similar to: Simulate p-value in lme4

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

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

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

Hi, I'm analyzing a large number of large simulation datasets, and I've isolated one of the bottlenecks. Any help in speeding it up would be appreciated. `dat` is a dataframe of samples from a regular grid. The first two columns are the spatial coordinates of the samples, the remaining 20 columns are the abundances of species in each cell. I need to calculate the species richness in

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

lme() and gls() models from the nlme package are all crashing with R.3.4.0. Identical code ran correctly, without error in R 3.3.3 and earlier versions. The behavior is easily demonstrated using one of the examples form the lme() help file, along with two simple variants. I have commented the errors generated by these calls, as well as the lines of code generating them, in the code example below.

Hi, I apologize for this often/old question. I found some hints but couldn't solve the problem so far. I have C functions (incl. the header files) as well as the R wrapper functions which I want to use for faster calculations. These functions are included in a R package. The installation process seems to be ok (no errors). I also can load the package without errors. But when I call the

Full_Name: Per Broberg Version: 1.00 OS: Windows 98 Submission from: (NULL) (62.20.231.229) I tested my installation with the following: > library(lme) Loading required package: nls Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "C:\PROGRAM\R\RW1000/library/nls/libs/nls.dll": LoadLibrary failure > data(Orthodont) > fm1

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

Hello everyone, I'm stumped. I'd like to create a scatterplot matrix with circular reference lines. Here is an example in 2d: library(ellipse) set.seed(1) dat <- matrix(rnorm(300), ncol = 3) colnames(dat) <- c("X1", "X2", "X3") dat <- as.data.frame(dat) grps <- factor(rep(letters[1:4], 25)) panel.circ <- function(x, y, ...) { circ1

Df contains Session_Setup DCT RevDataVols_bin counts comp 1 Session_Setup RLL 1 NA Session_Setup+RLL+1 2 Session_Setup RLL 2 NA Session_Setup+RLL+2 3 Session_Setup RLL 3 NA Session_Setup+RLL+3 4 Session_Setup RLL 4 NA Session_Setup+RLL+4 5 Session_Setup RLL 5

Hi, we're using a GSM-Gateway on asterisk to forward incoming calls to the cellphones, but, of course, the cellphones always display the callerid from the gateway. Does anyone know a symbian app that could (on an incoming call) connect via grps/3G to a database behind the asterisk and fetch the real callerid and do a calleridname-lookup on a number? -------------- next part -------------- An

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

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

Dear R-philes: I am having an issue with exporting contingency tables with xtable(). I set up a contingency and convert it to a matrix for passing to xtable() as shown below. v.cont.table <- table(v_lda$class, grps, dnn=c("predicted", "observed")) v.cont.mat <- as.matrix(v.cont.table) Both produce output as follows: observed predicted uh uh~ uh 201

l would like to create the following matrice treatmentgrp strata 1 1 1 1 1 1 1 2 1 2 1 2 2 1 2 1 2 1 2 2 2 2 2 2 l should be able to choose the size of the treatment grps and stratas the method l used intially creates the

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

On 11 May 2017 at 10:17, Erwan Le Pennec wrote: | Dear all, | | I've stumbled a similar issue with the package cluster when | compiling the 3.4.0 version with the settings of Fedora RPM specs. | Compiling R with the default setting of configure yields a version that | works for cluster... and nlme. | | I did not find the exact option that was the cause of this issue | but

Hi, I have a dataset like this, > testdata Grouped Data: expr ~ visit | subject expr visit subject 1 6.502782 V1 A 2 6.354506 V1 B 3 6.349184 V1 C 4 6.386301 V2 A 5 6.376405 V2 B 6 6.758640 V2 C 7 6.414142 V3 A 8 6.354521 V3 B 9 6.396636 V3 C I tried the command >