similar to: p-value for fixed effect in generalized linear mixed model

Dear R-users, I'm new to R, so my apologies if this question doesn't make sense. I've tried the following model in lmer, and it works perfectly: model<-lmer(aphids~densroot+zone+(1|zone/site), family=quasipoisson) But if I try the exact same model with a different variable, totmas, the model looks as follows: model<-lmer(aphids~totmas+zone+(1|zone/site), family=quasipoisson)

Hi I am running some linear and non-linear mixed effect models and would like to do some planned contrasts (a priori contrasts) I have looked in the help and in many forums and it seems possible to do so but don't understand how to write the function and I couldn't find an example in Pinheiro and Bates. lme {nlme} has a contrasts argument but I can't understand how to code it.

Dear group, I tried to run a Repeated Mesures Anova for Mixed effects model and I got a warnning after entering the model specification saying: "Reached total allocation of 254Mb: see help(memory.size)". here is part of the log: *********************************************************** > aphids<-read.table("aphid.txt",header=T) > attach(aphids) > names(aphids)

Dear R Users, May be this message should be directy send to Douglas Bates ... I just want to know if I can use the AIC value given in the output of an lmer model to classify my logistic models. I heard that the AIC value given in GLIMMIX output (SAS) is false because it come from a calculation based on pseudo-likelyhood. Is it the same for lmer ??? thanks, Arnaud Arnaud MOSNIER Biologiste

Hi, I am performing a repeated measures 2-way ANOVA to assess the influence of plant and leaf on aphid fecundity. Fecundity is measured for each aphid on a single leaf. Here is what I typed. wingless <- reshape(Wingless, varying =

Hi, I am trying to fit a model with lmer in R and proc glimmix in SAS. I have simplified my code but I am surprised to see I get different results from the two softwares. My R code is : lmer(y~age_cat + (1|cat),data=fic,family=binomial(link = "logit"), NaGQ=1) My SAS code is : ods output Glimmix.Glimmix.ParameterEstimates=t_estimates; proc glimmix data=tab_psi method=laplace;

Hello. Some months age an e-mail was posted in which a comparison between Glimmix and glm was discussed. I have not been able to find that e-mail on the R archive. Does anyone recall the date of the above e-mail? Thank you very much. ******************************************* Antonio Paredes USDA- Center for Veterinary Biologics Biometrics Unit 510 South 17th Street, Suite 104 Ames, IA 50010

Hi folks, I'm pleased to introduce a new package called ?aphid?, for analysis with profile hidden Markov models in R. The package contains functions for multiple and pairwise sequence alignment for both nucleic acids and proteins (preferably in the DNAbin or AAbin format), model building, parameter optimization (Baum Welch and Viterbi training), plotting, file import & export,

Hi folks, I'm pleased to introduce a new package called ?aphid?, for analysis with profile hidden Markov models in R. The package contains functions for multiple and pairwise sequence alignment for both nucleic acids and proteins (preferably in the DNAbin or AAbin format), model building, parameter optimization (Baum Welch and Viterbi training), plotting, file import & export,

I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying

Hello R-Community, we are running aprogram to fit Non-linear differential equations to Aphid population Data and to estimate the birth and death parameters, here is the code: dat<-data.frame(Time=c(0:60),Cur=c(5,6.2,59,39,38,44,20.4,19.4,34.2,35.4,38.2,48.2,55.4,113.2, 97,112,115,126,136.6,140.6,147.2,151.6,157.8,170,202,210.4,221.2,224.4,248.2,266,

Greetings. I'm a Master's student working on an analysis of herbivore damage on plants. I have a tried running a glm with one categorical predictor (aphid abundance) and a binomial response (presence/absence of herbivore damage). My predictor has four categories: high, medium, low, and none. I used the "ordered" function to sort my categories for a glm. ah <-

Dear R users, I've been trying to find an R package which does the PL estimation of spatial GLMMs especially with the negative binomial model. so it would be something similar to the "proc GLIMMIX" with the PL method in SAS. I've looked up some possible packages related to GLMMs, but it doesn't seem to be anyone using the PL estimation. Thanks for your help! Fei He UCR

I'm trying to fit a generalized mixed effects model to a data set where each subject has paired categorical responses y (so I'm trying to use a binomial logit link). There are about 183 observations and one explanatory factor x. I'm trying to fit something like: (lmer(y~x+(1|subject))) I also tried fitting the same type of model using glmmPQL from MASS. In both cases, I get a

This is a simple problem, but a mystery to me. I'm trying to grab $Family "Scelionidae" from one dataframe and put it into another dataframe occupied with NA in $Family. The result is a "158" ends up there instead of Scelionidae. Simply put fam$Family[1] <- least$Family[1] If I have made a mistake here, can somebody point it out. I've included the simple

Dear R users, I am having problems trying to fit quasipoisson and negative binomials glm. My data set contains abundance (counts) of a species under different management regimens. First, I tried to fit a poisson glm: > summary(model.p<-glm(abund~mgmtcat,poisson)) Call: glm(formula = abund ~ mgmtcat, family = poisson) . . . (Dispersion parameter

Hello R-help, According to a research article that covers the topic I'm analyzing, in Stata, a Poisson pseudo-maximum-likelihood (PPML) estimation can be obtained with the command poisson depvar_ij ln(indepvar1_ij) ln(indepvar2_ij) ... ln(indepvarN_ij), robust I looked up Stata help for the command, to understand syntax and such: www.stata.com/help.cgi?poisson Which simply says

Dear all, I have a quasipoisson glm for which I need confidence bands in a graphic: gm6 <- glm(num_leaves ~ b_dist_min_new, family = quasipoisson, data = beva) summary(gm6) library('VIM') b_dist_min_new <- as.numeric(prepare(beva$dist_min, scaling="classical", transformation="logarithm")). My first steps for the solution are following: range(b_dist_min_new)

Dear all, I am analyzing counts of seabirds made from line transects at sea. I have been fitting Poisson and negative binomial distributions to the data using the goodfit function from the vcd library. I would also like to evaluate how well a quasi-poisson distribution fits the data. However, none of the potentially suitable functions I have identified (goodfit(vcd), fitdistr(MASS),

Hi, I would like to obtain the dispersion parameter for a quasipoisson model for later use in calculating QAIC values for model comparison.Can anyone suggest a method of how to go about doing this? The idea I have now is that I could use the residual deviance divided by the residual degrees of freedom to obtain the dispersion parameter. The residual deviance is available in the summary