similar to: [nlme] How to calculate standard error of random effects in lme

Hi Everyone,  Sorry to ask what I think is a basic question but I really haven't found my answer yet in the archives.  I am trying to run a mixed effects model in R using the lme package. My experiment is such that I am interested in the effects of Temperature (2 levels) and Species (3 levels) on Growth. I collected individuals from three populations within each species. Because individuals

Hi I am working on a Nested one-way ANOVA. I don't know how to implement R code to test the significance of the random factor My R code so far can only test the fixed factor : anova(lme(PCB~Area,random=~1|Sites, data = PCBdata)) numDF denDF F-value p-value (Intercept) 1 12 1841.7845 <.0001 Area 1 4 4.9846 0.0894 Here is my data and my hand

Hello I have a data set with fixed and random effects, therefore I am using the lme function: lm(y ~ xfixed, random=~1|xrandom, data) After this I want to get the F-values for both the fixed and random predictors. I can easily get the F-value and df for the xfixed predictors (anova()), but how to get the F-value for the xrandom predictors? Thanks in advance. /R

Hi all! I am working with GLMM using the binomial family I use the following codes I dropped no significant terms, refitting the model and comparing the changes with likelihood: G.1<-lmer(data$Ymat~stu+spi+stu*sp1+(1|ber),data=data,family="binomial") G.1b<-lmer(data$Ymat~stu+spi+(1|ber),data=data,family="binomial") anova (G.1,G.2) But, when I want to evaluate the

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

Dear professor, I'm using package nlme, but I can't find a way to specify two uncorrelated random effects. For example, a random intercept and a random slope. In package lme4, we can specify&nbsp;x + (x ll g) to realize, but how in nlme? Thanks! ???????????????????????? Zhen Wang Graduate student, Department of Medical Statistics, School of Public Health, Sun Yat-sen

Hello, I have a dataset concerning slugs. For each slug, the number of pumps per one time slot was counted. The number of pumps follows Bi(30, p) where p is very small, thus could be approximated by Poisson dist. (# of pumps is very often = 0) The slugs were observed during 12 time slots which are correlated in time as AR(1). The time slots are divided into two categories: Resting time

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep

Hi, Is there any similar command to "predict" which can be used with a logistic random effects model? I have run a random effects model using "lme()", and then use "predict.lme()" with no problems. However, I would also like to run a logistic random effects model, and then also run a predict command on the logistic random effects model. If I use "lme()",

I want to analyze binary, multinomial, and count outcomes (as well as the occasional continuous one) for clustered data. The more I search the less I know, and so I'm hoping the list can provide me some guidance about which of the many alternatives to choose. The nlme package seemed the obvious place to start. However, it seems to be using specifications from nls, which does non-linear

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

Hello, how do I obtain standard errors of variances and covariances of the random effects with LME comparable to those of for example MlWin? I know you shouldn't use them because the distribution of the estimator isn't symmetric blablabla, but I need a measure of the variance of those estimates for pooling my multiple imputation results. Regards, Roel.

Hello everyone, I'm quite new to R and am trying to run a logistic model to look at how various measures of boldness in individual animals influences probability of capture, however I also want to include random effects and I'm not sure how to construct a model that incorporates both of these things. Data was collected from 6 different groups of 6 individuals with 10 replicates for

Dear Madam or Sir I am writing you hoping, that you can help me with a problem concerning the output of regressions done with the function lme in R. I would need the standard deviations for intercepts and predictors, but in the output I can only find those for the intercepts. Could it be, that this is my fault? (I am just a beginner with R and multilevel modeling). I am sorry to annoy you with

I have a linear mixed-effects model (from the package nlme) with a random effect; Is there something like an "r squared" for the whole model which I can state? I´d like to kown: How would I do anova for a linear mixed-effects model? Lic. Florencia BonattoUniversidad Nacional de Rio Cuarto, Cordoba. [[alternative HTML version deleted]]

> To sum up, I can't figure out how MLWin calculates the > standardized residuals. But I understand that this is not a > question for the R list. > Nevertheless, it would help if someone could point me to some > arguments why not to use them and stick to the results > obtainable by ranef(). Hi Dirk: Well, it is interesting that mlWin and lmer generate the same exact

Hi all, I''ve been trying to fit a mixed effects model and I''ve been having problems. =>My aim: to know whether states atributes, political parties and individual atributes affect the electoral results of men and women candidates. I use candidates as replications for states and for political parties. =>Response: Percentage of valid votes casted to each

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the

Hi all - R2.0.1, OS X Perhaps while there is some discussion of lme going on..... I am trying to execute a glmm using glmmPQL from the MASS libray, using the example data set from McCullagh and Nelder's (1989, p442) table 14.4 (it happens to be the glmm example for GENSTAT as well). The data are binary, representing mating success (1,0) for crosses between males and females from two