lme4

I suspect the answer to this question is rather simple and I just don't know what it is. Long story short, I want to display a dotplot of the random intercepts and slopes from a model I am estimating. I'm using the ggCaterpillar function helpfully introduced here . However, this function, as well as the standard dotplot from lattice, sort the ensuing graph by decreasing order of the random intercept. I would like to sort the graph by increasing value of the random effect (either alphabetical or numerical). Consider this minimum working example that comes standard in the lme4 package, along

More than 1 year ago (Feb 2013), I had used lmer to run a mixed effect model involving a binomial outcome with the following command: nl3.lmer <- glmer( cul.bi ~ food.act + where + intlan + inter.cul + via.m + via.h + (1|Id), data=drm, family=binomial) Everything had worked fine, without any error or warning messages and I had presented my results. When trying to look at it today I ran into two problems: 1) summary(nl3.lmer) Length Class Mode 1 mer S4 ...instead of the usual summary. Is it because of the change of class from a mer object to a merMod object? I read something about that change

I have a, what I thought, really simple question. In a longitudinal experiment with a group of participants has everyone rated everyone else on, let's say, 10 variables (e.g. "This person is likeable.", "This person is dull." and so on) at 7 different times. If i want to get some sort of perceiver and target variance for one variable/response I'd use: lmer(scale(Var1) ~ (1|target) + (1|perceiver), data= subset(x, time_point == 1)) Here we have a dependent variable "Var1" of a dataframe "x" with the specification of the 1st time_point (which is also a variable of x). So far so good, this works

I want to run a mixed model (using lme4::lmer ) on 60M observations of the following format; all predictor/dependent variables are categorical (factors) apart from the continuous dependent variable tc ; patient is the grouping variable for a random intercept term. I have 64-bit R and 16Gb RAM and I'm working from a central server. RStudio is the most recent server version. model <- lmer(tc~sex+age+lho+atc+(1|patient), data=master,REML=TRUE) lho sex tc age atc patient 18 M 16.61 45-54 H 628143 7 F 10.52 12-15 G 2013855 30 M 92.73 35-44 N 2657693 19 M 24.92 70-74 G 2420965 12 F 17.44 65-69 A

I have a dataset with 142 data entries: 121 individuals measured on two occasions (two years, before and after treatment, Year = 0 or 1), in the second year 46 individuals were in treated plots and the rest were in control plots (treatment = 0 or 1). Here's some example data: ID <- c("480", "480", "620", "620","712","712") Year <- c("0", "1", "0", "1","0", "1") Plot <- c("14", "14", "13", "13","20","20") Treat <- c("0", "0", "0", "1", "0", "1") Exp <- c("31", "43", "44", "36", "29", "71") ExpSqrt <- c("5.567764", "6.557439", "6.633250", "6.000000", "5.385165", "8.426150") Winter <- data.frame

I am trying to predict values over time (Days in x axis) for a glmer model that was run on my binomial data. Total Alive and Total Dead are count data. This is my model, and the corresponding steps below. full.model.dredge<-glmer(cbind(Total.Alive,Total.Dead)~(CO2.Treatment+Lime.Treatment+Day)^3+(Day|Container)+(1|index), data=Survival.data,family="binomial") We have accounted for overdispersion as you can see in the code (1:index). We then use the dredge command to determine the best fitted models with the main effects (CO2.Treatment, Lime.Treatment, Day) and their corresponding interactions.