similar to: standardized beta weights for lmer models

Dear Sir/madam, I use lmer to extract model in your package of lme4. It seems works well. But the problem is when I use anova/summary the extracted model, no p-value is shown at all. In previous version(nlme), I mainly use p-value to judge which term is significant or not, and then make a decision to keep this term or not. Does it means that sth wrong with my installation of package/R? or you use

Dear colleagues: I am working with binary mixed models using lme4 package and everything works fine until I try to spatialize the fitted models using grids predictors. I have used the "predict" function from RASTER package and the RSAGA functionalities, but always I have the same error: "Error in object$xlevels : $ operator not defined for this S4 class". I think the

Hello all, I using lmer to develop a mixed effects model. I start with an overly parameterized model (as suggested in Zuur et al. Mixed Effects Models and Extension in Ecology with R) that looks something like this: m1 <- lmer( Y ~ aS + bS + c + d + e + (c|SpeciesId) + (d|SpeciesId) + (e|SpeciesId)) aS and bS are species level predictors an so do not vary within a SpeciesId. However, c, d, and

Hi- I want to fit a model with crossed random effects and heteroskedastic level-1 errors where inferences about fixed effects are of primary interest. The dimension of the random effects is making the model computationally prohibitive using lme() where I could model the heteroskedasticity with the "weights" argument. I am aware that the weights argument to lmer() cannot be used to

Hi all, I am trying to run a linear mixed effect models in lmer() from the lme4 package using the weights option. I am using the R version 2.7.2 (2008-08-25) and lmer version in lme4_0.999375-26, which I think it is the latest version! I am getting and error message when I add the option "weights" in the lmer function. This is the error message I get "Error en

I originally sent this to Doug Bates but have received no reply yet so I thought I would expand to a wider source. I've been trying to estimate linear mixed effect models in lmer() from the lme4 package using the weights option. The help and code for lmer() suggest to me that this is implemented but I can't seem to get it to do anything with weights = , no error message reported it

Dear R users, I want to draw standard error lines for the predicted regression line estimated by logistic regression using lmer. I have two predictors: cafr and its quadratic form I(cafr^2), where cafr is a variable centered around the mean of original variable. Now, the estimated value from the fitted model will be, (model@X)%*%fixef(model) In the logit scale, the mean sum of square from fitted

Hello, This is a follow up question to my previous one http://tolstoy.newcastle.edu.au/R/e4/help/08/02/3600.html I am attempting to model relationship satisfaction (MAT) scores (measurements at 5 time points), using participant (spouseID) and couple id (ID) as grouping variables, and time (years) and conflict (MCI.c) as predictors. I have been instructed to include random effects for the

I run 9 WLS regressions in R, with 7 predictors each. What I want to do now is compare: (1) The strength of predictors within each model (assuming all predictors are significant). That is, I want to say whether x1 is stronger than x2, and also say whether it is significantly stronger. I compare strength by simply comparing standardized beta weights, correct? How do I compare if one predictor is

(This time with the r-help in the recipients...) Be careful when mixing lme4 and lmerTest together -- lmerTest extends and changes the behavior of various lme4 functions. >From the help page for lme4-anova (?lme4::anova.merMod) > ?anova?: returns the sequential decomposition of the contributions > of fixed-effects terms or, for multiple arguments, model >

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply

Hi,   I am trying to use the lmer function from the lme4 package in R 2.8.0. to fit a generalized mixed-effects model for a dependent variable with a binomial distribution (for more info on my experiment, look below). However, I encounter a major problem: How is it possible to find the general test statistic and see the relative importance of the predictors? The methods which I found described in

Hello, and sorry for asking a question without the data - hope it can still be answered: I've run two things on the same data: # Using lme: mix.lme <- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| group, data = mydata) # Using lmer mix.lmer <- lmer(DV ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = mydata) lme provided an output (fixed effects and random

R-experts: In 2002, Hans Van Houwelingen et al. published a tutorial on how to do meta-regression in Statistics in Medicine. They used the classic BCG dataset of Colditz to demonstrate correct methodology and computed the results using PROC MIXED in SAS. In trying to duplicate the results presented in this paper, I have discovered that I can reproduce certain items with lmer but not

Dear R users, Prof. Ripley just corrected my understanding of the use of weights in glm, which I thought would allow me to correctly use lmer. However I'm still having problems. My data takes the form of # of infected and uninfected individuals that were measured over time under different treatments. I'm using lmer to adjust for the repeated measures over time. In fitting the

Hi, I try to make a model using lmer, but the weigths is not accept. m1<-lmer(ocup/total~tempo+(tempo|estacao),family=binomial,weights=total) Erro em lmer(ocup/total ~ tempo + (tempo | estacao), family = binomial, : object `weights' of incorrect type I dont understand why this error, with glm this work. the total object is a vector. Any idea? Thanks Ronaldo -- God is subtle, but

Hi all, I've now got a problem with some modified lmer code (function lmer1 pasted at end) - I've made only three changes to the lmer code (marked), and I'm not really looking for comments on this function, but would like to know why execution of the following commands that use it almost invariably (but not quite predictably) leads to the R session terminating. Here's the command

Hello R users! I've been experimenting with lmer to estimate a mixed model with a dichotomous dependent variable. The goal is to fit a hierarchical model in which we compare the effect of individual and city-level variables. I've run up against a conceptual problem that I expect one of you can clear up for me. The question is about random effects in the context of a model fit with a

It seems that the standardized deviance residulas, that one gets on plots of a glm.object x with plot(x) are calculated as r <- residuals(x) s <- sqrt(deviance(x)/df.residual(x)) w <- weights(x) hii <- lm.influence(x)$hat r.w <- if (is.null(w)) r else (sqrt(w) * r) rs <- r.w/(s * sqrt(1 - hii)) This implies that, for example, for binomial B(ni,pi) data the devaince residials

Perhaps I am not looking in the right place, but I am looking for a way to use lmer() to run a multilevel model that incorporates sampling weights. I have used the Lumley survey package to use sampling weights in the past, but according to post I found online from Thomas Lumley in mid-2012, R is currently not equipped to be able to do this. His post is here: