lmer

Is it possible to plot the random intercept or slope of a mixed model when it has more than one predictor? With one predictor I would do like this: #generate one response, two predictors and one factor (random effect) resp<-runif(100,1, 100) pred1<-c(resp[1:50]+rnorm(50, -10, 10),resp[1:50]+rnorm(50, 20, 5)) pred2<-resp+rnorm(100, -10, 10) RF1<-gl(2, 50) #gamm library(mgcv) mod<-gamm(resp ~ pred1, random=list(RF1=~1)) plot(pred1, resp, type="n") for (i in ranef(mod$lme)[[1]]) { abline(fixef(mod$lme)[1]+i, fixef(mod$lme)[2]) } #lmer library(lme4) mod<-lmer(resp ~ pred1 + (1|RF1)) plot(pred1,

I'm trying to understand the function lmer. I've found plenty of information about how to use the command, but not much about what it's actually doing (save for some cryptic comments here: http://www.bioconductor.org/help/course-materials/2008/PHSIntro/lme4Intro-handout-6.pdf ). I'm playing with the following simple example: library(data.table) library(lme4) options(digits=15) n<-1000 m<-100 data<-data.table(id=sample(1:m,n,replace=T),key="id") b<-rnorm(m) data$y<-rand[data$id]+rnorm(n)*0.1 fitted<-lmer(b~(1|id),data=data,verbose=T) fitted I understand that lmer is fitting a model of the form

I am new with mixed effect models and I need your help please. I have plotted the below graph in ggplot: ggplot(tempEf,aes(TRTYEAR,CO2effect,group=Myc,col=Myc)) + facet_grid(~N) + geom_smooth(method="lm",se=T,size=1) + geom_point(alpha = 0.3) + geom_hline(yintercept=0, linetype="dashed") + theme_bw() However, I would like to represent a mixed effects model instead of lm in geom_smooth , so I can include SITE as a random effect. The model would be the following: library(lme4) tempEf$TRTYEAR <- as.numeric(tempEf$TRTYEAR) mod <- lmer(r ~ Myc * N * TRTYEAR + (1|SITE), data=tempEf) I have included

I fitted a model in R with the lmer() -function from the lme4 package. I scaled the dependent variable: mod <- lmer(scale(Y) ~ X + (X | Z), data = df, REML = FALSE) I look at the fixed-effect coefficients with fixef(mod) : > fixef(mod) (Intercept) X1 X2 X3 X4 0.08577525 -0.16450047 -0.15040043 -0.25380073 0.02350007 It is quite easy to calculate the means by hand from the fixed-effects coefficients. However, I want them to be unscaled and I am unsure how to do this exactly. I am aware that scaling means substracting the mean from every Y and deviding by the standard deviation. But both, mean

The qqmath function makes great caterpillar plots of random effects using the output from the lmer package. That is, qqmath is great at plotting the intercepts from a hierarchical model with their errors around the point estimate. An example of the lmer and qqmath functions are below using the built-in data in the lme4 package called Dyestuff. The code will produce the hierarchical model and a nice plot using the ggmath function. library("lme4") data(package = "lme4") # Dyestuff # a balanced one-way classiï¬cation of Yield # from samples produced from six Batches summary(Dyestuff) # Batch is