similar to: Help with complex lme model fit

Hi all, I've now got a problem with some modified lmer code (function lmer1 pasted at end) - I've made only three changes to the lmer code (marked), and I'm not really looking for comments on this function, but would like to know why execution of the following commands that use it almost invariably (but not quite predictably) leads to the R session terminating. Here's the command

Sparc Solaris 8 SunStudio 11 compilers R-2.3.1 spatstat 1.10-1 R-2.3.1 and spatstat were installed using SunStudio11 compilers on a Sparc Solaris 8 machine. No errors were reported in the compilations, however, when I try to load spatstat I get an error: > library(spatstat) Loading required package: mgcv This is mgcv 1.3-20 Error in dyn.load(x, as.logical(local), as.logical(now)) :

Hi everyone, I've been having difficulty writing wrapper functions for some functions where those same functions include other functions with eval() calls where the environment is specified. A very simple example using function lmer from lme4: lmerWrapper <- function(formula, data, family = gaussian, method = c("REML", "ML", "PQL", "Laplace",

There seems to be a minor bug in barplot.default when used with log scale w= here one or more values is NA: dat <- matrix(1:25, 5) dat[2,3] <- NA barplot(dat, beside =3D T) #Plots and appropriate barplot with gaps for m= issing data barplot(dat, beside =3D T, log =3D "y") #Error in if (min(height + offset) <=3D 0) stop("log scale error: at least = one 'height +

Affected: Various 2.x.x releases of samba on Solaris 2.6/8 and Netware 5.1 SP5. Problem: Unable to copy files from a mapped Netware drive to a mapped samba share on a PC desktop (W2K). Error: Several, but commonly "Cannot copy MYFILE. Access is denied. The source file may be in use" or "An unexpected network error has occurred". Has anyone seen similar? It appears that some

Hi, I'm stuck with a fairly basic re-ordering problem. I want to extract part of a matrix as a vector, and reorder it to match with an erratic sequence of x,y coordinates. Here's an example that shows what I want and how close I've got (but for some reason my mind just can't get the final step at the moment!): #Define a grid: xy100 <- expand.grid(1:10, 1:10) # Define a matrix

Hi problem: plot(xxx) results in the graphics device window adding the axes values for what ever xxx is but failing to actually add lines or produce the plot until the consol window is activated and moved over the graphics window. The graphics window then shows what has been plotted. This started happening 4 days ago, up until that time plot(xxx) would produce the plot on the graphics device

Hi, I'm trying to make sense of the image3d plots in misc3d. Unfortunately I can't find any authors on the help pages of the function to e-mail directly. In the following toy example, I'm puzzled as to why (1) the positions of the image plot on the axes include negative values when I think I've specified all to be 1:3 and (2) the logic that needs to be adhered to in order to

I have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4. I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge. Here is my lme code: dat <-

Dear R helpers   I have following csv file which is an input   id       par_value    coupon_rate     frequency_coupon   tenure    ytm   1            1000             10                      1                     5          12   # Here frequency_coupon is coded s.t. 0 means Daily compounding, 1 means monthly compouding, 2 means Quarterly, 3 means Half yearly and 4 means only once. Thus in the

Douglas Bates wrote: > > The GLMM function in the lme4 package allows you to specify crossed > random effects within the random argument without the need for the > pdBlocked and pdIdent constructions. Simply ensure that your grouping > factors are defined in such a way that each distinct group has a > different level in the grouping factor (this is usually not a problem

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

Hi, I have a data set on germination and plant growth with the following variables: dataset=fm mass (response) sub (fixed effect) moist (fixed effect) pop (fixed effect) mum (random effect nested within population) iheight (covariate) plot (random effect- whole plot factor for split-plot design). I want to see if moist or sub interacts with mum for any of the pops, but I am getting an error

Consider the Assay data where block, sample within block and dilut within block is random. This model can be fitted with (where I define Assay2 to get an ordinary data frame rather than a grouped data object): Assay2 <- as.data.frame(Assay) fm2<-lme(logDens~sample*dilut, data=Assay2, random=list(Block = pdBlocked(list(pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) )) Now, block

Hi all - R2.0.1, OS X Perhaps while there is some discussion of lme going on..... I am trying to execute a glmm using glmmPQL from the MASS libray, using the example data set from McCullagh and Nelder's (1989, p442) table 14.4 (it happens to be the glmm example for GENSTAT as well). The data are binary, representing mating success (1,0) for crosses between males and females from two

I am doing an analysis and would like to use lme() and the multcomp package to do multiple comparisons. My design is a within subjects design with three crossed fixed factors (every participant sees every combination of three fixed factors A,B,C). Of course, I can use aov() to analyze this with an error term (leaving out the obvious bits): y ~ A*B*C+Error(Subject/(A*B*C)) I'd also like

Try this data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) I'm

Hello, I have used nlme to fit a model, the R syntax is like fmla0<-as.formula(paste("~",paste(colnames(ldata[,9:13]),collapse="+"),"-1")) > fmla1<-as.formula(paste("~",paste(colnames(ldata[,14:18]),collapse="+"),"-1")) >

Hello, I'm very sorry for my repeated question, which i asked 2 weeks ago, namely: i'm interested in possibly simple random-part specification in the call of GLMM(...) (from lme4-package) i have a random blocked structure (i.e. ~var.a1+var.a2+var.a3, ~var.b1+var.b2,~var.c1+var.c2+var.c3+var.c4), and each one part of it i would like to model as Identity-structure matrix. So i had,

hello! this is a question, how can i specify the random part in the GLMM-call (of the lme4 library) for compound matrices just in the the same way as they defined in the lme-Call (of the nlme library). For example i would just need random=list(my.Subject=pdBlocked(list(pdIdent(~... , ...),pdIdent(~... , ...)))) this specification , if i also attach library(nlme) , is not