similar to: Simulate p-value in lme4

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

library(glmmADMB) #Example for glmm.admb data(epil2) glmm.admb(y~Base*trt+Age +Visit,random=~Visit,group="subject",data=epil2,family="nbinom") Gives: Error in glmm.admb(y ~ Base * trt + Age + Visit, random = ~Visit, group = "subject", : The function maximizer failed ****************** R version 2.4.1 RC (2006-12-14 r40181) powerpc-apple-darwin8.8.0 locale: C

hi, I installed the epil repo and imported the key but everytime i try to install a package from epil Public key for proftpd-1.3.3f-1.el6.i686.rpm is not installed I get this with evry package i try to install using centos 6 i686

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Dear R-users, A recent post (Feb 16) to R-help inquired about fitting a glmm with a negative binomial distribution. Professor Ripley responded that this was a difficult problem with the simpler Poisson model already being a difficult case: https://stat.ethz.ch/pipermail/r-help/2005-February/064708.html Since we are developing software for fitting general nonlinear random effects models we

Dear R users, Is there a simple way to use the effect function (library(effects)) with a GEE estimated model? library(gee) library(MASS) library(effects) attach(epil) b = gee(y ~ lbase*trt + lage + V4, family=poisson, id=subject, corstr="exchangeable") plot(effect("lbase*trt", b)) # Errore in effect("lbase*trt", b) : nessun metodo applicabile per

Dear Group: I am working with a data frame containing 316 rows of individuals with 79 variables. Each of these 79 variables have values that range between -4 to +4, and I want to subset this data frame so that in the resulting new dataframe, values of _all_ of these variables should range between -3 and +3. Let's say I have the following dataframe (it's a toy example with 4 individuals

[cc'ing back to r-help] On Fri, Nov 18, 2011 at 4:39 PM, matteo dossena <matteo.dossena at gmail.com> wrote: > Thanks a lot, > > just to make sure i got it right, > > if (using the real dataset) from the LogLikelihood ratio test model1 isn't "better" than model, > means that temporal auto correlation isn't seriously affecting the model? yes. (or

Hi R-users,   I intend to apply a mixed model on a set of longitudinal data, with a negative binomial distributed dependent variable, and after following the discussions on R help list I saw that more experienced people recommended using lmer (from lme4 pack), glmmPQL (from MASS) or glmm.admb (from glmmADMB pack)     My first problem: yesterday this syntax was ok, now I get this weird message (I

I've been running a glmer logit on a very large data set (600k obs). Running on a 10% subset works correctly, but for the complete data set, R completes apparently without error, but does not display the results. Given these jobs take about 200 hours, it's very hard to make progress by trial and error. I append the code and the sample and complete output. As is apparent, I upgraded R

Hi I compouted a multiple linear regression with repeated measures on one explanatory variable: BOLD peak (blood oxygenation) as dependent variable, and as independent variables I have: -age.group (binaray:young(0)/old(1)) -and task-difficulty measured by means of the reaction-time 'rt'. For 'rt' I have repeated measurements, since each subject did 12 different tasks. -> so

Hi, I have sort of a newbie question. I've seriously put a lot of effort into how to handle simple replicates in a repeated ANOVA design, but haven't had much luck. I really liked reading "Notes on the use of R for psychology experiments and questionnaires", by Jonathan Baron and Yuelin Li ( http://www.psych.upenn.edu/~baron/rpsych/rpsych.html ) but still didn't run across

Dear R users, Does anyone know how to get standard errors of the parameter estimates in glmm.admb? Thanks, Istvan

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

Dear R Masters, I'm an anesthesiology resident trying to make his way through basic statistics. Recently I have been confronted with longitudinal data in a treatment vs. control analysis. My dataframe is in the form of: subj | group | baseline | time | outcome (long) or subj | group | baseline | time1 |...| time6 | (wide) The measured variable is a continuous one. The null hypothesis in

Using lmer() on my data results in an error. The problem, I think, is my model specification. However, lm() works ok. I recreated this error with a more simple dataset. (See code below.) # word and letter recognition data # two within factors: # word length: 4, 5, 6 letters # letter position: 1-4 (in 4-letter words), 1-5 (in 5-letter words), 1-6 (in 6-letter words) # one dependent variable: #

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have

Ben Bolstad experienced something odd while running R CMD check over a package of ours using r-devel (yesterday's fresh). He tracked down the problem to a vignette calling 'library(MASS)'. Please kindly ignore if this is a known (and temporary) issue. Laurent ----- Forwarded message from Ben Bolstad <bolstad@stat.berkeley.edu> ----- Return-Path:

Hi R community! I've fitted three mixed-effects regression models to a thousand bootstrap samples (case-resampling regression) using the lme4 package in a custom-built for-loop. The only output I saved were the inferential statistics for my fixed and random effects. I did not save any output related to the performance to the machine learning algorithm used to fit the models (REML=FALSE).

It turns out that when I use GUI (file-change dir) to set the working directory, R will crash. If I use setwd() instead, the example runs well. Regards, On 4 April 2011 00:17, Wincent <ronggui.huang@gmail.com> wrote: > OK, I dig into the problem and found that Chinese character in the path > should be blamed. > Once the path rename to English only, it works. > > Regards,