create multiple graphs on one layout. Both parameters are set as follows: par(mfrow=c(r,k)) par(mfcol=c(r,k))

where r is the number of rows and k the number of columns. The graphical parameter mfrow fills the layout by row and mfcol fills the layout by column. When the mfrow parameter is set, an empty graph window will appear and with each high-level plot command a part of the graph layout is filled. We have seen an example in the previous section, see figure 7.6. A more flexible alternative to set the layout of a plotting window is to use the function layout. An example, three plots are created on one page, the first plot covers the upper half of the window. The second and third plot share the lower half of the window. ## first argument is a matrix with integers ## specifying the next n graphs nf = layout( rbind( c(1,1), c(2,3) ) ) ## If you are not sure how layout has divided the window ## use layout.show to display the window splits ## layout.show(nf) plot(rnorm(100),type="l") hist(rnorm(100)) qqnorm(runif(100))

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Figure 7.8: The plotting area of this graph is divided with the layout function. The matrix argument in the layout function can contain 0’s (zero’s), leaving a certain sub plot empty. For example: nf = layout( rbind( c(1,1), c(0,2) ) )

Other settings The following list shows some more parameters, these are usually set as an argument of the plotting routine. For example, plot(x,y, col=2). • lwd, the line width of lines in a plot, a positive number default lwd=1. • lty, the line type of lines in a plot, this can be a number or a character. For example lty="dashed".

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• col, the color of the plot, this can be a number or a character. For example col = "red". • font, an integer specifying which font to use for text on plots. • pch, an integer or character that specifies the plotting symbols, in scatterplots for example. • xlab, ylab, character strings that specify the labels of the x and y axis. Usually given direct with the high-level plotting functions like plot or hist. • cex, character expansion. A numerical value that gives the amount to scale the plotting symbols and texts. The default is 1. Some of the graphical parameters may be set as vectors so that each point, text or symbol could have its own graphical parameter. This is another way to display an additional dimension. Lets look at a plot with different symbols, for the cars data set we can plot the ‘Price’ and ‘Mileage’ variables in a scatterplot and have different symbols for the different ‘Types’ of cars. Ncars = dim(cars)[1] plot( cars$Price, cars$Mileage, pch = as.integer(cars$Type) ) legend(20000,37, legend = levels(cars$Type), cex=1.25, pch=1:6 )

The color palette The graphical parameter col can be a vector. This can be used to create a scatterplot of ‘Price’ and ‘Mileage’ where each point has a different color depending on the ‘Weight’ value of the car. To do this, we first need to change the color palette in R. The color palette specifies which colors corresponds with the numbers 1,2,3,... in the specification col = number. The current palette can be printed with the function palette. palette() [1] "black" "red" [6] "magenta" "yellow"

"green3" "gray"

"blue"

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This means plot(rnorm(100), col=2) will create a scatterplot with red points. The function can also be used to change the palette. Together with a few auxiliary functions (heat.colors, terrain.colors, gray), it is easy to create a palette of colors, say from dark to light red. palette(heat.colors(Ncars)) palette() [1] "red" "#FF2400" "#FF4900" "#FF6D00" "#FF9200" "#FFB600" [7] "#FFDB00" "yellow" "#FFFF40" "#FFFFBF" ...

So in the color palette, col=1 represents red, col=2 a slightly lighter red and so on. Then in the plot function we specify col=order(cars$Weight), the largest value has order number Ncars. The following code uses several (plot) functions to create a colored scatterplot and a color legend. ## split the screen in two, the larger left part will contain ## the scatter plot the right side contains a color legend layout(matrix(c(1, 2), nc = 2), widths = c(4, 1)) ## create the scatterplot with different colors plot( cars$Price, cars$Mileage, pch = 16, cex = 1.5, col = order(cars$Weight) ) ## do some calculations for the color legend, determine ## minimum and maximum weight values. zlim = range(cars$Weight, finite = TRUE) ## lets use 20 color values in the color legend levels = pretty(zlim, 20) ## start the second plot that is the color legend plot.new() plot.window( xlim = c(0, 1), ylim = range(levels), xaxs = "i", yaxs = "i" ) ## use the function rect to draw multiple colored rectangles rect( 0, levels[-length(levels)], 1, levels[-1], col = terrain.colors(length(levels) - 1) ) ## draw an axis on the right-hand side of the legend axis(4)

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Figure 7.9: Examples of different symbols and colors in plots To set the color palette back to default use palette("default").

7.3.2 Some handy low-level functions Once you have created a plot you may want to add something to it. This can be done with low-level plot functions. Adding lines The function lines and abline are used to add lines on an existing plot. The function lines connects points given by the input vector. The function abline draws straight lines with a certain slope and intercept. plot(c(-2,2),c(-2,2)) lines(c(0,2), c(0,2), col="red") abline(a=1,b=2, lty=2) abline(v=1, lty=3, col="blue", lwd=3)

The functions arrows and segments are used to draw arrows and line segments.

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## three arrows starting from the same point ## but all pointing to a different direction arrows( c(0,0,0), c(1,1,1), c(0,0.5,1), c(1.2,1.5,1.7), length = 0.1 )

Adding points and symbols The function points is used to add extra points and symbols to an existing graph. The following code adds some extra points to the previous graph. points(rnorm(4), rnorm(4), pch=3, col="blue") points(rnorm(4), rnorm(4), pch=4, cex=3, lwd=2) points(rnorm(4), rnorm(4), pch="K", col="green")

Adding titles and text The functions title, legend, mtext and text can be used to add text to an existing plot. title(main="My title", sub="My subtitle") text(0, 0, "some text") text(1, 1, "Business & Decision", srt=45)

The first two arguments of text can be vectors specifying x,y coordinates, then the third argument must also be a vector. This character vector must have the same length and contains the texts that will be printed at the coordinates. The function mtext is used to place text in one of the four margins of the plot. mtext("Text in the margin", side=4)

In R you can place ordinary text on plots, but also special symbols, Greek characters and mathematical formulas on the graph. You must use an R expression inside the title, legend, mtext or text function. This expression is interpreted as a mathematical expression, similar to the rules in LaTex.

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text(-1.5, -1.5, expression( paste( frac(1, sigma*sqrt(2*pi)), " ", plain(e)^{frac(-(x-mu)^2, 2*sigma^2)} ) ), cex = 1.2 )

See for more information the help of the plotmath function.

My title ●

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Figure 7.10: The graph that results from the previous low-level plot functions.

7.3.3 Controlling the axes When you create a graph, the axes and the labels of the axes are drawn automatically with default settings. To change those settings you specify the graphical parameters

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that control the axis, or use the axis function. One approach would be to first create the plot without the axis with the axes = F argument, and then draw the axis using the low-level axis function. x <- rnorm(100) y <- rnorm(100) ## do not draw the axes automatically plot(x,y, axes=F) ## draw them manually axis(side=1) axis(side=2)

The side argument represents the side of the plot for the axis (1 for bottom, 2 for left, 3 for top, and 4 for right). Use the pos argument to specify the x or y position of the axis. x <- rnorm(100) y <- rnorm(100) plot(x,y, axes=F) axis(side=1, pos=0) axis(side=2, pos=0)

The location of the tick marks and the labels at the tick marks can be specified with the arguments at and labels respectively. ## Placing tick marks at specified locations x <- rnorm(100) y <- rnorm(100) plot(x,y, axes=F) xtickplaces <- seq(-2,2,l=8) ytickplaces <- seq(-2,2,l=6) axis(side=1, at=xtickplaces) axis(side=2, at=ytickplaces) ## Placing labels at the tick marks x <- 1:20 y <- rnorm(20) plot(x,y, axes=F) xtickplaces <- 1:20 ytickplaces <- seq(-2,2,l=6) xlabels <- paste("day",1:20,sep=" ") axis(side=1, at=xtickplaces, labels=xlabels) axis(side=2, at=ytickplaces)

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Notice that R does not plot all the axis labels. R has a way of detecting overlap, which then prevents plotting all the labels. If you want to see all the labels you can adjust the character size, use the cex.axis parameter. x <- 1:20 y <- rnorm(20) plot(x,y, axes=F) xtickplaces <- 1:20 ytickplaces <- seq(-2,2,l=6) xlabels <- paste("day", 1:20,sep=" ") axis(side=1, at=xtickplaces, labels=xlabels, cex.axis=0.5) axis(side=2, at=ytickplaces)

Another useful parameter that you can use is tck. It specifies the length of tick marks as a fraction of the smaller of the width or height of the plotting region. In the extreme casetck = 1, grid lines are drawn. To draw logarithmic x or y axis use log="x" or log="y", if both axis need to be logarithmic use log="xy". ## adding an extra axis with grid lines, this ## is on top of the existing axis. axis(side=1,at= c(5,10,15,20), labels=rep("",5), tck=1, lty=2) ## Example of logarithmic axes x <- runif(100,1,100000) y <- runif(100,1,100000) plot(x,y, log="xy", col="grey")

7.4 Trellis Graphics 7.4.1 Introduction Trellis graphics add a new dimension to traditional plotting routines. They are extremely useful to visualize multi-dimensional data. You create a trellis graphic by using one of the trellis display functions, these are in the package lattice. Visualization of multidimensional data can be achieved through a multi panel layout, where each panel displays a particular subset of the data. The following table (Table 7.1) displays some of the trellis display functions in R in the lattice package.

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Figure 7.11: Graphs resulting from previous code examples of customizing axes. A call to a trellis display function differs from a call to a normal plot routine. It resembles a call to one of the statistical modeling functions such as lm or glm. The call has the following form: TrellisFunction(formula, data = data.frame, other graphical parameters)

Depending on the specific trellis display function, the formula may not have a ‘response’ variable. To create a scatterplot of the Price variable against the Weight variable and a histogram of the Weight variable in the car.test.frame data frame, proceed as follows: cars <- read.csv("cars.csv", row.names=1) library(lattice) xyplot(Price ~ Weight, data=cars) histogram( ~ Weight, data=cars)

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CHAPTER 7. GRAPHICS Trellis function barchart bwplot densityplot dotplot histogram qq xyplot wireframe levelplot stripplot cloud splom

7.4. TRELLIS GRAPHICS description Bar charts plot Box and whisker plot Kernel density plots, smoothed density estimate Plot of labeled data Histogram plot Quantile-quantile plot Scatterplot 3D surface plot Contour plot 1 dimensional scatterplot 3D scatterplot Scatterplot matrices

Table 7.1: Trellis display functions

7.4.2 Multi panel graphs To create a multi panel layout use the ‘conditioning’ operator | in the formula. To see the relationship between Price and Weight for each Type of car, use the following construction. xyplot(Price ~ Weight | Type, data=cars)

Use the * operator to specify more than one conditioning variable. The following example demonstrates two conditioning variables. First, create some example data, one numeric variable and two grouping variables with three levels. x <- rnorm(1000) y <- sample(letters[1:3], size=1000, rep=T) z <- sample(letters[11:13], size=1000, rep=T) exdata <- data.frame(x,y,z)

Next, a histogram plot is created for the variable x conditioned on the variables y and z. histogram(~x|y*z, data=exdata)

For each combination of the levels of y and z, a histogram plot is created. The order can be changed: histogram(~x|z*y, data=exdata)

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2000 2500 3000 3500

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Figure 7.12: Trellis plot Price versus Weight for different types The above examples were based on conditioning variables of type factor. In this case R will create a separate panel for each level of a factor variable or for each level combination of multiple factor variables. To create Trellis graphics based on numeric conditioning variables you can use the functions equal.count or shingle to create conditioning intervals of numeric variables. These intervals can then be used in a Trellis display function. Lets look at our cars example data frame that contains information on 60 different cars. Suppose we want to create a histogram of the variable ‘Mileage’ conditioned on the variable ‘Weight’. We then proceed as follows: weight.int <- equal.count( cars$Weight, number=4, overlap=0 )

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Figure 7.13: A trellis plot with two conditioning variables This creates the conditioning intervals. The variable ‘Weight’ is divided into four equal intervals without overlap. weight.int Data: [1] 2560 [16] 3310 [31] 3110 [46] 3480

2345 2695 2920 3200

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Intervals: min max count 1 1842.5 2562.5 15 2 2572.5 2887.5 16 3 2882.5 3222.5 16 4 3262.5 3857.5 15

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Overlap beetween adjacent intervals: [1] 0 2 0

To draw the histograms use the following R code: histogram( ~Mileage | weight.int ,data=cars)

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Figure 7.14: Histogram of mileage for different weight classes

7.4.3 Trellis panel functions Trellis graphs are constructed per panel. A general trellis display function calls a panel function that does the actual work. The name of the default panel function that is called by the general trellis display function is panel.name, where name is the name of the general trellis display function. So, if we call the trellis display function xyplot then this will in turn call the function panel.xyplot. If you look at the code of the general trellis display function xyplot you won’t see a lot. The corresponding panel function panel function panel.xyplot does all the work. xyplot function (x, data, ...)

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UseMethod("xyplot") panel.xyplot function(...) a lot R of code ...

A powerful feature of trellis graphs is that you can write your own panel function and pass this function on to the general trellis display function. This is done using the argument panel of the trellis display function. Suppose we want to plot ‘Price’ against ‘Mileage’ conditioned on the ‘Type’ variable and suppose that, in addition, we want a separate symbol for the highest price. We create our own panel function: panel.maxsymbool <- function(x,y){ biggest <- y == max(y) panel.points(x[!biggest],y[!biggest]) panel.points(x[biggest], y[biggest],pch="M") }

The above function first finds out what the maximum y value is, it then plots the points without the maximum y value and then plots the maximum y value using a different symbol. Note that we use the function panel.points instead of the normal low-level points function. The normal low-level functions can not be used inside a function that is going to be used as panel function. This is because lattice panel functions need to use grid graphics. So use the panel versions: panel.points, panel.text, panel.abline, panel.lines and panel.segments. Once a panel function is defined you should pass it to the trellis display function xyplot( Price ~ Mileage | Type, data=cars, panel = panel.maxsymbol)

The following example fits a least squares line through the points of each panel. Additional graphical parameters can also be passed on. The next example enables the user to specify the type of line using the graphical parameter lty. panel.lsline <- function(x,y, ...){ coef <- lsfit(x,y)$coef panel.points(x,y) panel.abline(coef[1],coef[2], ...) } xyplot(Price~Mileage|Type,data=cars, panel=panel.lsline, lty=2)

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Figure 7.15: Trellis plot with modified panel function

7.4.4 Conditioning plots The function coplot can be a nice alternative to the trellis function xyplot. It can also create multi panel layouts where each panel represents a part of the data. The function has many arguments that can be set. A few examples are given below. ## no need to specify intervals, only the number of intervals coplot(lat~long | depth, number=4, data=quakes, col="blue") ## two conditioning variables coplot(lat~long | depth*mag, number=c(4,5), data=quakes) ## conditioning on a factor and numeric variable coplot(Price~Mileage | Type*Weight, number=3 ,data=cars)

The function coplot can also use a customized panel function, the points function is used as default panel function. The following example uses the function panel.smooth as panel function. coplot(Price ~ Mileage | Weight, number=4 , panel = panel.smooth,

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Figure 7.16: Trellis plot adding a least squares line in each panel data=cars, col= "dark green", pch=2 )

7.5 The ggplot2 package The ggplot2 package (see [7]) is a collection of plotting routines based on the grammer of graphics, see [8]. The functions in ggplot2 can take away some of the anoying extra code that makes plotting a hassle (like drawing legends). At the same time ggplot2 provides a powerful model of graphics that makes it easy to produce complex multilayered graphics. This section only gives a brief introduction, for a thorough description of the possibilities see http://had.co.nz/ggplot2.

7.5.1 The qplot function The function qplot (quick plot) in ggplot2 can be used to create complex plots with little coding effort. The following code displays some examples.

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185

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165

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Given : mag

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175

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lat

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300

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−35

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165

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100

185

long

Figure 7.17: A coplot with two conditioning variables library(ggplot2) x <- runif(1000) y <- 2*x^2 + rnorm(1000,0,0.3) z <- 5*log(x) + rnorm(1000,0,0.3) testdata <- data.frame(x, y, z) ## a simple scatter plot qplot(x, y, data = testdata) ## transformations qplot(x, log(y), data = testdata) ## changing the size of the symbols qplot(x, y, data = testdata, size=z)

The function qplot can also create other types of plots. This can be done by using the argument geom, which stands for geometric object. Such an object not only describes the type of plot but also a corresponding statistical calculation. For example, a smoothing line calculated according to some smoothing algorthm. The default value for geom is point, a standard scatter plot. To draw a line graph between the points use: qplot(x,y, data = testdata, geom=c("line"))

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Given : Weight 2000

25

30

3000

3500

35

10000

20000

Price

10000

20000

20

2500

20

25

30

35

Mileage

Figure 7.18: A coplot with a smoothing line The geom argument can be a vector of names, this will result in one plot with multiple graphs on top of each other. The following code first plots a scatter plot with a loess smoothing line, and then a scatter plot with a regression line, using lm. qplot(x,y, data = testdata, geom=c("point", "smooth"), span = 0.2) qplot(x,y, data = testdata, geom=c("point", "smooth"), method = "lm")

7.5.2 Facetting Facetting in ggplot2 is the equivalent of trellis plots and allows you to display certain sub sets of your data in different facets. x <- rnorm(1000) G1 <- sample(c("A", "B", "C"), size=1000,rep=T) G2 <- sample(c("X", "Y", "Z"),size=1000,rep=T) testdata <- data.frame(x,G1,G2) qplot(x,data = testdata, facets = G1~G2, geom="histogram")

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7.5.3 Plots with several layers Although the function qplot is enough for creating many plots, the use of the ggplot function in combination with geometric object functions is a much more powerful way to create plots that consists of several layers. the function ggplot c <- ggplot(data = testdata, aes(y=y,x=x) ) c + stat_smooth(colour=3, size=6) + geom_point()

134

8 Statistics The base installation of R contains many functions for calculating statistical summaries, data analysis and statistical modeling. Even more functions are available in all the R packages on CRAN. In this section we will discuss only some of these functions. For a more comprehensive overview of the statistical possibilities see for example [9] and [10].

8.1 Basic statistical functions 8.1.1 Statistical summaries and tests A number of functions return statistical summaries and tests. The following table contains a list of only some of the statistical functions in R. The names of the functions usually speak for themselves. Function acf(x, plot=F) chisq.test(x) cor(x,y) ks.test(z) mad(x) mean(x) mean(x, trim=a) median(x) quantile(x, probs) range(x) stem(x) t.test(x,...) var(x) var(x,y) var.test(x,y)

purpose auto or partial correlation coefficients chi squared goodness of fit test correlation coefficient Kolmogorov-Smirnov goodness of fit test median absolute deviation mean trimmed mean median sample quantile at given probabilities the range, i.e. the vector c(min(x), max(x)) stem-and-leaf-plot One or two sample Student’s t-test variance of x or covariance matrix of x covariance test on variance equality of x and y

Table 8.1: Some functions that calculate statistical summaries. The remainder of this sub section will give some examples of the above functions.

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quantiles The quantile function needs two vectors as input. The first one contains the observations, the second one contains the probabilities corresponding to the quantiles. The function returns the empirical quantiles of the first data vector. To calculate the 5 and 10 percent quantile of a sample from a N(0,1) distribution, proceed as follows: x <- rnorm(100) xq <- quantile(x,c(0.05,0.1)) xq 5% 10% -1.496649 -1.205602

The function returns a vector with the quantiles as named elements. stem-and-leaf-plots A stem-and-leaf-plot of x is generated by: stem(x) N = 100 Median = -0.014053 Quartiles = -0.676618, 0.749655 Decimal point is at the colon The decimal point is at the | -3 -2 -1 -0 0 1 2 3

| | | | | | | |

5 721 654422222111000000 988877666555544444433333222111100 111233345566667777788888889 000012224444788 01 3

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distribution tests To test if a data vector is drawn from a certain distribution the function ks.test can be used. x <- runif(100) out = ks.test(x,"pnorm") out One-sample Kolmogorov-Smirnov test data: x D = 0.5003, p-value < 2.2e-16 alternative hypothesis: two-sided

The output object out is an object of class ‘htest’. It is a list with five components. names(out) [1] "statistic" out$statistic D 0.5003282

"p.value"

"alternative" "method"

"data.name"

The function can also be used to test if two data vectors are drawn from the same distribution. x1 = rnorm(100) x2 = rnorm(100) ks.test(x1,x2) Two-sample Kolmogorov-Smirnov test data: x1 and x2 D = 0.1, p-value = 0.6994 alternative hypothesis: two-sided

Alternative functions that can be used are chisq.test, shapiro.test and wilcox.test. Note that the functions in table 8.1 usually require a vector with data as input. To calculate for example the median value of a column in a data frame: Either access the column directly or use the function with.

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median (cars$Price) [1] 12215.5 with( cars, mean(Price) )

Some functions accept a matrix as input. For example, the mean of a matrix x, mean(x), will calculate the mean of all elements in the matrix x. The function var applied on a matrix x will calculate the covariances between the columns of the matrix x. x <- matrix(rnorm(99),ncol=3) var(x) [,1] [,2] [,3] [1,] 1.4029791 -0.1047594 0.1188696 [2,] -0.1047594 1.0752726 -0.0587097 [3,] 0.1188696 -0.0587097 0.8468122

The function summary is convenient for calculating basic statistics of columns of a data frame. summary(cars) Price Min. : 5866 1st Qu.: 9932 Median :12216 Mean :12616 3rd Qu.:14933 Max. :24760 Type Compact:15 Large : 3 Medium :13 Small :13 Sporty : 9 Van : 7

Country Reliability Mileage USA :26 Min. : 1.000 Min. :18.00 Japan :19 1st Qu.: 2.000 1st Qu.:21.00 Japan/USA: 7 Median : 3.000 Median :23.00 Korea : 3 Mean : 3.388 Mean :24.58 Germany : 2 3rd Qu.: 5.000 3rd Qu.:27.00 France : 1 Max. : 5.000 Max. :37.00 (Other) : 2 NA’s :11.000 Weight Disp. HP Min. :1845 Min. : 73.0 Min. : 63.0 1st Qu.:2571 1st Qu.:113.8 1st Qu.:101.5 Median :2885 Median :144.5 Median :111.5 Mean :2901 Mean :152.1 Mean :122.3 3rd Qu.:3231 3rd Qu.:180.0 3rd Qu.:142.8 Max. :3855 Max. :305.0 Max. :225.0

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8.1.2 Probability distributions and random numbers Most of the probability distributions are implemented in R and each of the distributions has four ‘flavors’: the cumulative probability distribution function, the probability density function, the quantile function and the random sample generator. The names of these functions consist of the code for the distribution preceded by a letter indicating the desired flavor. • p cumulative probability distribution function • d probability density function • q quantile function • r random sample For example, the corresponding commands for the normal distribution are: pnorm(x,m,s) dnorm(x,m,s) qnorm(p,m,s) rnorm(n,m,s)

In these expressions m and s are optional arguments representing the mean and standard deviation (not the variance!); p is the probability and n the number of random draws to be generated. The next table gives an overview of the available distributions in R with the corresponding parameters. Don’t forget to precede the code with p, d, q or r (for example pbeta or qgamma). The column ‘Defaults’ specifies the default values of the parameters. If there are no default values, you must specify them in the function call. For example, rnorm(100) will run, but rbeta(100) will not. The following code generates 1000 random standard normal numbers with 5% contamination, using the ifelse function. x
rnorm(1000) <- rnorm(1000,0,10) runif(1000) ifelse(p < 0.95,x,cont)

The function sample randomly samples from a given vector. By default it samples without replacement and by default the sample size is equal to the length of the input vector. Consequently, the following statement will produce a random permutation of the elements 1 to 50:

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CHAPTER 8. STATISTICS Code beta binom cauchy chisq exp f gamma geom hyper lnorm logis nbinom norm pois t unif weibull wilcoxon

8.1. BASIC STATISTICAL FUNCTIONS

Distribution beta binomial Cauchy chi squared exponential F gamma geometric hyper geometric lognormal logistic negative binomial normal (Gaussian) Poisson Student’s t uniform Weibull Wilcoxon

Parameters shape1, shape2 size, prob location, scale df, ncp rate df1, df2 shape, rate, scale prob m, n, k meanlog, sdlog location, scale size, prob, mu mean, sd Lambda df, ncp min, max shape, scale m, n

Defaults –, – –, – 0, 1 -, 1 1 –, – –, 1, 1/rate – –, –, – 0, 1 0, 1 –, –, – 0, 1 1 –,0 0, 1 –, 1 –, –

Table 8.2: Probability distributions in R x <- 1:50 y <- sample(x) y [1] 48 20 10 37 39 16 11 1 45 42 27 49 14 38 18 5 44 41 2 22 [21] 50 33 25 12 24 34 30 6 43 13 15 40 31 4 35 36 26 19 32 47 [41] 17 21 7 28 3 23 29 46 8 9

To randomly sample three elements from x use sample(x,3) [1] 13 4 1

To sample three elements from x with replacement use sample(x,3,rep=T) [1] 13 6 9

To randomly select five cars from the data frame ‘cars’ proceed as follows:

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x <- sample(1:dim(cars)[1], 5) cars[x,] Weight Disp. Mileage Fuel Type Toyota Camry 4 2920 122 27 3.703704 Compact Acura Legend V6 3265 163 20 5.000000 Medium Ford Festiva 4 1845 81 37 2.702703 Small Honda Civic 4 2260 91 32 3.125000 Small Dodge Grand Caravan V6 3735 202 18 5.555556 Van

There are a couple of algorithms implemented in R to generate random numbers, look at the help of the function set.seed, ?set.seed to see an overview. The algorithms need initial values to generate random numbers the so-called seed of a random number generator. These initial numbers are stored in the S vector .Random.seed. Every time random numbers are generated, the vector .Random.seed is modified, which means that the next random numbers differ from the previous ones. If you need to reproduce your numbers, you need to manually set the seed with the set.seed function. set.seed(12) rnorm(5) [1] -1.258 0.710 1.807 -2.229 -1.429 rnorm(5) # different random numbers set.seed(12) rnorm(5) # the same numbers as the first call [1] -1.258 0.710 1.807 -2.229 -1.429

8.2 Regression models 8.2.1 Formula objects R has many routines to fit and analyse statistical models. In general, these models are used by calling a modeling function (like lm, tree, glm, nls or coxph) with a so-called formula object and additional arguments. Formula objects play a very important role in statistical modeling in R, they are used to specify the model to be fitted. The exact meaning of a formula object depends on the modeling function. We will look at some examples in the following sections. The general form is given by: response ~ expression

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Sometimes the term response can be omitted, expression is a collection of variables combined by operators. Some examples of formula objects: myform1 <- y ~ x1 + x2 myform2 <- log(y) ~ sqrt(x1) + x2:x3 myform1 y ~ x1 + x2 myform2 log(y) ~ sqrt(x1) + x2:x3 data.class(myform2) "formula"

A description of formulating models using formulas is given in the various chapters of [9]. The next sections will give some examples of different statistical models in R.

8.3 Linear regression models 8.3.1 Formula objects R can fit linear regression models of the form y = β0 + β1 x1 + · · · + βp xp +  where β = (β0 , · · · , βp ) are the intercept and p regression coefficients and x1 , · · · , xp the p regression variables. The error term  has mean zero and is often modeled as a normal distribution with some variance. For two regression variables you can use the function lm with the following formula y ~ x1 + x2

By default R includes the intercept of the linear regression model. To omit the intercept use the formula: y ~ -1 + x1 + x2

Be aware of the special meaning of the operators *, -, ^, \ and : in linear model formulae. They are not used for the normal multiplication, subtraction, power and division. The : operator is used to model interaction terms in linear models. The next formula includes an interaction term between the variable x1 and the variable x2

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y ~ x1 + x2 + x1:x2

which corresponds to the linear regression model y = β0 + β1 x1 + β2 x2 + β12 x1 x2 +  There is a short hand notation for the above formula which is given by y ~ x1*x2

In general, x1*x2*...*xp is a short hand notation for the model that includes all single terms, order 2 interactions, order 3 interactions, ..., order p interactions. To see all the terms that are generated use the terms function. myform <- y ~ x1*x2*x3*x4 terms(myform) # ignoring some other output generated by terms attr(,"term.labels") [1] "x1" "x2" "x3" "x4" [5] "x1:x2" "x1:x3" "x2:x3" "x1:x4" [9] "x2:x4" "x3:x4" "x1:x2:x3" "x1:x2:x4" [13] "x1:x3:x4" "x2:x3:x4" "x1:x2:x3:x4"

The ^ operator is used to generate interaction terms up to a certain order. y ~ (x1+x2+x3)^2

The above formula is equivalent to y ~ x1 + x2 + x3 + x1:x2 + x2:x3 + x1:x3

The - operator is used to leave out terms in a formula. We have already seen that -1 removes the intercept in a regression formula. For example, to leave out a specific interaction term in the above model use: y ~ (x1+x2+x3)^2 - x2:x3

which is equivalent to y ~ x1 + x2 + x3 + x1:x2 + x1:x3

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The function I is used to suppress the specific meaning of the operators in a linear regression model. For example, if you want to include a transformed x2 variable in your model, say multiplied by 2, the following formula will not work: y ~ x1 + 2*x2

The * operator already has a specific meaning, so you should use the following construction: y ~ x1 + I(2*x2)

You should also use the I function when you want to include a ‘centered’ regression variable in your model. The following formula will work, however, it does not return the expected result. y ~ x1 + (x2 - constant)

Use the following formula instead: y ~ x1 + I(x2 - constant)

8.3.2 Modeling functions Linear regression models are widely used to model linear relationships between different variables. There are many different functions in R to fit and analyze linear regression models. The main function for linear regression is lm and its main arguments are: lm(formula, data, weights, subset, na.action)

As an example we will use our ‘cars’ data set to fit the following linear regression model. Weight = β0 + β1 × Mileage +  In R this model is formulated and fitted as follows cars.lm <- lm( Weight ~ Mileage , data = cars)

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The result of the function lm is stored in the object ‘cars.lm’, which is an object of class ‘lm’. To print the object, simply enter its name in the R console window. cars.lm Call: lm(formula = Weight ~ Mileage, data = fuel.frame) Coefficients: (Intercept) Mileage 5057.83 -87.74223 Degrees of freedom: 60 total; 58 residual Residual standard error: 265.1798

Objects of class lm, and almost every other object resulting from statistical modeling functions, have their own printing method in S-PLUS. What you see when you type in cars.lm is not the complete content of the cars.lm object. Use the function print.default to see the complete object. print.default(cars.lm) $coefficients (Intercept) Mileage 5057.82990 -87.74223 $residuals Eagle Summit 4 397.663796 Ford Festiva 4 33.632728 Mazda Protege 4 189.921563 ... ... ...

Ford Escort 4 182.663796 Honda Civic 4 9.921563 Mercury Tracer 4 -491.531836

As you can see the object cars.lm is in fact a list with named components; coefficients, residuals etc. Use the function names to retrieve all the component names of the cars.lm objects names(cars.lm) [1] "coefficients" [4] "rank" [7] "qr" [10] "call"

"residuals" "fitted.values" "df.residual" "terms"

"effects" "assign" "xlevels" "model"

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So cars.lm contains much more information than you would see by just printing it. The next table gives an overview of some generic functions, which can be used to extract information or to create diagnostic plots from the cars.lm object. generic function summary(object) coef(object) resid(object) fitted(object) deviance(object) anova(object) predict(object) plot(object)

meaning returns a summary of the fitted model extracts the estimated model parameters extracts the model residuals of the fitted model returns the fitted values of the model returns the residual sum of squares returns an anova table returns predictions create diagnostic plots

Table 8.3: List of functions that accept an lm object These functions are generic. They will also work on objects returned by other statistical modeling functions. The summary function is useful to get some extra information of the fitted model such as t-values, standard errors and correlations between parameters. summary(cars.lm) Call: lm(formula = Weight ~ Mileage, data = cars) Residuals: Min 1Q -569.274 -159.073

Median 8.793

3Q 191.494

Max 570.241

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5057.830 180.402 28.04 <2e-16 *** Mileage -87.742 7.205 -12.18 <2e-16 *** --Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 Residual standard error: 265.2 on 58 degrees of freedom Multiple R-Squared: 0.7189, Adjusted R-squared: 0.714 F-statistic: 148.3 on 1 and 58 DF, p-value: < 2.2e-16

Model diagnostics The object cars.lm object can be used for further analysis. For example, model diagnostics:

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• Are residuals normally distributed? • Are the relations between response and regression variables linear? • Are there outliers? Use the Kolmogorov-Smirnov test to check if the model residuals are normally distributed. Proceed as follows: cars.residuals <- resid(cars.lm) ks.test( cars.residuals, "pnorm", mean = mean(cars.residuals), sd = sd(cars.residuals) ) One-sample Kolmogorov-Smirnov test data: cars.residuals D = 0.0564, p-value = 0.9854 alternative hypothesis: two-sided

Or draw a histogram or qqplot to get a feeling for the distribution of the residuals par(mfrow=c(1,2)) hist(cars.residuals) qqnorm(cars.residuals)

A plot of the residuals against the fitted value can detect if the linear relation between the response and the regression variables is sufficient. A Cooke’s distance plot can detect outlying values in your data set. R can construct both plots from the cars.lm object. par(mfrow=c(1,2)) plot(cars.lm, which=1) plot(cars.lm, which=4)

Updating a linear model Some useful functions to update (or change) linear models are given by: add1 This function is used to see what, in terms of sums of squares and residual sums of squares, the result is of adding extra terms (variables) to the model. The ‘cars’ data set also has an ‘Disp.’ variable representing the engine displacement.

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Normal Q−Q Plot 600

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Figure 8.1: A histogram and a qq-plot of the model residuals to check normality of the residuals. add1(cars.lm, Weight~Mileage+Disp.) Single term additions Model: Weight ~ Mileage Df Sum of Sq RSS 4078578 Disp. 1 1297541 2781037

AIC 672 651

drop1 This function is used to see what the result is, in terms of sums of squares and residual sums of squares, of dropping a term (variable) from the model. drop1(cars.lm, ~Mileage) Single term deletions Model: Weight ~ Mileage Df Sum of Sq RSS 4078578 Mileage 1 10428530 14507108

AIC 672 746

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Figure 8.2: Diagnostic plots to check for linearity and for outliers. update This function is used to update a model. In contrary to add1 and drop1 this function returns an object of class ‘lm’. The following call updates the cars.lm object. The ∼.+Disp construction adds the Disp. variable to whatever model is used in generating the cars.lm object. cars.lm2
update(cars.lm, ~. + Disp.)

Call: lm(formula = Weight ~ Mileage + Disp., data = cars) Coefficients: (Intercept) 3748.444

Mileage -57.976

Disp. 3.799

8.3.3 Multicollinearity The linear regression model can be formulated in matrix notation as follows: y = Xβ + 

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where X has N rows, the number of observations and p + 1 columns the number of regression coefficients plus an intercept. Then for a normally distributed error term  it can be shown that the least squares estimates βˆ for the parameter β are given by βˆ = (X 0 X)−1 X 0 y

(8.1)

When the matrix X does not have full rank, so less then p + 1, then the matrix X 0 X in equation 8.1 is singular and an inverse does not exists. This is the case of perfect multicollinearity, which does not happen often in practice. The problem of nearly perfect multicollinearity occurs when X 0 X is nearly singular. This occurs when two or more regression variables are strongly correlated. Consider the following simulated data. x1 <- runif(100,1,2) x2 <- runif(100,1,2) x3 <- 2*x1 +4*x2 + rnorm(100, 0, 0.01) y <- 6*x1 + 5*x2 + 3*x2 + rnorm(100, 0, 0.4) testdata <- data.frame(y,x1,x2,x3) out.model <- lm(y ~ x1+x2+x3, data = testdata) summary(out.model) Call: lm(formula = y ~ x1 + x2 + x3, data = testdata) Residuals: Min 1Q -1.09211 -0.26002

Median 0.05173

3Q 0.29653

Max 0.82532

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.1932 0.3146 -0.614 0.541 x1 0.7615 8.3111 0.092 0.927 x2 -2.4102 16.5890 -0.145 0.885 x3 2.6310 4.1428 0.635 0.527 Residual standard error: 0.4079 on 96 degrees of freedom Multiple R-Squared: 0.9815,Adjusted R-squared: 0.9809 F-statistic: 1698 on 3 and 96 DF, p-value: < 2.2e-16

Looking at the output, a strange thing is the huge standard error for x2. This may indicate that there is something wrong.

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SVD and VIF Two tools to detect multicollinearity are the singular value decomposition (SVD) of the X matrix and the calculation variance inflation factors (VIF). The singular value decomposition of X finds matrices U, D and V such that    X =U 



d0 d1

  V 

... dp

When X does not have full rank, one or more of the singular values di are zero. In practice this will not happen often. The more likely case is that the smallest singular value is small compared to the largest singular value. The SVD of the X matrix in the above example can be calculated with the function svd. X <- model.matrix(out.model) svd(X) $d [1] 94.26983374 3.06760623 1.29749565 ... matrix U and V not displayed...

0.02145514

The variance inflation factors V IFi , i = 1, ..., p are based on regressing one of the regression variables xi on the remaining regression variables xj , j 6= i for i = 1, ..., p. For each of these regressions the R-squared statistic Ri2 , i = 1, ..., p can be calculated. Then the VIF is defined as V IFi =

1 1 − Ri2

It can be shown that the V IFi can be interpreted as how much the variance of the estimated regression coeeficient βi is inflated by the existence of correlation among the regression variables in the model. A V IFi of 1 means that there is no correlation among the i-th regression variable and the remaining regression variables, and hence the variance of βi is not inflated at all. The general rule of thumb is that VIFs exceeding 4 warrant further investigation, while VIFs exceeding 10 are signs of serious multicollinearity requiring correction. The function vif in the ‘DAAG’ package calculates the VIFs for a fitted linear regression model.

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library(DAAG) vif(out.model) x1 x2 x3 4150.9 13130.0 17797.0

8.3.4 Factor (categorical) variables as regression variables The lm function (and other modeling functions, such as coxph and glm, as well) accepts factor variables (categorical variables) as regression variables. It is not possible to estimate a parameter for each level of the factor variable, as the model could be overparameterized, and the X 0 X matrix in formula 8.1 would be singular. One can avoid overparameterization by using a so-called contrast matrix to impose restrictions on the parameters. By default R uses the so called treatment contrast matrix for unordered factor variables, and a polynomial contrast matrix is used for ordered factor variables. There are other contrasts. The estimated parameter values in a treatment contrast are easy to interpret. One factor level is left out, then the parameter values of the other levels represent the difference between that level and the level that is left out. Consider the following example where we create an artificial data frame with one numeric response column and one factor column with four levels (A,B,C and D) as the regression variable. The corresponding y values of each factor level have a certain mean as calculated in the code below. y1 <- rnorm(100) + 5 y2 <- rnorm(100) + 10 y3 <- rnorm(100) + 30 y4 <- rnorm(100) + 50 y <- c(y1, y2, y3, y4) x <- as.factor( c(rep("A",100), rep("B",100), rep("C",100), rep("D",100)) ) testdata <- data.frame(x, y) lm(y~x, data=testdata) Call: lm(formula = y ~ x, data = testdata) Coefficients: (Intercept) 4.930

xB 5.177

xC 25.107

xD 45.087

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Level A has been left out. You can see that parameter value xC is about 25, representing the difference in mean between level A and level C. When using a treatment contrast, the lowest level is left out of the regression. By default, this is the level with the name that comes first in alphabetical order. The parameters estimates for the remaining levels represent the difference between that level and the lowest level. Consider the above example code again, but rename level A to level X. and fit the linear model again. x <- as.factor(c(rep("X",100),rep("B",100),rep("C",100),rep("D",100))) testdata <- data.frame(x,y) lm(y~x,data=testdata) Call: lm(formula = y ~ x, data = testdata) Coefficients: (Intercept) 10.107

xC 19.930

xD 39.910

xX -5.177

Now level B is lowest level and is left out. So the parameter estimate for xC represents the difference in mean between level B and C, which is about 20. If you are using a treatment contrast, the lowest level will be left out. When you don’t want to leave out that particular level, you can use the so-called SAS contrast. This is the treatment contrast but leaving out the last factor level. lm(y~x, data=testdata, contrasts = list(x = contr.SAS)) Call: lm(formula = y ~ x, data = testdata, contrasts = list(x = contr.SAS)) Coefficients: (Intercept) 4.930

x1 5.177

x2 25.107

x3 45.087

Or alternatively you can reorder the factor. Suppose you want to leave out level C in the above example, proceed as follows. testdata$x <- ordered(testdata$x,levels=c("C","B","D","X"))

The order in the levels is specified by the levels argument. You can check the order by printing the levels of the variable.

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levels(testdata$x) [1] "C" "B" "D" "X" lm(y~x, contrast=list(x=contr.treatment), data=testdata) Call: lm(formula = y ~ x, data = testdata, contrasts = list(x = contr.treatment)) Coefficients: (Intercept) 30.04

x2 -19.93

x3 19.98

x4 -25.11

In the above example, we used the function ordered to define the level C as the lowest level. Consequently, level C is left out in the regression and the remaining parameters are interpreted as difference between that level and the level C. The reorder function is used to order factor variables based on some other data. Suppose we want to order the levels of x in such a way that the lowest level has the smallest variance in y, then we use reorder as follows: testdata$x <- reorder(testdata$x,testdata$y,var) levels(testdata$x) [1] "D" "X" "C" "B"

Level D has the smallest variance in y, and will be left out in a regression where we use a treatment contrast for the regression variable x.

8.4 Logistic regression Logistic regression can be used to model data where the response variable is binary, a factor variable with two levels. For example, a variable Y with two categories ‘yes’ / ‘no’ or ‘good’ / ‘bad’. Many companies have build score cards based on logistic regression where they try to separate ‘good’ customers from ‘bad’ customers. The logistic regression model calculates the probability of an outcome, P (Y = good) and P (Y = bad) = 1 − P (Y = good) given some regression variables X1 , ..., Xp as follows: P (Y = good) =

exp(α0 + α1 X1 + ... + αp Xp ) 1 + exp(α0 + α1 X1 + ... + αp Xp )

The regression coefficients α0 , ..., αp are estimated with a data set.

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8.4.1 The modeling function glm The function glm can be used to fit a logistic regression model. Let’s generate a data set and demonstrate the different aspects of building a logistic regression model. nrecords = 1000 ncols = 3 x <- matrix( runif( nrecords*ncols), ncol=ncols) y <- 2 + x[,1] + 3*x[,2] - 4*x[,3] y = exp(y)/(1+exp(y)) ppp <- runif(nrecords) y <- ifelse(y > ppp, "good", "bad") testdata <- data.frame(y, x)

To get an idea which variables in your data set have an influence on your binary response variable: plot the observed fraction (‘yes’/‘no’) against the (potential) regression variables. • Divide the variable Xi into say ten buckets (equal intervals). • For each bucket calculate the observed fraction ‘good’/ ‘bad’. • Plot bucket number against observed fraction. Some R code that plots observed fractions for a specific regression variable: obs.prob <- function(x) { ## observed fraction of good, the ## second level in this case out <- table(x) out[2]/length(x) } plotfr <- function(y, x, n=10) { tmp <- cut(x,n) p <- tapply(y,tmp, obs.prob) plot(p) lines(p) title( paste(deparse(substitute(y)), "and", deparse(substitute(x))) ) }

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par(mfrow=c(2,2)) plotfr(testdata$y, testdata$X1) plotfr(testdata$y, testdata$X2) plotfr(testdata$y, testdata$X3)

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Figure 8.3: Explorative plots giving a first impression of the relation between the binary y variable and x variables. The plots in figure 8.3 show strong relations. For variable X3 there is a negative relation, just as we have simulated. The interpretation of the formula object that is needed as input for glm, is the same as in lm. So for example the : operator is also used here for specifying interaction between variables. The following code fits a logistic regression model and stores the output in the object test.glm. test.glm = glm(y ~ X1 + X2 + X3, family = binomial, data=testdata) summary(test.glm) Call: glm(formula = y ~ X1 + X2 + X3, family = binomial, data = testdata) Deviance Residuals: Min 1Q Median -2.5457 -0.5829 0.3867

3Q 0.6721

Max 1.9312

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Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.9616 0.2617 3.675 0.000238 *** X1 1.6361 0.3065 5.338 9.4e-08 *** X2 3.3955 0.3317 10.236 < 2e-16 *** X3 -4.0446 0.3513 -11.515 < 2e-16 *** --Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 1158.48 Residual deviance: 863.47 AIC: 871.47

on 999 on 996

degrees of freedom degrees of freedom

Number of Fisher Scoring iterations: 5

The object test.glm is a ‘glm’ object. As with lm objects in the previous section, the glm object contains more information. Enter print.default(test.glm) to see the entire object. The functions listed in table 8.3 can also be used on glm objects.

8.4.2 Performance measures To assess the quality of a logistic regression model several performance measures can be calculated. In the ROCR package there are functions to calculate the Receiver Operator curve (ROC), the Area under the ROC and lift charts from any glm model that is fitted. A logistic regression model can be used to predict ‘good’ or ‘bad’. Since the model only calculates probabilities of ‘good’, a threshold t ∈ (0, 1) is chosen, if the probability is above t then ‘good’ is predicted otherwise bad is predicted. Dependent on this threshold t we will have the following numbers, TP, FP, FN and TN as displayed in the following so-called confusion matrix.

Model predicted

Good Bad

Observed Good Bad TP FP FN TN number of goods number of bads

Table 8.4: confusion matrix

Let ng be the number of observed goods and nb the number of observed bads, then we have:

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1. TP (true positive) is the number of observations for which the model predicted good and that were observed good. True positive rate, T P R = T P/ng . 2. TN (true negative) is the number of observations for which the model predicted bad and that were observed bad. True negative rate, T N R = T N/nb . 3. FP (false positive) is the number of observations for which the model predicted good but were observed bad. False positive rate, F P R = 1 − T N R. 4. FN (false negative) is the number of observations for which the model predicted bad but were actually observed good. False negative rate, F N R = 1 − T P R. The ROC curve is a parametric curve, for all tresholds t ∈ (0, 1) the points (T P R, F P R) are calculated. Then these points are plotted. A ROC curve demonstrates several things: 1. It shows the trade-off between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity). 2. The closer the curve follows the left-hand border and then the top border of the ROC space, the more accurate the test. 3. The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test. The area under the curve (AUR) is a measure of how accurate the model can predict ‘good’. A value of 1 is a perfect predictor while a value of 0.5 is very bad predictor. The code below shows how to create an ROC and how to calculate the AUROC. library(ROCR) pred <- prediction( test.glm$fitted, testdata$y) perf <- performance(pred, "tpr", "fpr") plot(perf, colorize=T, lwd= 3) abline(a=0,b=1) performance(pred, measure="auc")@y.values [[1]] [1] 0.8353878

8.4.3 Predictive ability of a logistic regression Another way to look at the quality of a logistic regression model is to look at the predictive ability of the model. When we predict P (Y = good) with the model, a pair of observations with different observed responses (one ‘good’, the other ‘bad’) is said to be concordant if the observation with the ‘bad’ has a lower predicted probability than the observation with the ‘good’. If the observation with the ‘bad’ has a higher predicted

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Figure 8.4: The ROC curve to assess the quality of a logistic regression model probability than the observation with the ‘good’, then the pair is discordant. If the pair is neither concordant nor discordant, it is a tie. Four measures of association for assessing the predictive ability of a model are available. These measures are based on the number of concordant pairs, nc , the number of discordant pairs, nd , let the total number of pairs t, and the number of observations, N. 1. The measure called c, also an estimate of the area under ROC, c = (nc + 0.5 × (t − nc − nd ))/t 2. Somer’s D, D = (nc − nd )/t 3. Kendall’s tau-α, defined as (nc − nd )/(0.5 · N · (N − 1)) 4. Goodman-Kruskal Gamma, defined as (nc − nd )/(nc + nd ) Ideally, we would like nc to be very high and nd very low. So the larger these measures the better the predictive ability of the model. The function lrm in the ‘Design’ package can calculate the above measures.

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library(Design) lrm(y ~ X1 + X2 + X3, data=testdata) Logistic Regression Model lrm(formula = y ~ X1 + X2 + X3, data = testdata)

Frequencies of Responses bad good 198 802 Obs 1000

Max Deriv Model L.R. 2e-08 233.9

Coef Intercept 1.9018 X1 0.8474 X2 3.1342 X3 -3.9718

d.f. 3

S.E. Wald Z 0.3089 6.16 0.3147 2.69 0.3403 9.21 0.3784 -10.50

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C 0.824

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P 0.0000 0.0071 0.0000 0.0000

8.5 Tree models Tree-based models are not only used for predictive modeling. They can be used to screening variables, assessing the adequacy of linear models and summarizing large multivariate data sets. When the response variable is a factor variable with two or more levels, then the term classification tree is used. The tree model produces rules like: • IF Price ≤ 200 AND Weight ≤ 300 THEN Type is ‘Small’. • IF Price > 200 AND Weight > 300 AND Mileage > 23 THEN Type is ‘Van’.

When the response variable is numeric the tree is called a regression tree. The model produces rules like • IF Price ≤ 200 AND Weight ≤ 300 THEN Mileage = 34.6. • IF Price > 200 AND Weight > 456 AND Type is ‘Van’ THEN Mileage is 23.8.

These rules are constructed from the data by recursively dividing the data into disjoint groups by splitting certain variables. A detailed description of an algorithm is described in [10] and [11]. The basic ingredients of such an algorithm are: • A measure for the quality of a split. • A split selection rule. How and which variables do we split? • A stopping criteria, we need to stop splitting at some stage before we end up with individual data points.

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Compared to linear and logistic regression models trees have the following advantages • Easier to interpret, especially when there is a mix of numeric and factor variables. • Can model response variables that are factor and have more than two levels. • More adept at capturing nonadditive behavior.

8.5.1 An example of a tree model A nice feature of these rules is that they are easily visualized in a tree graph. There are some R packages that deal with trees. For example the packages rpart, party and randomForest. The latter two have methods to build multiple trees, that improve predictive accuracy compared to a single tree. We consider the modeling function rpart in the ‘rpart’ package, that constructs a single tree. This package also contains the example data frame ‘car.test.frame’, we use it to construct a tree that predicts the type of a car given the mileage and price of that car. library(rpart) fit <- rpart(Type ~ Mileage + Price, data = car.test.frame) ## basic overview of the rules fit n= 60 node), split, n, loss, yval, (yprob) * denotes terminal node 1) root 60 45 Compact (0.25 0.05 0.22 0.22 0.15 0.12) 2) Price>=9152.5 49 34 Compact (0.31 0.061 0.27 0.041 0.18 0.14) 4) Mileage>=20.5 37 22 Compact (0.41 0.027 0.32 0.054 0.19 0) 8) Mileage< 23.5 19 7 Medium (0.32 0.053 0.63 0 0 0) * 9) Mileage>=23.5 18 9 Compact (0.5 0 0 0.11 0.39 0) * 5) Mileage< 20.5 12 5 Van (0 0.17 0.083 0 0.17 0.58) * 3) Price< 9152.5 11 0 Small (0 0 0 1 0 0) * ## detailed listing of an rpart object displaying only a part. summary(fit) Call: rpart(formula = Type ~ Mileage + Price, data = car.test.frame) n= 60 CP nsplit rel error xerror xstd 1 0.2444444 0 1.0000000 1.1555556 0.05851383 2 0.1555556 1 0.7555556 0.9555556 0.07756585 3 0.1333333 2 0.6000000 0.8444444 0.08294973

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3 0.4666667 0.6000000 0.08563488

Node number 1: 60 observations, complexity param=0.2444444 predicted class=Compact expected loss=0.75 class counts: 15 3 13 13 9 7 probabilities: 0.250 0.050 0.217 0.217 0.150 0.117 left son=2 (49 obs) right son=3 (11 obs) Primary splits: Price < 9152.5 to the right, improve=10.259180, (0 missing) Mileage < 27.5 to the left, improve= 7.259083, (0 missing) Surrogate splits: Mileage < 27.5 to the left, agree=0.933, adj=0.636, (0 split) ... ...

A graphical representation can be obtained with the following code plot(fit) text(fit)

Price>=9152

|

Mileage>=20.5 Small

Mileage< 23.5 Van

Medium

Compact

Figure 8.5: Plot of the tree: Type is predicted based on Mileage and Price

8.5.2 Coarse classification and binning When building regression models binning or coarse classification is sometimes used.

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Binning is a procedure that creates a nominal (factor) variable from a continuous (numeric) variable. I.e. each value of a numeric variable gets mapped to a certain interval (or category). There are a couple of reasons why we want to do this. First nonlinear effects can be captured in a very simple way, second the binned variable is less sensitive to outliers. Coarse classification is a procedure to group all the possible outcomes of a nominal (factor) variable into a smaller set of outcomes. The main reason to do this is because there may be too many outcomes, and so some outcomes are very infrequently observed. Tree based models can used in the regression context to create bins or perform the coarse classification. In this context a response variable and a regression variable for which we want to create bins are availabe. Suppose we have the following data: age <- runif(500, 17, 75) p = exp(-0.1*age) + 0.5 r <- runif(500) y <- ifelse(p>r, "bad", "good") testdata <- data.frame(age,y)

So the probability of observing ‘good’ increases with ‘age’. For the creation of a score card we don’t want to use the absolute value of age, we want to bin the age variable into bins and use those bins. How do we chose these bins? • Simple approach, just ‘manually’ split the age variable into intervals. For example, intervals with the same number of points, or intervals with the same length. • Use a tree based approach, so fit a tree with only the age variable as the regression variable and the ‘good’ /‘bad’ variable as the response. To make the analysis more robust we want a minimum number of obervations in a bin, for example 30. out <- rpart( y~age, data = testdata, control = rpart.control(minbucket= 30) ) plot(out) text(out)

The tree in Figure 8.6 shows the result of the binning. In this case the tree algorithm identifies four age intervals (bins): age < 26.6, 26.6 ≤ age < 31.51, 31.51 ≤ age < 41.88 and age ≥ 41.88 .

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age< 41.88 |

age< 26.6 good age>=31.51 bad

good

bad

Figure 8.6: Binning the age variable, two intervals in this case

8.6 Survival analysis In R survival analysis (also called churn analysis in marketing) of duration data (also called time to event data) can be done with either non-parametric approaches (KaplanMeier or Cox proportional hazards) or with parametric approaches (accelerated failure time models). Typical for duration data is (right)censoring. In a study the analyst can often not wait until all machines fail. At the time of study some machines may still work and the only information that we have is that the machine has survived a certain time span. This is called right censoring. The R package ‘survival’ contains both non-parametric (the coxph function) and parametric modeling (the function survreg) functions. Another implementation of the accelerated failure time approach can be found in the package ‘Design’, the psm function. The modeling functions for survival analysis require the usage of an extra packaging function in the left hand side of a formula object. This function is used to indicate if a certain event was censored or not. For example, suppose we have a data frame with the columns time and status. The packaging function Surv connects time and status for right censored data. Where status = 1 means an event and status = 0 for censored. Surv(time,status) ~ Age

The packaging function allows the user to specify a different type of censoring. For example, left censored data is specified as follows:

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Surv(time,status, type="left") ~ Age + Sex

The right hand side of the formula has the same interpretation as in linear regression models.

8.6.1 The Cox proportional hazards model To demonstrate the function coxph from the package ‘survival’, that fits a Cox proportional hazards model we use data that is analyzed in the paper of M. Prins and P.J Veugelers [12]. The data results from a multi-center cohort study among injecting drug users, one of the things the researches wanted to know was how long before a HIV infected person would develop AIDS, the incubation time. The data and a complete description can be downloaded from www.splusbook.com. In R we import the data into the IDUdata data frame and print only the first 10 persons of the data frame and show only a few columns. We also need to calculate the incubation time as the difference between the AIDS time and the entry time. The time unit used for the incubation time is months. IDUdata <- read.csv("IDUdata.txt") IDUdata$IncubationTime = IDUdata$AIDStime - IDUdata$Entrytime > IDUdata[1:10, c(3,5,6,7,8,9,13)] Sex PosDate Age Entrytime AidsStatus Event IncubationTime 1 1 194 24 251 0 0 36 2 1 166 25 215 1 3 60 3 2 225 26 225 1 2 28 4 1 289 29 238 0 0 63 5 1 297 33 199 0 0 102 6 1 192 20 265 1 3 22 7 1 282 22 282 0 0 19 8 1 195 29 195 0 0 100 9 1 183 22 166 0 0 123 10 1 223 33 224 1 2 10

An estimation of the survival curve for the incubation time can be calculated with the function survfit. kmfit <- survfit(Surv(IncubationTime, AidsStatus)~1, data=IDUdata) kmfit Call: survfit(formula = Surv(IncubationTime, AidsStatus) ~ 1, data = IDUdata) n 418

events 76

median 0.95LCL 0.95UCL 135 118 Inf

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The median survival time is estimated to be 135 months. So if a person is infected with HIV, he has a 50% probability that he will not develop AIDS within 135 months. A numerical and graphical output the complete survival curve can be created from the kmfit object. Use the functions summary and plot. summary(kmfit) Call: survfit(formula = Surv(IncubationTime, AidsStatus) ~ 1, data = IDUdata) time n.risk n.event survival 0 414 3 0.993 1 405 1 0.990 2 402 1 0.988 5 388 1 0.985 7 385 2 0.980 10 378 3 0.972 ... ... plot(kmfit) title("Survival curve for the abline(h=c(0.9,0.8), lty=2) abline(v=c(45,76), lty=2)

std.err lower 95% CI upper 95% CI 0.00417 0.985 1.000 0.00483 0.981 1.000 0.00541 0.977 0.998 0.00596 0.974 0.997 0.00694 0.967 0.994 0.00821 0.956 0.989

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Figure 8.7: Survival curve: 10% will develop AIDS before 45 months and 20% before 76 months.

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It is interesting to know if the age of a person has any impact on the incubation time. A Cox proportional hazards model is used to investigate that. IDU.analysis1 <- coxph( Surv(IncubationTime, AidsStatus) ~ Age, data=IDUdata )

The result of coxph is an object of class ‘coxph’. It has its own printing method: IDU.analysis1 Call: coxph(formula = Surv(IncubationTime, AidsStatus) ~ Age, data = IDUdata)

coef exp(coef) se(coef) z p Age 0.0209 1.02 0.0175 1.20 0.23 Likelihood ratio test=1.39

on 1 df, p=0.238

n= 418

The summary function for ‘coxph’ objects returns the following information: summary(IDU.analysis1) Call: coxph(formula = Surv(IncubationTime, AidsStatus) ~ Age, data = IDUdata) n= 418 coef exp(coef) se(coef) z p Age 0.0209 1.02 0.0175 1.20 0.23 exp(coef) exp(-coef) lower .95 upper .95 Age 1.02 0.98 0.987 1.06 Rsquare= 0.003 (max possible= Likelihood ratio test= 1.39 on Wald test = 1.43 on Score (logrank) test = 1.44 on

0.851 ) 1 df, p=0.238 1 df, p=0.231 1 df, p=0.231

Use the generic function resid to extract model residuals. In a survival analysis there are several types of residuals, for example martingale residuals and deviance residuals. The residuals can be used assess the linearity of a regression variable or to identify

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influential points. See [9] and [13] for a detailed discussion on how to use the residuals from a Cox model. As an example we use the martingale residuals to look at the functional form of the Age regression variable. Do this by • Fitting a model without the Age variable (in our case, the model reduces to a model with only the intercept). • Extract the martingale residual from that model. • Plot the martingale residual against the ‘Age’ variable, see Figure 8.8.

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Figure 8.8: Scatter plot of the martingale residuals An estimation of the survival time can be made for subjects of certain ages, use the function survfit as in the following code. The output shows that the median predicted survival time for a subject of age ten is infinite. As Figure 8.9 shows, the solid line corresponding to a subject of age ten never reaches 0.5.

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newAges = data.frame(Age=c(10,30,60)) pred <- survfit(IDU.analysis1,newdata=newAges, se=T) pred n events median 0.95LCL 0.95UCL [1,] 418 76 Inf 135 Inf [2,] 418 76 135 118 Inf [3,] 418 76 97 60 Inf plot(pred,lty=1:3)

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Figure 8.9: Three subjects with age 10, 30 and 60

8.6.2 Parametric models for survival analysis In a parametric modeling approach the distribution of the time T to an event is modeled with a parametric model. For example a log normal or Weibull distrbution. The general form resembles the ordinary linear regression model and is given by: f (T ) = a0 + a1 X1 + · · · + ap Xp + σW for some distribution W . A possible choice for f is the log function, this corresponds to the accelerated failure time model. The code below fits an accelerated failure time model for the IDU data with a Weibul distribution.

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## some times are negative for convenience we set it to 1. IDUdata$IncubationTime[IDUdata$IncubationTime <= 0] = 1 IDU.param <- survreg( Surv(IncubationTime, AidsStatus) ~ Age, data = IDUdata, dist = "weibull" ) summary(IDU.param) Call: survreg(formula = Surv(IncubationTime, AidsStatus) ~ Age, data = IDUdata, dist = "weibull") Value Std. Error z p (Intercept) 5.7839 0.3934 14.70 6.38e-49 Age -0.0135 0.0132 -1.02 3.07e-01 Log(scale) -0.2721 0.1001 -2.72 6.57e-03 Scale= 0.762 Weibull distribution Loglik(model)= -510.6 Loglik(intercept only)= -511.1 Chisq= 1.02 on 1 degrees of freedom, p= 0.31 Number of Newton-Raphson Iterations: 7 n= 418

Predictions of the survival time can be made with the predict method. newAges = data.frame(Age=30:35) newAges$prediction = predict(IDU.param, newdata=newAges) newAges Age prediction 1 30 216.6348 2 31 213.7250 3 32 210.8543 4 33 208.0222 5 34 205.2282 6 35 202.4716

8.7 Non linear regression A good book on nonlinear regression is [14]. The function nls in R can fit nonlinear regression models of the form:

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y = f (x, θ) +  for some nonlinear function f and where x is a vector of regression variables. The error term  is often assumed to be normally distributed with mean zero. The p unknown parameters are in the vector θ = (θ1 , ..., θp ) and need to be estimated from data points (yi , xi ), i = 1, ..., n. Unlike the formula specification in linear models, the operators in a formula object for nonlinear models have the ‘normal’ mathematical meaning. For example, to specify the following nonlinear model: y=

β 1 x1 + β 2 + x2

use the formula object: y ~ b1*x1/(b2+x2)

The right hand side of an the formula for nonlinear models can also be a function of the data and parameters. For example: mymodel <- function(b1,b2,x1,x2){ b1*x1/(b1+x2) } y ~ mymodel(b1,b2,x1,x2)

The nls function tries to estimate parameters for a nonlinear model that minimize the sum of squared residuals. So the following statement: nls(y ~ mymodel(b1,b2,x1,x2))

minimizes X

(y[i] − mymodel(b1,b2,x1[i],x2[i]))2

i

with respect to b1 and b2. In nonlinear models the right hand side of the model formula may be empty, in which case R will minimize the sum of the quadratic right hand side terms. The above specification, for example, is equivalent to nls(~ y - mymodel(b1,b2,x1,x2))

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To demonstrate the nls function we first generate some data from a known nonlinear model and add some noise to it. x <- runif(100,0,30) y <- 3*x/(8+x) y <- y +rnorm(100,0,0.15) our.exp <- data.frame (x=x,y=y)

The next plot is a scatterplot of our simulated data.

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Figure 8.10: Scatter plot of our simulated data for nls The model that we used to simulate our example data is the so-called Michaelis-Menten model, which is given by the following form: y=

β1 x + β2 + x

where  is normally distributed, β1 has value 3 and β2 has value 8. To fit the model and display the fit results, proceed as follows: fit1 <- nls( y ~ beta1*x /(beta2 + x), start = list( beta1 = 2.5, beta2 = 7)), data=our.exp

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) summary(fit1) Formula: y ~ beta1 * x/(beta2 + x) Parameters: Estimate Std. Error t value Pr(>|t|) beta1 2.9088 0.0672 43.28 <2e-16 *** beta2 7.3143 0.5376 13.61 <2e-16 *** --Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 Residual standard error: 0.1414 on 98 degrees of freedom

So the first argument of nls is a formula object. Unlike the formula specification in lm or coxph the operators here have the ‘normal’ mathematical meaning. The second argument is required and specifies the initial values of the parameters. They are used to initiate the optimization algorithm to estimate the parameter values. The third argument is the data frame with the data. Note that the nls function can sometimes fail to find parameter estimates. One of the reasons could be poor initial values for the parameters. For example: fit1 <- nls( y ~ a*x/(b+x), start = list(a = 25000, b = 600), data = our.exp ) Error in nls( y ~ a * x/(b + x), start = list(a = 25000, b = 600), data = our.exp ): singular gradient

The output of nls is a list of class ‘nls’. Use the generic summary function to get an overview of the fit. The output of summary is also a list, it can be stored and used to calculate the variance matrix of the estimated parameters. Although you can do this calculation directly on the components of the list, as in the code below. fit1.sum <- summary(fit1) fit1.sum$cov.unscaled * fit1.sum$sigma^2 beta1 beta2 beta1 0.004516374 0.03408523 beta2 0.034085234 0.28903582

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In this case it is more convenient to use the function vcov which is a generic function that also accepts a model object other than that generated by nls. vcov(fit1) beta1 beta2 beta1 0.004516374 0.03408523 beta2 0.034085234 0.28903582

Use the function predict to calculate model predictions and standard errors of these predictions. Suppose we want to calculate prediction on the values of x from 0 to 10. Then we proceed as follows: x <- seq(0,30,l=100) pred.data <- data.frame(x=x) x.pred <- predict(fit1, newdata = pred.data)

The output object x.pred is a vector which contains the predictions. You can insert the predictions in the pred.data data frame and plot the predictions together with the simulated data as follows: pred.data$ypred <- x.pred plot(our.exp$x, our.exp$y) lines(pred.data$x,pred.data$ypred)

8.7.1 Ill-conditioned models Similair to the problem of multicollinearity in linear regression (as described in section 8.3.3), nonlinear models can be ill-conditioned too. However, with nonlinear models it may not only be a data issue, but the nonlinear model it self may be ill conditioned. Such a model can cause the estimation procedure to fail, or estimated model parameters may have very large confidence intervals. Consider the following model, the so-called Hill equation: f (x, θ) = Vm

xα k α + xα

Given the data points in Figure 8.12 we see that two sets of paramters fit the data equally well. The solid and dashed lines corresponds to α = 0.8, 3.1, Vm = 1.1.08, 1, k = 0.3, 1 respectively. Either more data at lower x values are needed or a different model must be used. The following R code simulates some data from the model and fits the model with the simulated data.

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Figure 8.11: Simulated data and nls predictions ## Create the model function HillModel <- function(x, alpha, Vm, k) { Vm*(x^alpha)/(k^alpha + x^alpha) } ## Simulate data and put it in a data frame k1 = 0.3; Vm1 = 1.108; alpha1 = 0.8 x <- runif(45, 1.6, 5) datap <- HillModel(x, alpha1,Vm1,k1) + rnorm(45, 0, 0.09) simdata <- data.frame(x, datap) ## Fit the model out <- nls( datap ~ HillModel(x, alpha,Vm,k), data = simdata, start = list( k = 0.3, Vm=1.108, alpha=0.8) ) ## Print output summary(out) vcov(out) Formula: datap ~ HillModel(x, alpha, Vm, k) Parameters: Estimate Std. Error t value Pr(>|t|) k 0.1229 1.4140 0.087 0.93116 Vm 1.0108 0.3184 3.174 0.00281 ** alpha 0.9399 5.5311 0.170 0.86588 ---

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Residual standard error: 0.08745 on 42 degrees of freedom Number of iterations to convergence: 14 Achieved convergence tolerance: 6.249e-06 k Vm alpha k 1.9993975 -0.4390971 7.79519 Vm -0.4390971 0.1013896 -1.74292 alpha 7.7951900 -1.7429199 30.59291

Eventhough the fitting routine nls started with the same parameter values as those that were used in simulating the data the nls function does not get really close, and the standard error of the alpha parameter is quit large. Even more disturbing, when we simulate new data with the same parameters the nls function will come up with very different results. When observations with a smaller x value are available the problem is less ill-conditioned. ## simulate data with smaller x values x <- runif(45, 0.01, 5) datap <- HillModel(x, alpha1,Vm1,k1) + rnorm(45, 0, 0.09) simdata <- data.frame(x, datap) ## Fit the model out <- nls(

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datap ~ HillModel(x, alpha,Vm,k), data = simdata, start = list( k = 0.3, Vm=1.108, alpha=0.8) ) ## Print output summary(out) vcov(out) Estimate Std. Error t value Pr(>|t|) k 0.24442 0.04727 5.171 6.1e-06 *** Vm 1.04371 0.06781 15.392 < 2e-16 *** alpha 0.94588 0.28034 3.374 0.00160 ** --Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1 Residual standard error: 0.08786 on 42 degrees of freedom Number of iterations to convergence: 4 Achieved convergence tolerance: 8.43e-07 k Vm alpha k 0.002234067 0.001549951 -0.00304783 Vm 0.001549951 0.004598040 -0.01778873 alpha -0.003047830 -0.017788730 0.07859150

When data with a smaller x value are not available, the Hill model with three parameters is not identifyable. Maybe a parameter should be fixed at a certain value instead of trying to estimate it.

8.7.2 Singular value decomposition A useful trick to find out if certain parameters or combination of parameters are estimable (identifyable), i.e. whether they influence model predictions enough, or whether they will be obscured by measurement noise, is a singular value decomposition of the so-called sensitivity matrix. Let us assume we have a (non linear) model. yipred = f (xi , θ). For data point i = 1, ..., n. Then the sensitivity matrix S(θ) for the parameter vector θ, is defined by

S(θ) =

∂y pred . ∂θ

So S can be calculated by:

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 (S(θ))ij =

∂y pred ∂θ

 = ij

∂yipred . ∂θj

This method will rank the importance with respect to the influence on y pred of linear combinations of the parameters. Thereto a singular value decomposition of S is performed. 



d1 d2

  S=U 

..

  V , 

. dp

where U and V are unitary, and the di are called the singular values of S(θ). This can also be interpreted as  ∆(y

pred

  )'U 



d1 d

   V · ∆θ . 

... dp

So the i-th singular value di shows the effect of changes of the parameters in the direction given in the i-th row of V . If a singular value drops below a certain critical value or is relatively small compared to the largest singualr value then the model shows signs of ill-conditioning. This certainly obvious if a singular value is (nearly) zero, a small change in the parameter will have no effect on the measurement space. Note that for a linear regression model y = Xβ the sensitivity matrix S is just the matrix X and that small singular values correspond to the multicollinearity problem, see section 8.3.3. For the Hill model the code below uses the function deriv for the calculation of the sensitivity matrix and the function svd for its singular value decomposition. ## calc symbolic derivatives with respoect to the parameters ModelDeriv <- deriv( expression( Vm*(x^alpha)/(k^alpha + x^alpha)), name = c("Vm", "alpha", "k") ) ## evaluate the derivative at a certain x and parameter values sensitivity <- eval( ModelDeriv, envir = list( x = seq(from = 1.6,to = 5, l = 50), k = 0.3,

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Vm = 1.108, alpha = 0.8 ) ) ## the gradient matrix is given as an attribute extract it and ## calculate the singular value decomposition sensitivity <- attributes(sensitivity)$gradient svd(sensitivity) $u ... matrix u skipped ... $d [1] 6.89792666 0.52385899 0.03482234 $v [,1] [,2] [,3] [1,] -0.8862039 -0.4128734 -0.2101863 [2,] -0.3014789 0.1694330 0.9382979 [3,] 0.3517857 -0.8948900 0.2746248

The largest singular value is 6.898 and the smallest has a value of 0.0348. This ratio becomes better is we include data points with smaller x values. sensitivity <- eval( ModelDeriv, envir = list( x = seq(from = 0.01, to = 5, l = 50), k = 0.3, Vm = 1.108, alpha = 0.8 ) ) sensitivity <- attributes(sensitivity)$gradient svd(sensitivity) ... $d [1] 6.6156912 1.3827939 0.4279231 ...

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9 Miscellaneous Stuff 9.1 Object Oriented Programming 9.1.1 Introduction The programming language in R is object-oriented, In R this means: • All objects in R are members of a certain class. • There are generic methods that will pass an object to its specific method. • The user can create a new classes, new generic and specific methods. There are many classes in R, such as ‘data.frame’, ‘lm’ and ‘h.test’. The function data.class can be used to request the class of a specific object. mydf <- data.frame(x=c(1,2,3,4,5), y = c(4,3,2,1,1)) data.class(mydf) [1] "data.frame" myfit <- lm(y~x, data=mydf) data.class(myfit) [1] "lm"

There are two object oriented systems in R, old style classes (also called S version 3 or S3 classes), and new style classes (also called S version 4 or S4 classes). We first discuss old style classes and then new style classes. Note that many of the existing routines still make use of the old-style classes. When creating new classes, it is recommended to use new style classes.

9.1.2 Old style classes Generic and specific methods In R there are a number of generic methods that can be applied to objects. For example, any object can be printed by using the generic print method:

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print(mydf) print(myfit)

or simply mydf myfit

A data frame will be printed in a different way than an object of class lm. It may not be surprising that the generic print function does not do the actual printing, but rather looks at the class of an object and then calls the specific print method of this class. The function print therefore does not show much of the code that does the actual printing. print function (x, ...) UseMethod("print")

A generic function has this form, a ‘one-liner’ with a call to the function UseMethod. For example, if the class of the object myfit is lm then print(myfit) will call the function print.lm. If the class of the object is ‘someclass’ then R will look for the function print.someclass. If that function does not exists then the function print.default will be called. The function methods returns all specific methods for a certain class: > methods(class="lm") [1] add1.lm* [5] confint.lm* [9] dfbetas.lm* [13] extractAIC.lm* [17] influence.lm* [21] model.frame.lm [25] print.lm [29] rstudent.lm [33] vcov.lm*

alias.lm* cooks.distance.lm* drop1.lm* family.lm* kappa.lm model.matrix.lm proj.lm* simulate.lm*

anova.lm deviance.lm* dummy.coef.lm* formula.lm* labels.lm* plot.lm residuals.lm summary.lm

case.names.lm* dfbeta.lm* effects.lm* hatvalues.lm logLik.lm* predict.lm rstandard.lm variable.names.lm*

Non-visible functions are asterisked

The output of the function methods is a vector with the specific methods. So for the class lm we see that plot.lm is a specific method, so a we could use plot(lm.object). Another specific method is extractAIC.lm. The AIC quantity for a linear regression model can be calculated as follows: Fit a linear regression model with the function lm. This results in an object of class lm. Then apply the generic function extractAIC, which will call the specific extractAIC.lm function.

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cars.lm <- lm(Price~Mileage,data=cars) extractAIC(cars.lm) [1] 2.0000 967.2867

The AIC quantity can also be calculated for other models, such as the Cox proportional hazards model. For the model fitted in section 8.6 with the function coxph, we extract the AIC: IDU.analysis1 <- coxph( Surv(IncubationTime, AidsStatus) ~ Age, data=IDUdata ) extractAIC(IDU.analysis1) [1] 1.0000 796.3663

The function methods can also be used to see which classes have an implementation of a specific method. methods(generic.function="extractAIC") [1] extractAIC.aov* extractAIC.coxph* [4] extractAIC.glm* extractAIC.lm* [7] extractAIC.survreg*

extractAIC.coxph.penal* extractAIC.negbin*

Non-visible functions are asterisked

Creating new classes R allows the user to define new classes and new specific and generic methods in addition to the existing ones. The function class can be used to assign a certain class to an object. For example: mymatrix <- matrix(rnorm(50^2),ncol=50) class(mymatrix) <- "bigMatrix"

The object mymatrix is now a matrix of class ‘bigMatrix’ (whatever that may mean). The class bigMatrix does not have a lot of meaning yet, since it does not have any specific methods. We will write a number of specific methods for objects of class bigMatrix in the following section. Using the function class directly is not recommended. One could for instance run the following statements without any complaints or warnings m2 <- matrix(rnorm(16),ncol=4) class(m2) <- "lm"

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However, m2 is not a real lm object. If it is printed, R will give something strange. When an lm object is printed, the specific function print.lm is called. This function expects a proper lm object with certain components. Our object m2 does not have these components. m2 Call: NULL Warning messages: 1: $ operator is deprecated for atomic vectors, returning NULL in: x$call 2: $ operator is deprecated for atomic vectors, returning NULL in: object$coefficients No coefficients

So it is recommended to use a so-called constructor function. To create an object of certain class use only the constructor function for that class. The constructor function can then be designed in such a way that it only returns a ‘proper’ object of that class. If you want an lm object use the function lm, which can act as a constructor function for the class lm. For our bigMatrix class we create the following constructor function: bigMatrix <- function(m) { if(data.class(m) == "matrix") { class(m) = "bigMatrix" return(m) } else { warning("not a matrix") return(m) } } m1 <- bigMatrix("ppp") m2 <- bigMatrix( matrix(rnorm(50^2),ncol=50))

Defining new generic and specific methods Two specific methods can be created for our bigMatrix class, print.bigMatrix and plot.bigMatrix. Printing a big matrix results in many numbers on screen. The specific print method for bigMatrix only prints the dimension and the first few rows and columns.

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print.bigMatrix <- function(x, nr=3,nc=5) { cat("Big matrix \n") cat("dimension ") cat(dim(x)) cat("\n") print(x[1:nr, 1:nc]) } m2 Big matrix dimension 50 50 [,1] [,2] [,3] [,4] [,5] [1,] -0.7012566 -0.7327267 -0.706452 -0.2355600 -1.2577592 [2,] 1.6390825 -0.2999556 -1.131336 -0.2536510 -0.3878151 [3,] 0.8964895 0.2022080 1.379076 -1.7892237 0.9087716

The plot method displays the matrix as an image plot. plot.bigMatrix <- function(x) { image(1:ncol(x),1:nrow(x),x) title( paste("plot of matrix ", deparse(substitute(x)), sep="") ) } plot(m2)

9.1.3 New Style classes There is no formal description of an object of S3 class. It could be a matrix where the user (accidently) assigned the lm class to it. The new style classes in R allow the user to define a new class more formally than old style classes. The new style classes differ from the old style classes: • All objects of a new-style class must have the same structure. This is not true for many old-style classes. The new-style classes have a more formal and tighter specification. • The new class mechanism has greater uniformity than the old and the new engine has many more tools to make programming easier. • Methods can be designed for data types (for example, vectors) that where not classes in the old engine. • Class inheritance is more rigorous than in the old style classes.

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30 10

20

1:nrow(x)

40

50

plot of matrix m2

10

20

30

40

50

1:ncol(x)

Figure 9.1: Result of the specific plot method for class bigMatrix. Creating a new style class definition New-style classes are made up of slots. These are similar to but distinct from components of a list, in that the number of slots and their names and classes are specified when a class is created: objects are extracted from slots by the @ operator. Exact matching of slot names is used, unlike the $ operator for lists. A new style class definition is created with the function setClass. Its first argument is the name of the class, and its representation argument specifies the slot. For example, a class ‘fungi’ to represent the spatial location of fungi in a field might look like: setClass("fungi", representation( x="numeric", y="numeric", species="character" ) )

Once a class definition has been created it can be examined by the function getClass.

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getClass("fungi") Slots: Name: Class:

x numeric

y species numeric character

To list all the classes in R or in your workspace use the function getClassses. ## class definitions in your workspace getClasses(where=1) [1] "fungi"

The class ‘fungi’ can also be created by combining other classes. For example: setClass("xyloc", representation(x="numeric", y="numeric") ) setClass("fungi", representation("xyloc", species="character") )

A class can be removed with the function removeClass. To create (or instantiate) an object from the fungi class use the function new. field1 <- new("fungi", x=runif(10), y=runif(10), species=sample(letters[1:5],rep=T,10) ) field1 An object of class "fungi" Slot "x": [1] 0.41644379 0.89240433 0.88980142 0.77224325 0.80395122 0.83608564 [7] 0.04149246 0.24511134 0.74946802 0.26268302 Slot "y": [1] 0.6828478 0.2134961 0.8681543 0.9748187 0.0253564 0.9479711 0.3381227 [8] 0.3446705 0.4415452 0.0979566 Slot "species": [1] "a" "d" "e" "d" "b" "d" "c" "e" "a" "b"

When you instantiate new objects from a certain class, you can perform a validity check. For example, our ‘fungi’ class should have input vectors of the same lengths. We can build in a validity check as follows.

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## function to check validity, it should return true or false validFungi <- function(object){ len <- length([email protected]) if(len != length([email protected]) || length([email protected]) != len) { cat("Mismatch in length of slots") return(FALSE} } else return(TRUE) } setClass("fungi", representation( x="numeric", y="numeric", species="character" ), validity = validFungi ) setValidity("fungi", validFungi)

field2 <- new("fungi", x=runif(110), y=runif(10), species=sample(letters[1:5],rep=T,110) ) Error in validObject(.Object) : invalid class "fungi" object: FALSE Mismatch in length of slots

The function validFungi, as any validity checking function, must have exactly one argument called object. Creating new generic and specific methods A generic function is show, which shows an object. If we want to show our objects from class fungi in a different way then we can write a new function (myfungishow) that shows our fungi object differently. The function setMethods sets the new function myfungishow as the specific show method for the fungi class. We have the following R code: myfungishow <- function(object){ tmp = rbind( x = format(round([email protected],2)), y = format(round([email protected],2)),

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species = [email protected] ) dimnames(tmp)[[2]] = rep("", length([email protected])) print(tmp, quote=F) } setMethod("show","fungi", myfungishow) field1 x 0.97 0.55 0.44 0.03 0.92 0.46 0.49 0.92 0.30 0.19 y 0.44 0.15 0.35 0.79 0.73 0.42 0.04 0.65 0.68 0.18 species c e a c c d e e e b

Note that the setMethod function copies the function myfungishow into the class information. In fact after a call to setMethod the function myfungishow can be removed. This totally different from the old-style classes, where the specific method was searched for by a naming convention (print.fungi). To see the specific show method for the fungi class use the function getMethods. getMethods("show", w=1) An object of class "MethodsList" Slot "methods": $fungi Method Definition: function (object) { tmp = rbind(x = format(round(ob[email protected], 3)), y = format(round([email protected], 2)), species = [email protected]) dimnames(tmp)[[2]] = rep("", length([email protected])) print(tmp, quote = F) } Signatures: object target "fungi" defined "fungi" ... ...

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9.2 R Language objects 9.2.1 Calls and Expressions In R you can use the language to create new language constructions or adjust existing ones. For example, performing symbolic manipulation in R such as calculating derivatives symbolically, or writing functions that accept expressions as input. In R, ‘language objects’ are either objects of type ‘name’, ‘expression’ or ‘call’. To deal with R language objects you should prevent the system from beginning the usual evaluation of expressions and calls. Suppose we have the following statements: x <- seq(-3,3,l=100) y <- sin(x)

The evaluated version of sin(x) is stored in the object y (so y contains 100 numbers). That this result originated from sin(x) is not visible from y anymore. To keep an unevaluated version, the normal evaluation needs to be interrupted. This can be done by the function substitute. y <- substitute(sin(x)) y sin(x)

The object y is a so called ‘call’ object. The object y can still be evaluated using the function eval. eval(y) [1] -0.14112001 -0.20082374 -0.25979004 -0.31780241 -0.37464782 [6] -0.48400786 -0.53612093 -0.58626538 -0.63425707 -0.67991980 [11] -0.76359681 -0.80130384 -0.83606850 -0.86776314 -0.89627139 ...

In order to print the object y, for instance in a graph, y must be deparsed first. This can be done using the function deparse, which converts y to a character object. x <- seq(-3,3,l=100) titletext <- deparse(substitute(sin(x)) y <- sin(x) plot(x,y,type="l") title(titletext)

This seems cumbersome, since we could simply have used:

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title("sin(x)")

However, the substitute-deparse combination will come to full advantage in functions, for example: printexpr <- function(expr){ tmp <- deparse(substitute(expr)) cat("The expression ") cat(tmp) cat(" was typed.") invisible() } printexpr(sin(x)) The expression sin(x) was typed.

The function sys.call can be used inside a function and stores the complete call to the function that contains the sys.call function. plotit <- function(x,y){ plot(x,y) title(deparse(sys.call()) } plotit(rnorm(100),rnorm(100))

9.2.2 Expressions as Lists Expressions and calls can be seen as recursive lists, so they can be manipulated in the same way you manipulate ordinary lists. To create calls use the function quote, to create expressions use the function expression. The output of these functions can be evaluated using the eval function. my.expr <- expression(3*sin(rnorm(10))) my.expr eval(my.expr) [1] 0.09410139 [7] 2.98911418

1.41480964 0.71378911 0.25474676 -2.02085040

2.33318300 0.88611195

2.05434944

2.91265390

Let’s look at the expression my.expr we transform it to a list using the function as.list.

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as.list(my.expr) [[1]]: 3 * sin(rnorm(10))

This is a list with one component. Let us zoom in on this component and print it as a list. as.list(my.expr[[1]]) [[1]]: ’*’ [[2]]: [1] 3 [[3]]: sin(rnorm(10))

In this list, the first element of which is an object of class ‘name’. Its second element is of class ‘numeric’ and its third element is of class ‘call’. If we zoom in on the third element of the above list we get: as.list(my.expr[[1]][[3]]) [[1]]: sin [[2]]: rnorm(10)

Here the first component is of class ‘name’ and the second component is an object of class ‘call’. Working with expressions in this way can be of use in case one has a function testf which calls another function that depends on calculations that occur inside testf, like in the following example: testf <- function(){ n <- rbinom(1,10,0.5) expr <- expression(rnorm(10)) expr[[1]][[2]] <- n x <- eval(expr) x } testf()

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Indeed this could have been achieved in a much simpler manner, such as in the code below. But it’s the idea that counts here. testf <- function(){ n <- rbinom(1,10,0.5) x <- rnorm(n) x }

9.2.3 Functions as lists It may be surprising that we can also transform function objects to list objects. Lets look at a simple function. myf <- function(x,y) { temp1 = x+y temp2 = x*y tmp1/temp2 }

We use the function as.list to print the function as a list. as.list(myf) $x $y [[3]] { temp1 = x + y temp2 = x * y tmp1/temp2 }

The result is a list with three components, when we transform the third component as a list we get:

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as.list( as.list(myf)[[3]] ) [[1]] ‘{‘ [[2]] temp1 = x + y [[3]] temp2 = x * y [[4]] tmp1/temp2

We can even go further, print the second component of the last list as a list. as.list( as.list( as.list(myf)[[3]] )[[2]] ) [[1]] ‘=‘ [[2]] temp1 [[3]] x + y

9.3 Calling R from SAS The SAS system provides many routines for data manipulations and data analysis. It may be hard to convince a ‘long-time’ SAS user to use R for data manipulation or statistics. However, the graphics in R are superior compared to what SAS/GRAPH can offer. Some graphs are unavailable in SAS/GRAPH or very time consuming to program. We will give small examples on how to use R graphs in a SAS session.

9.3.1 The call system and X functions In SAS there are two ways to call external programs, the call system and the X functions. The following example calls Rcmd BATCH, this will start R as a non interactive BATCH session. It runs a specified R file with some plotting functions. Create an R file with some plot statements, the file plotR.R. The code in the file will instruct R to export the graph.

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jpg(filename = "C:\\temp\\Rgraph.jpg") x <- rnorm(100) y <- rnorm(100) par(mfrow=c(2,1)) plot(x,y) hist(y) dev.off()

Then in a SAS session, use the call system function to call an external program. In this case Rcmd BATCH. %let myf = ’"C:\Temp\plotR.R"’; data _null_; command=’Rcmd BATCH ’||&myf ; put command; call system(command); run;

The same example but now using the X function in SAS. %let myf = "C:\Temp\plotR.R"; %let command=Rcmd BATCH &myf ; X &command;

9.3.2 Using SAS data sets and SAS ODS The previous example used internal R data to create the plot. To create R graphs using SAS data sets you can • export SAS data to a text file and import that in R, • import the SAS data set in R directly. The following example creates a small data set in SAS and exports it using proc export. data testdata; input x y; datalines; 1 3 2 6 3 8

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run; proc export data = testdata outfile = "C:\temp\sasdata.csv" REPLACE; run;

Then in the R file plotR2.R we import the data and use the data to create a simple graph. sasdata <- read.csv("C:\\temp\\sasdata.csv") jpeg("C:\\temp\\Rgraph2.jpg") plot(sasdata$x, sasdata$y) dev.off()

Then in SAS we call Rcmd BATCH to run the above R file non interactively. %let myf = ’"C:\Temp\plotR2.R"’; data _null_; command=’Rcmd BATCH ’||&myf ; put command; call system(command); run;

The SAS output delivery system (ODS) is a convenient system to create reports in HTML, PDF or other formats. The ODS takes output from SAS procedures and graphs, together with specific user settings it creates a certain report. The graphs don’t have to be SAS graphs, they could be any graph. Lets use the same dataset testdata as in the previous example. First run the SAS code that calls the R code that creates the graph. %let myf = ’"C:\Temp\plotR2.R"’; data _null_; command=’Rcmd BATCH ’||&myf ; put command; call system(command); run;

When the graphs in R are created and are stored on disc, start the specifications of the SAS ODS:

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ods html file = "sasreport.html"; title ’SAS output and R graphics’; title2 ’a small example’; * Some SAS procedure that writes results in the report; proc means data = Testdata; run; * export the SAS data and call R to create the plot; proc export data = testdata outfile = "C:\temp\sasdata.csv" REPLACE; run; %let myf = ’"C:\Temp\plotR2.R"’; data _null_; command=’Rcmd BATCH ’||&myf ; put command; call system(command); run; * insert additional html that inserts the graph that R created; ODS html text = " My Graph created in R "; ODS html text = ""; ODS html close;

9.4 Defaults and preferences in R, Starting R, 9.4.1 Defaults and preferences The function options is used to get and set a wide range of options in R. These options influence the way results are computed and displayed. The function options lists all options, the function getOption lists one specific option and option(optionname = value) sets a certain option. options() $add.smooth [1] TRUE $check.bounds [1] FALSE

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$chmhelp [1] TRUE $continue [1] "+ " $contrasts unordered "contr.treatment"

ordered "contr.poly"

$defaultPackages [1] "datasets" "utils"

"grDevices" "graphics"

"stats"

"methods"

$device [1] "windows" $digits [1] 7 ... ...

One example is the number of digits that is printed, by default the number is seven. This can be increased. sqrt(2) [1] 1.414214 options(digits=15) sqrt(2) [1] 1.41421356237310

See the help file of the function options for a complete list of all options.

9.4.2 Starting R The online help describes precisely which initialization steps are carried out during the start up of R. Enter ?Startup to see the help file. If want to start R and set certain options or attach (load) certain packages automatically then this can be achieved by editing the file Rprofile.site. This file is located in the etc subdirectory of the R installation directory, so something like C:\Program Files\R2.5.0\etc. The following file is just an example file.

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# print extra digits options(digits=10) # papersize setting options(papersize="a4") # to prefer HTML help options(htmlhelp=TRUE) # adding libraries that should be attached library(MASS) library(lattice)

9.5 Creating an R package 9.5.1 A ‘private’ package Once you start working with R, you will soon start creating your own functions. If these functions are used regularly (more than one or two times), it may be useful to collect these functions in a ‘private’ package. I.e. the functions are only used by you and some colleagues, and you want to have the functions in a separate library so that they don’t pollute your workspace. In this case it may be enough to define the functions and save them to an R workspace image, a .RData file. This file is a binary file and can be attached to the R search path. myf1 <- function(x) { x^2 } myf2 <- function(x) { sin(x^2) + x } save(c("myf1", "myf2"), file = "C:\\MyRstuff\\AuxFunc.RData")

When a colleague needs these functions give him the binary file and let him attach it to his R session. attach(C:\\MyRstuff\\AuxFunc.RData")

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9.5.2 A ‘real’ R package When you intend to make your package available for more people or even to the R community by putting it on CRAN you may want to create a ‘real’ R package. I.e. add documentation, create meaningful help files and make it easy to install and use the package. To achieve this, there are certain steps that you need to undertake. This section only gives you a kick in the right direction. For a complete description look at the R manual ‘Writing R Extensions’. Before you can create an R package you should install the following tools first, the tools can be found on http://www.murdoch-sutherland.com/Rtools. • Perl, a scripting language. • Rtools.exe, a collection of command line tools and compilers. • Microsoft HTML help workshop, to create help files. • MikTex, a LaTex and pdftex package. When the tools are installed, your PATH variable should be edited, so that commands can be found. You need to be careful in specifying the order of the directories. For example, if you also have the MAKE utility of Borland then make sure that your system finds the R MAKE first when building R packages. Depending on the installation directories, your path may look like: PATH = C:\Rtools\bin; C:\perl\bin; C:\Rtools\MinGW\bin; C:\Program Files\HTML Help Workshop; C:\Program Files\R\R-2.5.0\bin; C:\texmf\miktex\bin;

A good starting point to create an R package is the function package.skeleton. We create a package ‘Lissajous’ with two functions that plot Lissjous figures to demonstrate the necessary steps. Define the R functions First create a script file that defines the functions that you want to package. In our case we have the following function definitions.

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LissajousPlot <- function(nsteps, a,b) { t <- seq(0,2*pi, l = nsteps) x <- sin(a*t) y <- cos(b*t) plot(x,y,type="l") } LissajousPlot2 <- function(nsteps, tend, a,b,c) { t <- seq(0, tend, l= nsteps) y = c*sin(a*t)*(1 + sin(b*t)) x = c*cos(a*t)*(1 + sin(b*t)) plot(x,y,type="l") }

Test the functions, make sure the functions produce the results you expect. Run the function package.skeleton The function package.skeleton creates the necessary files and sub directories that are needed to build the R package. It allows the user to specify which objects will be placed in the package. Specify a name and location for the package: package.loc = "C:\\RPackages" package.skeleton("Lissajous", path = package.loc, force=T) Creating directories ... Creating DESCRIPTION ... Creating Read-and-delete-me ... Saving functions and data ... Making help files ... Done. Further steps are described in ’C:\RPackages/Lissajous/Read-and-delete-me’.

The above call will put all objects in the current workspace in the package, use the list argument to specify only the objects that you want to put in the package. package.skeleton("Lissajous", path = package.loc, list = c("LissajousPlot", "LissajousPlot2"), force = T )

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If force = T then R will overwrite an existing directory. Note that previously edited DESCRIPTION and Rd help files are overwritten!! If the function has finished, the directory ‘Lissajous’ and some subdirectories are created. Edit and create some files The DESCRIPTION file, is a basic description of the package. R has created a skeleton that the user can edit. We use the following file. Package: Lissajous Type: Package Title: Create Lissajous figures Version: 1.0 Date: 2007-05-09 Author: Longhow Lam Maintainer: Longhow Lam Description: Create Lissajous figures License: no restrictions

This information appears for example when you display the general help of a package.

help(package="Lissajous")

The INDEX file is not created, it is an optional file that lists the interesting objects of the package. We use the file: LissajousPlot LissajousPlot2

Plot a Lissajous figure Plot another Lissajous figure

Create help and documentation The function package.skeleton has also created initial R help files for each function, the *.Rd files in the man subdirectory. R help files need to be written in ‘R documentation’ format. A markup language that closely resembles LaTex. The initial files should be edited to provide meaningful help. Fortunately, the initial Rd files created by R provide a good starting point. Open the files and modify them. When the package is build, these documentation files are compiled to html and Windows help files. Each function should have a help file, it is the help that will be displayed when a user uses the help function. help(LissajousPlot2)

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Build the package Now the necessary steps are completed, the package can be build. Open a DOS box, go to the directory that contains the ‘Lissajous’ directory and run the command: > Rcmd build --binary Lissajous

When the build is successful, you should see the zip file: Lissajous 1.0.zip. Install and use the package In the RGui window go to the menu ‘Packages’ and select ‘Install package(s) from local zip files...’. Then select the Lissajous 1.0.zip file, R will install the package. To use the package, it should be attached to your current R session. library(Lissajous) help(Lissajous) par(mfrow=c(2,2)) LissajousPlot(300,2,5) LissajousPlot(300,14,4) LissajousPlot2(300,10,2,7,5) LissajousPlot2(300,10,100,25,6)

9.6 Calling R from Java The package ‘rJava’ can be used to call java code within R, which is comparable with the technology described in section 6.3. The other way around is also possible, calling R from java code. This is implemented in the JRI package which will also be installed if you install the rJava package. This could become useful when you want to extend your java programs with the numerical power of R, or build java GUI’s around R. This section demonstrates the latter. We will make use of the NetBeans IDE. This is powerful yet easy to use environment for creating java (GUI) applications, it is freely available from www.netbeans.org. So to replicate the example in this section download the following software: • Java development Kit (JDK) • The NetBeans IDE • The R package ‘rJava’

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Figure 9.2: Some Lissajous plots The next figure shows a small application that allows the user to import a text file, create explorative plots and fit a regression model. The NetBeans project and java code files are available from the website of this document. The code is not that difficult. Most of the work is done in the JRI package which contains an ‘REngine’ object that you can embed in your java code. A brief description of the java gui. A global REngine object re is defined and created in the java code. Rengine re = new Rengine(args, false, new TextConsole());

Throughout the java program the object re can be used to evaluate R expressions. For example, if the ‘Import Data’ button is clicked an import file dialog appears that will return a filename, then the following java code is called: String evalstr = "infile <- " + "\"" + filename + "\""; re.eval(evalstr); String impstr = "indata = read.csv(infile)"; re.eval(impstr);

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Figure 9.3: A small java gui that can call R functions. This will cause R to call the read.csv function and create an R object infile. Then if the user click on the ‘Create pairs plot’ button, the user can select the variables that will be plotted in a pairs plot. The java program will run: String filename; filename = "C:/Temp/Test.jpg"; String evalstr = "plotfile <- " + "\"" + filename + "\""; re.eval(evalstr); re.eval("jpeg(plotfile, width=550, height=370)"); re.eval("pairs(indata[colsel])"); re.eval("dev.off()");

So the REngine object re is used to evaluate the pairs function and store the result in a jpeg file. This jpeg file is picked up by the java gui (in a JLabel object), so that it is visible. Then when the user clicks on ‘Fit Model’, a dialog will appear where the user selects the response and the regression variables. The R engine is called to fit the linear regression model. The output is displayed in the results window.

9.7 Creating fancy output and reports The R system contains several functions and methods that facilitate the user to create fancy output and reports. First a short overview of these methods, then some examples.

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• Instead of the normal output to screen, the function sink redirects the output of R to a connection or external file. • The package ‘xtable’ contains functions to transform an R object to an xtable object which can then be printed to HTML or LATEX. • The package ‘R2HTML’ contains functions to create HTML code from R objects. • The functions jpeg and pdf (see section 7.2.4) export R graphics to external files in jpeg and pdf format. These files can then be included in a web page or document. • Sweave is a tool that can process a document with chunks of R code, see [15]. It parses the document, evaluates the chunks of R code and puts the resulting output (text and graphs) back in the document in such a way that the resulting document is in its native format. The formats that are implemented are LATEX, HTML and ODF (Open Document Format).

9.7.1 A simple LATEX-table In a monthly report that is created in LATEX, the output of a linear regression in R is needed. ## load the xtable package library(xtable) ## specify the file that will contain the regression output in Latex format mydir = "C:\\Documents and Settings\\Longhow\\Mijn Documenten\\R\\RCourse\\" outfile <- paste(mydir,"carslm.tex",sep="") ## Fit a linear regression lm.out <- lm(Price ~ Mileage + Weight + HP, data = cars) ## transform the regression output object to an xtable object ## add a label so that the table can be referenced in Latex lm.out.latex <- xtable( lm.out, caption = "Regression output", label = "tab001", type = "latex" ) ## sink the xtable object to the latex file. sink(outfile) print(lm.out.latex) ## redirect output to normal screen

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sink()

Once the latex file has been created it can be imported in the the LATEXreport with the input command in latex. See Table 9.1. Estimate Std. Error t value (Intercept) 4236.9773 7409.1846 0.57 Mileage −161.5201 146.5253 −1.10 Weight 2.7349 1.6323 1.68 HP 36.0914 18.5871 1.94

Pr(>|t|) 0.5697 0.2750 0.0994 0.0572

Table 9.1: Regression output

9.7.2 An simple HTML report A small demonstration of Sweave. Every month you need to publish a report that includes some summary statistics and a graph on your local intranet site. Create a file, say datareport. Treat this file as a ‘normal’ HTML file. data report

Monthly summary of input data


Data summary  of  sales data from this month <>= out <- var(cars[c("Price", "Weight", "Mileage")]) out <- xtable(out,caption="Correlation of this months price data") print(out,type="HTML") @ A graph of the data <>= pairs(cars[c("Price", "Weight", "Mileage")]) @

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The chunks of R code start with << some options >>= and end with an @. There are a few options you can set. • echo = FALSE, the R statements in the chunk are not put in the output. Useful when some R statements need to run, for example importing or manipulating data, but need not to be visible in the final report. • results=hide, will hide any output. However, it will generate the R statements in the final document when the echo option is not set to FALSE. • fig=TRUE, create a figure in the report when the R code contains plotting commands. Save the file when you are ready and use Sweave in R to process this file. library(R2HTML) mydir = "C:\\Documents and Settings\\Longhow\\Mijn Documenten\\R\\RCourse\\" myfile <- paste(mydir,"data_report",sep="") Sweave(myfile, driver=RweaveHTML) Writing to file data_report.html Processing code chunks ... 1 : term Robj 2 : term Robj png file data_report.html is completed

The result is an ordinary HTML file that can be opened by a web browser.

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Bibliography [1] Longhow Lam, A guide to Eclipse and the R plug-in StatET. www.splusbook.com, 2007. [2] Diethelm W¨ urtz, “S4 ‘timedate’ and ‘timeseries’ classes for R,” Journal of Statistical Software. [3] Robert Gentleman and Ross Ihaka, “Lexical scope and statistical computing,” Journal of Computational and Graphical Statistics, vol. 9, p. 491, 2000. [4] W. N. Venables and B. D. Ripley, S Programming. Springer, 2000. [5] D. Samperi, “Rcpp: R/C++ interface classes, using c++ libraries from R,” 2006. [6] P. Murrell, R Graphics. Chapman & Hall, 2005. [7] Hadley Wickham, ggplot2: Elegant Graphics for Data Analysis. Springer, 2009. [8] Leland Wilkinson, The Grammar of Graphics. Springer, 2005. [9] W. N. Venables and B. D. Ripley, Modern Applied Statistics with S. Springer, September 2003. [10] J. Maindonald and J. Braun, Data Analysis and Graphics Using R: An Examplebased Approach. Cambridge University Press, 2007. [11] T. Hastie , R. Tibshirani , J. H. Friedman, The Elements of Statistical Learning . Springer, 2001. [12] M. Prins and P. Veugelers, “The european seroconverter study and the tricontinental seroconverter study. comparison of progression and non-progression in injecting drug users with documented dates of hiv-1 seroconversion.,” AIDS, vol. 11, p. 621, 1997. [13] T. M. Therneau and P. M. Grambsch, Modeling Survival Data: Extending the Cox Model. Springer, 2000. [14] Douglas M. Bates, Donald G. Watts, Nonlinear regression analysis and its applications. Wiley-Interscience, 2007. [15] Friedrich Leisch, “Sweave user manual,” 2006.

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Index Accelerated failure time model, 164 aggregate, 60 apply, 86 area under ROC, 157 array, 35 arrows, 119 as.difftime, 26 as.list, 41 attributes, 62 axes, 121 axis, 121

conflicts, 15 control flow, 77 Cox proportional hazard model, 164 csv files, 42 cumulative sum, 49 curve, 104 cut, 68 data frames, 35 databases, 45 debug, 82 debugging, 80 delimited files, 42 deriv, 178 difftime, 26 dim, 32 double, 19 duplicated, 50

bar plot, 107 binning, 162 break, 80 browser, 83 by, 90 C, 92 c, 28 calls, 189 cars, example data, 54 cbind, 56 character, 22 character manipulation, 63 chol, 34 Churn analysis, 164 coarse classification, 162 color palette, 117 color symbols, 117 compiled code, 92 compilers, 95 complex, 21 concordant, 158 conditioning plots, 123, 130 conflicting objects, 15

eclipse, 16 eval, 189 Excel files, 44 expressions, 189 Facetting, 133 factor, 22 factor variables, 152 FALSE, 21 figure region, 114 font, 117 for, 79 formula objects, 141 Fortran, 92 free variables, 75 glm, 155 Graphical Devices, 110

209

Index

Index

grep, 65 gsub, 67

mathematical expressions in graphs, 120 Mathematical operators, 29 matrix, 31 merge, 59 model diagnostics, 146 mtext, 120 multicollinearity, 149 multiple plots per page, 115

head, 55 help, 11 help, 19 HTML, 204 if, 77 ill-conditioned models, 174 import data, 42 integer, 20 is.infinite, 27 is.na, 27 is.nan, 27

NA, 27 NaN, 27 nchar, 64 Non linear regression, 170 NULL, 28 object oriented programming, 180 ODBC, 45 option, 196 order, 50 ordered factors, 24

join, 59 jpeg, 112 Kendall’s tau-a, 158 language objects, 189 lapply, 87 Latex, 204 layout, 115 layout.show, 115 lazy evaluation, 76 legends, 120 length, 49 level, 23 levels, 23 lexical scope, 75 line type, 117 line width, 117 Linear regression, 142 lines, 119 lists, 38 local variables, 73 logical, 21 logistic regression, 154 loops, 77 low level plot functions, 119 lrm, 159

package, 13 package creation, 198 paste, 64 pie plot, 107 plot, 103 plot region, 114 polynomial contrast, 152 POSIXct, 25 POSIXlt, 25 predictive ability, 158 preferences, 196 probability distributions, 139 proc.time, 85 ragged arrays, 89 random sample, 139 rbind, 57 rbind.fill, 58 read.table, 42 Receiver Operator curve, 157 Recycling, 29 regexpr, 65 regular expressions, 65 rep, 31

margins, 114 masked objects, 15

210

Index

Index

repeat, 80 replacing characters, 67 reports, 204 reshape, 61 reshape package, 58 return, 75 round, 29

tail, 55 tapply, 89 terms, 143 text files, 42 time-series, 37 Tinn-R, 17 titles, 103 traceback, 80 transpose, 34 treatment contrast, 152 tree models, 160 Trellis plots, 123 TRUE, 21 tsp, 38 typeof, 19

S3 classes, 180 S4 classes, 180 sample, 139 sapply, 87 scan, 44 scoping rules, 75 search, 13 search path, 13 segments, 119 sensitivity marix, 177 sequences, 30 sessionInfo, 14 singular value decomposition, 151, 177 solve, 34 Somer’s D, 158 sort, 50 stack, 61 stacking data frames, 57 start up of R, 197 statistical summary functions, 135 stop, 81 str, 41 strptime, 25 strsplit, 68 structure, 41 sub, 67 subset, 56 subset, 56 substring, 64 survreg, 169 svd, 34 Sweave, 204 switch, 78 symbols, 117 Sys.time, 27 system.time, 85

unique, 50 variance inflation factors, 151 vector, 28 vector subscripts, 47 vif, 151 warning, 81 while, 79 working directory, 12 workspace image, 12

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An introduction to R - cran.r

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