lme4

So, I've fitted a linear mixed model with two random intercepts in R: Y = X beta + Z b + e_i, where b ~ MVN (0, Sigma) ; X and Z are the fixed- and random-effects model matrices respectively, and beta and b are the fixed-effect parameters and random-effects BLUPs/conditional modes. I would like to get my hands on the underlying covariance matrix of b , which doesn't seem to be a trivial thing in lme4 package. You can get only the variances by VarCorr , not the actual correlation matrix. According to one of the package vignettes (page 2), you can calculate the covariance of beta: e_i * lambda *

I've been struggling with converting scaled and centered model coefficients from a glmer model back to uncentered and unscaled values. I analysed a dataset using GLMM in the lme4 (v1.1.7) package. It involves the calculation of maximum detection range of acoustic receivers and effect of environmental variables. Sample data: dd <- structure(list(SUR.ID = c(10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L, 10186L,

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

I am attempting to do a power analysis for a mixed-effects model using the development version of lme4 and this tutorial. I notice in the tutorial that lme4 throws a convergence error: ## Warning: Model failed to converge with max|grad| = 0.00187101 (tol = ## 0.001) The same warning comes up when I run the code for my dataset, with: ## Warning message: In checkConv(attr(opt, "derivs"), opt$par, checkCtrl = control$checkConv, : Model failed to converge with max|grad| = 0.774131 (tol = 0.001) The estimates from a regular glmer call with this updated version are also slightly different from when

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

In lm and glm models, I use functions coef and confint to achieve the goal: m = lm(resp ~ 0 + var1 + var1:var2) # var1 categorical, var2 continuous coef(m) confint(m) Now I added random effect to the model - used mixed effects models using lmer function from lme4 package. But then, functions coef and confint do not work any more for me! > mix1 = lmer(resp ~ 0 + var1 + var1:var2 + (1|var3)) # var1, var3 categorical, var2 continuous > coef(mix1) Error in coef(mix1) : unable to align random and fixed effects > confint(mix1) Error: $ operator not defined for this S4 class I tried to google and use

I want to do stepwise regression using AIC on a list of linear models. idea is to use e a list of linear models and then apply stepAIC on each list element. It fails. Hi guys I tried to track the problem down. I think I found the problem. However, I dont understand the cause. Try the code to see the difference between three cases. require(MASS) n<-30 x1<-rnorm(n, mean=0, sd=1) #create rv x1 x2<-rnorm(n, mean=1, sd=1) x3<-rnorm(n, mean=2, sd=1) epsilon<-rnorm(n,mean=0,sd=1) # random error variable dat<-as.data.frame(cbind(x1,x2,x3,epsilon)) # combine to a data frame dat$id<-c(rep(1,10),rep(2,10