similar to: Question on structuring variances using the lme4 package

Hello and thanks in advance I am running a glm in R the code is as follows with residual diagnostic code below model4<-glm(Biomass~(Treatment+Time+Site)^2, data=bobB, family=quasi(link="log", variance="mu")) par(mfrow=c(2,2)) plot(model2) to test the effect of grazing exclusion of feral horses for a Phd with following factors: Treatment - 3 levels which are grazed

I have a model: mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) # Here, cs and rtr are crossed random effects. cs 1-5 are of type TRUE, cs 6-10 are of type FALSE, so cs is nested in trth, which is fixed. So for cs I should get a fit for 1-5 and 6-10. This appears to be the case from the random effects: > mean( ranef(mod1)$cs[[1]][1:5] ) [1] -2.498002e-16 > var(

I am using package nlme and would like to specify initial values for a linear mixed-effects model to help with convergence. I am trying to specify those initial values using the msScale option under ?control? in the lme() function: lme(Y ~ X1, random= ~ X1|X2, control=list(msScale=lmeScale)) where, (as far as I understand), lmeScale is a function that can take initial values for parameters.

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

Hi all, I’m hoping someone might be able to help me out. Forgive me if my mistake is something simple. I am new to mixed models, new to R, and new to lme4 and am struggling to figure everything out. I have two questions that I am hoping someone can answer. 1) Am I using the correct random structure for my model? 2) Can someone help me figure out what is wrong with my syntax to code for random

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

Dear all, I have tried to consider a correlation structure in lme (package lme4), but without success. I have used something like: > risul<-lme(y~x+ z , data=mydata, random=~ x | g, correlation = corAR1()) but the result is the same as: > risul<-lme(y~x+ z , data=mydata, random=~ x | g). Can anybody help me? Antonella ************************************************** Prof.

Hi all, I have major heterogeneity in variances across labs (100-fold). There is no apparent variance heterogeneity across y-hat. By using lme4 in the following way, am I accounting for the variance differences in labs?: lmer(y ~ fixed1 + covariates + (fixed1|labs)) I'm not sure that it is - I think it is only allowing the means (slopes [conditional means] & intercepts) to differ

Hello R users- I am new to R, and tried searching the archives and literature for an answer to this - please be patient if I missed something obvious. I am fitting a logistic regression model, and would like to include variance functions (specifically the varIdent function). I cannot figure out how to do this either in glmmPQL (or something similar) for the model with random effects, or in glm

Hello. I am fitting a two-level mixed model which assumes equality of variance in the lowest-level residuals across groups. The call is: fit3<-lme(CLnNAR~CLnRGR,data=meta.analysis, + na.action="na.omit",random=~1+CLnRGR|study.code) I want to test the assumption of equality of variances across groups at the lowest level. Can someone tell me how to do this? I know that one

Hi, For further calculations I need to extract indivdual Variances of different random effects from a fitted model. I found out how to extract the correlations (VarCorr(m1)@reSumry$group1) but I was not able to find a way to extract the other components individually. To extract the Residuals I tried: (ranef(m1)@ stdErr) which unfortunately did not work. Thank you very much for your help!

Hi, I was reading the book on "Mixed Effects Models and Extensions in Ecology with R" by Zuur et al. In Section 6.2, an example is discussed where a gamm-model is fitted, with a smoother for time, which differs for each value of ID (4 different bird species). In earlier versions of R, the following code was used BM2<-gamm(Birds~Rain+ID+

Dear R users, I have this 2 models that fit to my data: M3varI <- update (M3, weights=varIdent(form= ~ 1|SITE)) M3AR1<-update(M3,correlation=corAR1()) The first one, updates my M3 so that I can account for the variance structure of random erros. The second one, updates my M3 so that I can account for the correlation structure of random errors. How can I put them toghether in one

Hi R users, I'm here trying to understand correlated residuals in nonlinear estimation. I'm reading/studying the book Bates, D. M. and D. G. Watts, (1988), /Nonlinear regression analysis and its applications/, Wiley, NY. pages 92-94, trying to reproduce the figures and to find out the code in R to perform the necessary calculations. I also consulted Pinheiro and Bates, but without

I have a data set with 50 different x values and 5 values for the sampling variance; each of the 5 sampling variances corresponds to 10 particular x values. I am trying to fit a mixed effect linear model and I'm not sure about the syntax for specifying the fixed variance structure. In Pinheiro's book my situation appears to be similar to the example used for varIdent, where there is a

Hello - I am fairly new to R, (i.e., ability to create functions/write programs insignificant) and was wondering if there might be a convenient way to model the following: I want to fit a gaussian glm to grouped data, while allowing for unequal variances in each of the groups. More specifically, my data set looks something like this: ---------------- data group 1 76 1 2 82 1 3

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

Greetings This is a long email. I'm struggling with a data set comprising 2,278 hydroacoustic estimates of fish biomass density made along line transects in two lakes (lakes Michigan and Huron, three years in each lake). The data represent lakewide surveys in each year and each data point represents the estimate for a horizontal interval 1 km in length. I'm interested in comparing

Hi, How extract the Variance and Covariance Matrices D of random effects and R of error in the lme object? Thanks in advance. Alexandre Galv??o

Three related questions on LMEs and GLMMs in R: (1) Is there a way to fix the dispersion parameter (at 1) in either glmmPQL (MASS) or GLMM (lme4)? Note: lme does not let you fix any variances in advance (presumably because it wants to "profile out" an overall sigma^2 parameter) and glmmPQL repeatedly calls lme, so I couldn't see how glmmPQL would be able to fix the dispersion