similar to: Multiple lmer runs using 2 'for' loops

Dear All, I am quite new to R and am having a problem trying to run a linear model with random effects/ a regression- with particular regard to my variable lengths being different and the models refusing to compute any further. The codes I have been using are as follows: vc<-read.table("P:\\R\\Testvcomp10.txt",header=T) >> attach(vc) > > family<-factor(family) >

---------- Forwarded Message ---------- Date: 18 September 2009 19:24 +0100 From: A Singh <bzwas at bristol.ac.uk> To: William Dunlap <wdunlap at tibco.com> Subject: RE: [R] Error: length(f1) == length(f2) is not TRUE Yup, they are all factors- and its still doesn't work. Getting to the stage where I can use 'summary()' is the problem- the error stalls the process before a

Dear R users, I am trying to fit an lmer model with only random effects which is giving me the following error: Error : length(f1) == length(f2) is not TRUE In addition: Warning messages: 1: In P1L55:family : numerical expression has 390 elements: only the first used 2: In P1L55:family : numerical expression has 390 elements: only the first used I am trying to extract variance components

Dear all, Does anyone have any suggestions on how to block multiple columns of data one at a time in the midst of an analysis, having specified the blocking variable? I am running a random effects model using lmer, and my data set has multiple columns. Individuals in the study are grouped into 60 families- which is the blocking factor. The random effects are markers (labeled Pxlyy below)

Dear List, I am trying to run a mixed model which, on the R console, prints output as follows: [1] "Marker" [1] "perm no." [1] NA Linear mixed model fit by REML Formula: peg.no.prm ~ 1 + (1 | family/f) Data: modeldf AIC BIC logLik deviance REMLdev 3119 3134 -1555 3112 3111 Random effects: Groups Name Variance Std.Dev. f:family (Intercept) 0.0

Dear List, I thought it would be much easier to put a second query into a second mail. I need to print 426*10000 blocks of variance components data, where 426 is the number of columns of data that have 10000 permutations of variance generated for each of them. I have tried printing out a smaller number of permutations for a smaller number of markers and that has worked. However, since a

Dear List, I need to print out each of 'k' levels of a factor 'n' times each, where 'n' is the number of elements belonging to each factor. I know that this can normally be done using the gl() command, but in my case, each level 'k' has an unequal number of elements. Example with code is as below: vc<-read.table("P:\\Transit\\CORRECT

Dear all, I'm using Roxygen for the first time and I'm getting a rather cryptic error message. I must be doing something wrong but I have no clue what is it. Any suggestions? Regards, Thierry roxygenize("AFLP", roxygen.dir = "AFLP", copy.package = FALSE, unlink.target = FALSE) Writing AFLP.outlier to AFLP/man/AFLP.outlier.Rd Writing AFLP.outlier to

Dear List, I'm having a curious problem with lapply(). I've used it before to convert a subset of columns in my dataframe, to factors, and its worked. But now, on re-running the identical code as before it just doesn't convert the columns into factors at all. As far as I can see I've done nothing different, and its strange that it shouldn't do the action. Has anybody

Dear R users, I am trying to read in a file with 105 columns, and when trying to attach it, get an error as follows: > vc1<-read.table("P:\\R\\Everything-I.txt", header=T, sep=" ", dec=".", na.strings=NA, strip.white=T) > attach(vc1) Error in attach(vc1) : variable names are limited to 256 bytes Is there a way to get around this, and make R accept the

1. One general question for general discussion: Is HLM6 faster than lmer? If so, why? What should I watch for to spot the differences? I'm always advocating R to students, but some faculty members are skeptical. A colleague compared the commercial HLM6 software to lmer. HLM6 seems to fit the model in 1 second, but lmer takes 60 seconds. My first thought was that LM6 uses PQL by default,

Markdowners: I've released version 0.4.1 of peg-markdown, a C implementation of markdown. peg-markdown uses Ian Piumarta's peg/leg parser generator to generate a parser from a parsing expression grammar (PEG). You can inspect the grammar for markdown at http://github.com/jgm/peg-markdown/tree/HEAD/markdown_parser.leg peg-markdown now provides both a C library and a standalone

I am looking at data of the following structure: n <- 100 dataset <- data.frame(gender=NULL,subject=NULL,outcome=NULL) for (i in 1:n){ gender <- c(rep("m",5),rep("f",5)) subject <- letters[1:10] outcome <- c(rbinom(5,1,0.6),rbinom(5,1,0.4)) dataset <- rbind(dataset,cbind(gender,subject,outcome))} I am interested in the significance of

Hi all, I am trying to run a linear mixed effect models in lmer() from the lme4 package using the weights option. I am using the R version 2.7.2 (2008-08-25) and lmer version in lme4_0.999375-26, which I think it is the latest version! I am getting and error message when I add the option "weights" in the lmer function. This is the error message I get "Error en

Dear folks (or Dear Professor Bates), I'm quite confused as to the current status of some of the available functions applicable to lmer objects. Following the examples in Baayen, Davidson, Bates (2006), my plan is to run mcmcsamp on a random effect model created by lmer in package lme4, then use the (perhaps outdated) pvals to estimate p-value. But then I couldn't find pvals anywhere.

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

Hi- I want to fit a model with crossed random effects and heteroskedastic level-1 errors where inferences about fixed effects are of primary interest. The dimension of the random effects is making the model computationally prohibitive using lme() where I could model the heteroskedasticity with the "weights" argument. I am aware that the weights argument to lmer() cannot be used to

Dear list I try to run a bootstrap with lmer. I often, but not always, get the error message: Error in objective(.par, ...) : Leading minor of order 6 in downdated X'X is not positive definite (with the number (here 6) varying) In R-archives I came across some threads that treated this problem, nevertheless they refer to lmer when using it with family = "binomial", so the

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 everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model. I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? (see below)