lme4

I need to extract the standard error of variance component from the output of lmer . library(lme4) model <- lmer(Reaction ~ Days + (1|Subject), sleepstudy) The following produces estimates of variance component : s2 <- VarCorr(model)$Subject[1] It is NOT the standard error of the variance. And I want the standard error . How can I have it ? EDIT : Perhaps I am unable to make you understand what I meant by "standard error of the variance component". So I am editing my post . In Chapter 12 , Experiments with Random Factors , of the book Design and Analysis of Experiments , by Douglas C.

When you have a multilevel model with lots of factors and interactions the size of the correlation of fixed effects matrix can become quite big and unclear. I can use the symbolic.cor=T parameter in the print method to make a clearer print of the summary like below: ratbrain <- within(read.delim("http://www-personal.umich.edu/~bwest/rat_brain.dat"), { treatment <- factor(treatment, labels = c("Basal", "Carbachol")) region <- factor(region, labels = c("BST", "LS", "VDB")) }) print(mod<-lmer(activate ~ region * treatment + (0 + treatment | animal),ratbrain),symbolic.cor=T) This plots a somewhat

I would like to make an interaction plot to visually display the difference or similarity in slopes of interaction of a categorical variable (4 levels) and a standardized continuous variable from the results of a regression model. with(GLMModel, interaction.plot(continuous.var, categorical.var, response.var)) Is not what I am looking for. It produces a plot in which the slope changes for each value of the continuous variable. I'm looking to make a plot with constant slopes as in the following plot: Any ideas? I fit a model of the form fit<-glmer(resp.var ~ cont.var*cat.var + (1|rand.eff) ,

I am estimating random effects logit model using glmer and I would like to report Marginal Effects for the independent variables. For glm models, package mfx helps compute marginal effects. Is there any package or function for glmer objects? Thanks for your help. A reproducible example is given below mydata <- read.csv("http://www.ats.ucla.edu/stat/data/binary.csv") mydata$rank <- factor(mydata$rank) #creating ranks id <- rep(1:ceiling(nrow(mydata)/2), times=c(2)) #creating ID variable mydata <- cbind(mydata,data.frame(id,stringsAsFactors=FALSE)) set.seed(12345) mydata$ran <- runif(nrow(mydata

I am running a multi-level model. I use the following commands with validatedRS6 as the outcome, random as the predictor and clustno as the random effects variable. new<-as.data.frame(read.delim("BABEX.dat", header=TRUE)) install.packages("lme4") library(lme4) model1<- glmer(validatedRS6 ~ random + (1|clustno), data=new, family=binomial("logit"), nAGQ = 1L) However, I get the following error Error in do.call(new, c(list(Class = "glmResp", family = family), ll[setdiff(names(ll), : 'what' must be a character string or a function I have absolutely no idea what has gone wrong and have searched the