similar to: glmmPQL help

Dear R Community, I?m relatively new in the field of R and I hope someone of you can help me to solve my nerv-racking problem. For my Master thesis I collected some behavioral data of fish using acoustic telemetry. The aim of the study is to compare two different groups of fish (coded as 0 and 1 which should be the dependent variable) based on their swimming activity, habitat choice, etc.

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

Dear R users, can anybody explain me why the function glmmPQL(.) behaves in different ways, depending on the number of measurements/individuals you use? To show you this, I generated two examples. The first one includes 20 indivduals with each 100 repeated measurements (binary response), the second one includes 40 individuals. The 'individuals' differ only in different x values. I

Dear all, I want to evaluate several generalized linear mixed models, including the null model, and select the best approximating one. I have tried glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result in similar parameter estimates but fairly different likelihood estimates. My questions: 1- Is it correct to calculate AIC for comparing my models, given that they use

I have a longitudinal data analysis project. There are 10 observations on each of 15 units, and I'm estimating this with randomly varying intercepts along with an AR1 correction for the error terms within units. There is no correlation across units. Blundering around in R for a long time, I found that for linear/gaussian models, I can use either the MASS method glmmPQL (thanks to

Greetings R-ers, A colleague and I have been exploring the behaviour of glmmPQL in R and S-PLUS 6 and we appear to get different results using the same code and the same data set, which worries us. I have checked the behaviour in R 1.7.1 (MacOS 9.2) and R. 1.9.0 (Windows 2000) and the results are the same, but differ from S-PLUS 6 with the latest Mass and nlme libraries (Windows XP). Here

Dear all, I have two questions concerning model simplification in GlmmPQL, for for random and fixed effects: 1. Fixed effects: I don't know if I can simply specify anova(model) and trust the table that comes up with the p value for each variable in the fixed effects formula. I have read that the only way to test for fixed effects is to do approximate wald tests based on the standard errors

I fit the following model using glmmPQL from MASS: fit.glmmPQL <- glmmPQL(ifelse(class=="Disease",1,0)~age+x1+x2,random=~1|subject,family=binomial) summary(fit.glmmPQL) The response is paired (pairing denoted by subject), although some subjects only have one response. Also, there is a perfect positive correlation between the paired responses. x1 and x2 can and do differ within each

Hi, I’m sorry, I know that it is a recurrent question but I have not been able to find the response in the Rhelp archives. I think my data require the use of the glmmPQL function but I do not know how to make the model selection. Since the AIC and log-likelihood are apparently meaningless, how can we select the parameters for a model and compare the models to find which one fits best the data?

Dear R-users, I have conducted an experiment with a 2*2*2 factorial within-subjects design. All factors are binary and the dependent measure is a frequency of successes between 0 and 4. Treating this as a normally distributed variable, I would perform a repeated-measures ANOVA as follows: > aov(y ~ A*B*C + Error(subj/(A+B+C))) but since the distribution of the dependent measure is clearly

Hi, I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER). I wanted to fit the following model:

I am analysing some binary data with a mixed effects model using glmmPQL. I am aware that I cannot use the AIC values to help me find the minimum adequate model so how do I perform likelihood ratio tests? I need to fix on the minimum adequate model but I'm not sure of the proper way to do this. Thank you very much, Elizabeth Boakes Elizabeth Boakes PhD Student Institute of Zoology

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Dear All, I am analysing a dataset on levels of herbivory in seedlings in an experimental setup in a rainforest. I have seven classes/categories of seedling damage/herbivory that I want to analyse, modelling each separately. There are twenty maternal trees, with eight groups of seedlings around each. Each tree has a TreeID, which I use as the random effect (blocking factor). There are two

Dear all, I have the following dataset: each row corresponds to count of forest floor small mammal captured in a plot and vegetation characteristics measured at that plot > sotr plot cnt herbc herbht 1 1A1 0 37.08 53.54 2 1A3 1 36.27 26.67 3 1A5 0 32.50 30.62 4 1A7 0 56.54 45.63 5 1B2 0 41.66 38.13 6 1B4 0 32.08 37.79 7 1B6 0 33.71 30.62

Hello all R-user I am relative new to the R-environment and also to GLMM, so please don't be irritated if some questions don't make sense. I am using R 2.0.0 on Windows 2000. I investigated the occurrence of insects (count) in different parts of different plants (plantid) and recorded as well some characteristics of the plant parts (e.g. thickness). It is an unbalanced design with 21

Hello, I'm using GLMM on the pscl package and i'm not getting the AIC on the summary. The code i'm using is (example) : mmall3 <-glmmPQL(allclues ~ cycloc + male, data=dados, family=poisson, random=~1|animal/idfid) and the results: Linear mixed-effects model fit by maximum likelihood Data: dados AIC BIC logLik NA NA NA Random effects: Formula: ~1 | animal

>On Wed, 3 Aug 2005, Bernd Weiss wrote: > >> I am trying to replicate some multilevel models with binary outcomes >> using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. > >That's not going to happen as they are not using the same criteria. the glmmPQL and lmer both use the PQL method to do it ,so can we get the same result by

Dear R-Helpers, I want to compare the results of outputs from glmmPQL and lmer analyses. I could do this if I could extract the coefficients and standard errors from the summaries of the lmer models. This is easy to do for the glmmPQL summaries, using > glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df, family = binomial), TRUE) > summary(glmmPQL.fit)$tTable

Hi - I am using R version 2.13.0. I have run several GLMMs using the glmmPQL function to model the proportion of fish caught in one net to the total caught in both nets by length. I started with a polynomial regression full model with three length terms: l, l^2, and l^3 (l=length). The length terms and intercept were the fixed effects and the random effect was a paired haul (n=18).