lme4

Dear Stack Overflow community, Currently I'm trying to rerun an old data analysis, binomial glmer model, (from early 2013) on the latest version of R and lme4, because I don't have the old versions of R and lme4 anymore. However, I experience similar warning messages as previous threads by dmartin and carine (first warning message) and other threads outside stack overflow (warnings 2 and 3). These warning messages didn't pop up on the earlier version of R and lme4 I used, so it must have something to do with latest updates? A subset of my dataset: df <- structure(list(SUR.ID = structure(c(1L,

I have a mer object that has fixed and random effects. How do I extract the variance estimates for the random effects? Here is a simplified version of my question. study <- lmer(Reaction ~ Days + (1|Subject), data = sleepstudy) study This gives a long output - not too long in this case. Anyway, how do I explicitly select the Random effects: Groups Name Variance Std.Dev. Subject (Intercept) 1378.18 37.124 Residual 960.46 30.991 part of the output? I want the values themselves. I have taken long looks at str(study) and there's nothing there! Also checked any extractor functions in the lme4

I wonder how to fit multivariate linear mixed model with lme4 . I fitted univariate linear mixed models with the following code: library(lme4) lmer.m1 <- lmer(Y1~A*B+(1|Block)+(1|Block:A), data=Data) summary(lmer.m1) anova(lmer.m1) lmer.m2 <- lmer(Y2~A*B+(1|Block)+(1|Block:A), data=Data) summary(lmer.m2) anova(lmer.m2) I'd like to know how to fit multivariate linear mixed model with lme4 . The data is below: Block A B Y1 Y2 1 1 1 135.8 121.6 1 1 2 149.4 142.5 1 1 3 155.4 145.0 1 2 1 105.9 106.6 1 2 2 112.9 119.2 1 2 3 121.6 126.7 2 1 1 121.9 133.5 2 1 2 136.5 146.1 2 1 3 145.8 154.0 2 2 1 102

I've been running linear mixed models using an old version of lme4. Now that I have updated lme4 I'm getting the following error: Error en [[<-.data.frame ( *tmp* , i, value = integer(0)) : replacement has 0 rows, data has 4211 I found in this website an answer that suggests to put all the grouping variables within the data frame specified by the data argument. I've done that but my code still doesn't work. Here it is: msdgtot=glmer(sdg.dens ~ ngbr.trees + (1 + ngbr.trees | factor(species)), data=d.sdg.ngb,family=poisson) Error en [[<-.data.frame ( *tmp* , i, value = integer(0)) : replacement

Does predictInterval() from the merTools package not like nested random effects? For example, using the msleep dataset from the ggplot2 package: library("lme4") library("merTools") library("ggplot2") mod <- lmer(sleep_total ~ bodywt + (1|vore/order), data=msleep) predInt <- predictInterval(merMod=mod, newdata=msleep) Returns an error: Error in '[.data.frame'(newdata, , j) : undefined columns selected This runs fine no problem: mod <- lmer(sleep_total ~ bodywt + (1|vore) + (1|order), data=msleep) predInt <- predictInterval(merMod=mod, newdata=msleep) (Well actually it gives a warning about NA