R Programming Tutorials. Courses. AVML 2012: <code>ggplot2</code> Introducing R. The purpose of these notes, an update of my 1992 handout Introducing S-Plus, is to provide a quick introduction to R, particularly as a tool for fitting linear and generalized linear models. Additional examples may be found in the R Logs section of my GLM course. R is a powerful environment for statistical computing which runs on several platforms. These notes are written specially for users running the Windows version, but most of the material applies to the Mac and Linux versions as well. 1.1 The R Language and Environment R was first written as a research project by Ross Ihaka and Robert Gentleman, and is now under active development by a group of statisticians called 'the R core team', with a home page at www.r-project.org.
R was designed to be 'not unlike' the S language developed by John Chambers and others at Bell Labs. R is available free of charge and is distributed under the terms of the Free Software Foundation's GNU General Public License. 1.2 Bibliographic Remarks. RSS R Introduction Course. Getting Started with Mixed Effect Models in R. Analysts dealing with grouped data and complex hierarchical structures in their data ranging from measurements nested within participants, to counties nested within states or students nested within classrooms often find themselves in need of modeling tools to reflect this structure of their data. In R there are two predominant ways to fit multilevel models that account for such structure in the data. These tutorials will show the user how to use both the lme4 package in R to fit linear and nonlinear mixed effect models, and to use rstan to fit fully Bayesian multilevel models. The focus here will be on how to fit the models in R and not the theory behind the models.
For background on multilevel modeling, see the references. [1] This tutorial will cover getting set up and running a few basic models using lme4 in R.Future tutorials will cover: To install lme4, we just run: install.packages("lme4") library(devtools)install_github("lme4", user = "lme4") AIC(MLexamp) AIC(MLexamp.2) AIC(MLexamp.3) R Tutorials--Multiple Regression. Preliminaries Model Formulae If you haven't yet read the tutorial on Model Formulae, now would be a good time!
Statistical Modeling There is not space in this tutorial, and probably not on this server, to cover the complex issue of statistical modeling. For an excellent discussion, I refer you to Chapter 9 of Crawley (2007). Here I will restrict myself to a discussion of linear modeling. Glm( ) for generalized linear models (covered in another tutorial) gam( ) for generalized additive models lme( ) and lmer( ) for linear mixed-effects models nls( ) and nlme( ) for nonlinear models and I'm sure there are others I'm leaving out My familiarity with these functions is "less than thorough" (!)
Warning: an opinion follows! Preliminary Examination of the Data For this analysis, we will use a built-in data set called state.x77. > state.x77 # output not shown > str(state.x77) # clearly not a data frame! If you are unclear on what these variables are, or want more information, see the help page: ? R Language Tutorials -- EndMemo.