Regression III: Advanced Methods

Transcription

1 Lecture 2: Software Introduction Regression III: Advanced Methods William G. Jacoby Department of Political Science Michigan State University

2 Getting Started with R What is R? A tiny R session Resources Setup under Windows Getting data into R Graphs and Statistical Models 2

3 What is R? A free, open-source implementation of the S language for data analysis and graphics Available for various operating systems (including Linux, Mac and Windows) A complete programming language Supported by a comprehensive help system and a large international community of users Increasingly used in advanced social-science research, as well as in many other disciplines In constant flux Not guaranteed by anyone to be fit for any purpose! 3

4 R Resources The main source for R and everything connected with it is the Comprehensive R Archive Network (CRAN): There one can find: Binary versions of R to download and install The source code Extensive documentation and contributed guides Information about many add-on packages 4

5 Add-on Packages Add-on packages are easily installed from the menus within R (We will use a number of packages in this course) Once installed, the package must be loaded into the current interactive R session Many packages contain datasets. These must also be loaded and attached to be used The number symbol # is used to insert comments R will not read anything after it (only works for a single line) 5

6 Documentation for R Installed as part of the R help system are the following documents: An Introduction to R (about 100 pages). Gives an introduction to the language and how to use R for doing statistical analysis and graphics R Data Import/Export (about 35 pages). Describes the import and export facilities available in R itself or via the foreign package Writing R Extensions (about 75 pages). Covers how to create your own packages, write R help files, etc. There are also various unofficial guides on CRAN under contributed Finally, Fox (2002) provides a great introduction, focusing on the use of R for regression analysis. 6

7 Getting Help in R A number of different types of help are available by clicking the help menu: Documentation on all installed packages is available in a web browser by clicking help html help The official manuals can be loaded in PDF format by clicking help manuals Help about individual functions and objects can also be obtained within R by typing help(data) or?data, for help on something whose name is known help.search( ordinal ), to search all the installed help files for occurrence of a particular text string apropos( stem ), to look for stem in the names of objects available in the current R session If all else fails, use the R-help list: 7

8 Getting set-up Download the installer from CRAN A good way of working with R is: 1. For each project, make a directory containing data and other materials. 2. Make a copy of the Rgui.exe shortcut. Rename the new shortcut. Right-click Properties and replace the entry in the Start In field with the new directory. 3. To use R for the project, double click on the newlycreated shortcut. Data files can be loaded easily, and you can keep projects separate from each other. 4. It is also useful to have a good text editor. Notepad will do, but there are much better alternatives The R plug-in for WinEdt is called RWinEdt An alternative is the Emacs Speaks Statistics (ESS) package 8

9 Using a Text Editor with R It is useful to have a good text editor when working in R. This will facilitate easier preparation of R commands and saving program sessions in R Scripts. Windows Notepad provides minimal editing capabilities, but there are much better alternatives. The WinEdt shareware program (also frequently used with LaTeX) can be configured to work well with R. The RWinEdt plug-in sets everything up, and it is loaded as anr package. An alternative is the Emacs Speaks Statistics (ESS) package A text editor can be loaded into R automatically at the beginning of each session, by adding a statement to the Rprofile. 9

10 Getting data in (1): Entering data directly The concatenate function, c, combines individual cases together into a vector The cbind (columns bind) and rbind (rows bind) functions combine vectors together into a matrix The data.frame function makes the matrix and a data frame object 10

11 Getting data in (2): External datasets For rectangular data in a text file, use read.table(): Mydata<-read.table( dataname.txt, header=true) header=true signifies that the first contains variable names The foreign library imports data files from other formats: use.value.labels=true converts SPSS value labels to categories. If you specify FALSE, all variables will be treated as quantitative All SPSS variable names will be imported in upper case letters 11

12 Re-specifying variables after importing to R To make a numerically coded variable into an unordered factor (categorical variable): To make a numerical variable into an ordered factor: 12

13 Recoding Variables using the recode function in the car package Recoding into a quantitative variable: Recoding into an unordered factor: 13

14 S Modeling Language The S modeling language is convenient in that it has a similar notation for most types of models Model specification generally takes the following form: Response ~ Independent Variables Where the tilde sign (~) is interpreted as regressed on For the general linear model, terms represent additive components as in the regression equation itself Some examples of formulas are: 14

15 Graphs in R All graphs are drawn on a chosen device either until a new device is started, or the device is closed dev.off() Some commonly used graphics devices are postscript( mygraphs.ps ) Necessary for LaTeX pdf( mygraph.pdf ) Necessary for PDF LaTeX trellis.device() - Used for the Lattice graphics system windows() The default graphics device Graphs in R are very flexible. 15

16 A small graph example (1) 16

17 A small graph example (2) Florida votes by county BUSH DADE PALM.BEACH BUCHANAN 17

18 Arc Software (A Brief Look) A user-friendly software package for regression analysis, focusing on regression graphics Available for various operating systems (including Linux, Mac and Windows) Written in the Xlisp-Stat language Software can be downloaded from: Documentation for Arc is contained in the Cook and Weisberg (1999) text Arc reads data from text files with internal formatting commands. Easiest way to prepare data is to copy from examples distributed with the software. Arc is a simple, but extremely powerful, tool for dynamic statistical graphics. 18

19 Preparing Data for Arc Arc can read data in several formats. The easiest way is to load an ASCII text file. Arc will prompt you (via dialog boxes) for additional information about the dataset name, variable names, and so on. Like the read.table function in R, you can include variable names in the first row of a text data file. Variable names will be converted to upper-case, unless they are enclosed in double quote. Arc data files are stored with the extension.lsp. These data files are also ASCII text files, but they include internal formatting commands along with the data. The structure is very simple, and I recommend preparing Arc data files in a text processor before reading them in. Extensions to Arc (available on the web site) can read MS Excel spreadsheets and SAS datasets. 19

20 Output from Arc Arc will respond to some Xlisp-Stat commands, but most interaction with the software occurs through menu selections and dialog boxes. Two different methods for saving printed output: Material in the Arc window can be cut and pasted as usual, using the items from the Edit menu. Lines can be saved automatically to a specified file by selecting Dribble from the Arc menu. Graphical output from Arc is usually saved by cutting and pasting. Instructions for saving Arc graphs in LaTeX format are available on the website for the software. 20

21 Next topics Examining data Transformations 21