similar to: error messgage in lmer for random intercept and slope model

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

Dear all, I would like to use the residuals in a general linear mixed effect model to diagnose model fit. I know that the resid function has been implemented for linear mixed models but not yet for general linear mixed effects. Is there a way to get them out of lmer fit objects? I tried searching the r-help archive and found nothing. I tried and failed to replicate what (I guessed would be

Dear R help I have a question on the variance of the binomial probit model. I have fitted the following model : > lmer1<-lmer(mp ~ l + op + l*op+ us_lev + bw_lev +(1|tatu) , + family = binomial(link="probit"), + method = 'Laplace', + data = matings, + msVerbose= True) > summary(lmer1) Generalized linear

Dear colleagues, I have a data from a repeated measures design that I'm analysing through a mixed model. Nine independent sampling units (flasks with culture medium with algae) were randomly divided into 3 groups ("c", "t1", "t2"). There is no need for inclusion of the random effect of the intercept, because the nine sample units are homogeneous among each other

Hi there, In multiple regression one way to view R^2 is as (the square of) the correlation between original y's and the estimated y's. Suppose you fit a multilevel model with random intercept for each cluster. Would it be valid to compute an R^2 by using fixed effects plus the group intercepts to reduce the residuals? I suspect this has been done and, given its absence from the lmer

I seem to come to this problem alot, and I can find my way out of it with a loop, but I wish, and wonder if there is a better way. Here's an example (lmer1-5 are a series of lmer objects): bs=data.frame(bic=BIC(lmer1,lmer2,lmer3,lmer4,lmer5)$BIC) rownames(bs)=c('lmer1','lmer2','lmer3','lmer4','lmer5') best=rownames(bs)[bs==min(bs)] > best [1]

Dear all, I'm using the latest version of the package lme4 and R version 2.7.2 (2008-08-25). After I run the model, I get the results of the model (cf. below). Then, I run an ANOVA using the "anova" function and I get the following message "Error in anova(lmer1) : Calculated PWRSS for a LMM is negative". I went trough the R-mailing list and a similar error message was

Dear useRs, I am using mgcv version 1.7-16. When I create a model with a few non-linear terms and a random intercept for (in my case) country using s(Country,bs="re"), the representative line in my model (i.e. approximate significance of smooth terms) for the random intercept reads: edf Ref.df F p-value s(Country) 36.127 58.551 0.644

R experts, I am working with a fairly large data set comprised of 563 rows by 116 columns including several different modes. I have been unable to read in the data set completely using the read.table function and the RGui (i.e. nearly half the total number of rows are missing from the data set along with the column names). The data does read in fully using Tinn-R's Rterm; however, at several

I'm having trouble with 'lmer' and would really appreciate it if anybody could help. I am trying to run generalized linear mixed effect model, and am using 'lmer', but some of the names inside the data do not show up in the summary after I compute the 'lmer'. As a close example of the data I have, the objects are Calls = MM, MB, MM, MM, MN, MNX, MNX, ... -this

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Dear All, I've been using mcmcsamp() successfully with a few different mixed models but I can't get it to work with the following. Is there an obvious reason why it shouldn't work with a model of this structure ? *brief summary of objective: I want to test the effect of no-fishing marine reserves on the abundance of a target species. I have samples at coral reef sites inside and

Running R 2.5.1 and a newly downloaded lme4 package on WinXP I'm trying to work my way through Everitt and Hothorn's "Handbook of Statistical Analyses Using R," c 2006. (No, it's not homework.) Chapter 10 discusses linear mixed effects models for longitudinal data. I've called my long data frame BtheB.long Here's the model from the book, which I run. lmer1 <-

Dear R Community! We analyse the impact of climbing activity on cliff vegetation. During our fieldwork, we recorded 90 Transects in 3 climbing sites. The aim is to see, if the plant cover (response: Cover) is influenced only by crevice availability (predictor: Cracs), or, additional, by the distance to the climbing route (predictor: Distance). Six plots are nested within one Transect

library(ROCR) n <- 1000 fitglm <- function(iteration,intercept,sigma,tau,beta){ x <- rnorm(n,0,sigma) ystar <- intercept+beta*x z <- rbinom(n,1,plogis(ystar)) xerr <- x + rnorm(n,0,tau) model<-glm(z ~ xerr, family=binomial(logit)) *int*<-coef(model)[1] *slope*<-coef(model)[2] # when add error you are suppose to get slightly bias slope. However when I change

0down votefavorite <https://stats.stackexchange.com/questions/296569/how-to-obtain-random-slope-random-intercept-plots-for-categorical-response-varia#> I'm trying to generate a random slope random intercept plot after ordinal regression using the clmmfunction from the ordinal package in R. I have aggression levels which are categorical with six levels. Earlier, I made random intercept

I am wondering if it possible to normalize the slope of a linear regression to its intercept to allow for valid between-group comparisons. Here is the scenario: I need to compare the slopes of biomass increase among NAFO divisions of Northwest Atlantic cod. However, the initial division biomass is a confounding factor that may influence the slope of the regression model. How can I normalize the

Windows Vista R 2.10.1 Does the following use of groupedData and lme produce an analysis with both random intercept and slope, or only random slope? zz<-groupedData(y~time | Subject,data=data.frame(data), labels = list( x = "Time", y = "y" ), units = list( x = "(yr)", y = "(mm)") ) plot(zz)

Dear all, I'm trying to fit a model on ecological data in which I have measured a few biotic and abiotic factors over the course of a few days in several individuals. Specifically, I'm interested in modelling y ~ x1, with x2, x3, and 'factor' as independent variables. Because data suggests both slope and intercept (for y ~x1) might differ between individuals, I'd want to

Dear list members, I am working with a multilevel growth curve, that in its simplest form goes like follows: Yit = Ai + Bi t + eit (the error term is assumed to follow an AR(1) autorregressive process) One major topic in my research is the convergence in the values of Y over time. Thus, I am interested in the relationship between the random effects for the intercept and the slope, and I