lme4

I am working on graphing the predicted values from a multilevel model (using the lme4 package). I am able to do this successfully using the Effect() function. As shown below: library(lme4) library(effects) m1=lmer(price~depth*cut+(1|cut),diamonds) plot(Effect(c("cut","depth"),m1)) But, I want to present these same data as a single plot with a legend. Using ggplots, I can do this; but, I lose the error bars, as shown below: ggplot(data.frame(Effect(c("cut","depth"),m1)), aes(x=depth,y=fit,color=cut,group=cut))+ geom_line() How can I recreate the first plot (with error bars) as a single plot?

I am trying to use the Python statsmodels linear mixed effects model to fit a model that has two random intercepts, e.g. two groups. I cannot figure out how to initialize the model so that I can do this. Here's the example. I have data that looks like the following (taken from here ): subject gender scenario attitude frequency F1 F 1 pol 213.3 F1 F 1 inf 204.5 F1 F 2 pol 285.1 F1 F 2 inf 259.7 F1 F 3 pol 203.9 F1 F 3 inf 286.9 F1 F 4 pol 250.8 F1 F 4 inf 276.8 I want to make a linear mixed effects model with two random effects -- one for the subject group and one for the scenario group. I am

I have a dataset dat2 on which I want to fit a linear mixed-effects model. I used lmer() (package lme4) in the past in complement of pvals.fnc to compute the associated p-values. However, since I reinstalled R 3.0.2 version with the new lme4 (1.0.4) and languageR (1.4) packages I obtain an error about the output of lmer function. It says that the output is not a mer object. Indeed its class is lmeRmod. Here is the code I use: names(dat2)<-c("auc","subj","decod","soa","vis") attach(dat2) mod1 <- lmer(auc ~ decod + (1 | subj)) mod2 <- lmer(auc ~ vis+ (1 | subj)) mod3 <- lmer(auc ~ decod + vis +

I can use gls() from the nlme package to build mod1 with no random effects. I can then compare mod1 using AIC to mod2 built using lme() which does include a random effect. mod1 = gls(response ~ fixed1 + fixed2, method="REML", data) mod2 = lme(response ~ fixed1 + fixed2, random = ~1 | random1, method="REML",data) AIC(mod1,mod2) Is there something similar to gls() for the lme4 package which would allow me to build mod3 with no random effects and compare it to mod4 built using lmer() which does include a random effect? mod3 = ???(response ~ fixed1 + fixed2, REML=T, data) mod4 = lmer(response ~

Does anyone know a way to produce a nice publication quality LaTeX table from an lme4 mer object? Neither the xtable method (package xtable ) nor the latex method (package Hmisc ) know how to deal with mer objects. For example, given this fit: library(lme4) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) Are there any options for producing a nice LaTeX table of the coefficient estimates for both the fixed and random effects? EDIT: Because this is somewhat buried in the comment threads below, note that a community wiki is in development for R LaTeX tables: Tools for making latex