Guide to Mixed Models in R. I created this guide so that students can learn about important statistical concepts while remaining firmly grounded in the programming required to use statistical tests on real data.
I want this to be a guide students can keep open in one window while running R in another window, because it is directly relevant to their work. In that spirit of openness and relevance, note that I created this guide in R v 3.1.0 and used the following packages: car v 2.0 MASS v 7.3 lme4 v 1.1 mlmRev v 1.0 agridat v 1.8 MCMCglmm v 2.19 ggplot2 v 0.9.3.1 scapeMCMC v 1.1 A mixed model is similar in many ways to a linear model. It estimates the effects of one or more explanatory variables on a response variable. What do I mean by that? ## Test.ID Observer Relation Aggression Tolerance Season ## 1 1 Charles Same 4 4 Early ## 2 2 Tyler Same 1 34 Early ## 3 3 Michelle Same 15 14 Early ## 4 4 Tyler Same 2 31 Early ## 5 5 Charles Same 1 4 Early ## 6 6 Rhyan Same 0 13 Early ## Loading required package: car. [Nlme-help] model.matrix. Jose Pinheiro (firstname.lastname@example.org)Thu, 19 Aug 1999 12:08:38 -0400 > I would like to extract the model matrix of a lme or nlme object but> I can't quite figure out how.
There are different model matrices in an lme model: one for the fixed effects and another for the random effects. I am not sure what you mean by the model matrix for an nlme model. For storage reasons, the model matrices are not saved with an lme object. You can generated them from the fitted object, but you need to be careful to use the data actually used in the fit and the make sure that the parameterization used to represent factors is the same as the one used in the fit (this is specified using the "contrasts" argument to the options() function). For example, if you fit the model > fmOrth <- lme(distance ~ age * Sex, Orthodont, random = ~ age) with the contrasts > options()$contrasts factor ordered "contr.helmert" "contr.poly" you can get the regression matrix for the fixed effects using Hope this helps, --Jose'
Influence.ME: Tools for Detecting Influential Data in Multilevel Regression Models. Despite the increasing popularity of multilevel regression models, the development of diagnostic tools lagged behind.
Typically, in the social sciences multilevel regression models are used to account for the nesting structure of the data, such as students in classes, migrants from origin-countries, and individuals in countries. The strength of multilevel models lies in analyzing data on a large number of groups with only a couple of observations within each group, such as for instance students in classes.
Nevertheless, in the social sciences multilevel models are often used to analyze data on a limited number of groups with per group a large number of observations. A typical example would be the analysis of data on individuals nested within countries. By nature, only a limited number of countries exists. With this publication, and of course with the software that was available for quite some time, we hope to contribute to a better usage of multilevel regression models.
How to interpret variance and correlation of random effects in a mixed-effects model? R - How to get coefficients and their confidence intervals in mixed effects models. CIs for prediction in GLMMs. X<-runif(100,0,10) f1<-gl(n = 10,k = 10)
GAMM4 CHOLMOD error. Difficulty with lme. Kevin Wright > > Generally, the only way to estimate f1:f2 is if you have all combinations of > data present for these two factors.
Well, he said it was unbalanced, he didn't say how unbalanced -- i.e. it's not clear (to me) whether there are any completely missing cells or not ... 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.  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)