similar to: Another issue with the Matrix package.

I encounter such problem with lmer under FreeBSD, but not under Windows. Anyone knows why? Thanks. > example(lmer) lmer> (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) Error in UseMethod("as.logical") : no applicable method for "as.logical" > traceback() 9: as.logical(EMverbose) 8: as.logical(EMverbose) 7: lmerControl() 6:

Dear Dr. Bates, and R-help, I've tried the anova function to test the difference between two coefficients, as shown on page 225 of your book "Mixed Effects Models in S and S-Plus (Statistics and Computing)". When I type: anova( fm2BW.lme, L = c(TimeDiet2 = 1, TimeDiet3 = -1) ) I got the following error message: Error: unexpected '=' in "anova( fm2BW.lme, L =

Hi, I am trying to find a way to automate production of page numbers in plots produced using the lattice package. To do this, I started playing around with setHook which works fine with vanilla plot, but the hook 'plot.new' doesn't appear to be relevant to the lattice package. I was wondering if there is a alternative someone can suggest for use with the lattice package. Example

Dears Sirs During my computational work I encountered unexpected behaviour when calling "ar" function. I want to select the order p of the autoregressive approximation by AIC criterion and sometimes an error occurs. Example: # time series

I am not sure whether there is a bug. When I tested the example given for "glht" in the help, I entered the following error: Running commands: amod <- aov(minutes ~ blanket, data = recovery) rht <- glht(amod, linfct = mcp(blanket = "Dunnett"), alternative = "less") Errors are: Error in try(coef.(model)) : could not find function

Hi, I was taken off guard by the following behavior in a lattice plot. I frequently want to add a predicted curve defined at more points than in the formula expression of xyplot. There have been numerous examples of how to do this on r-help, but I still often struggle to make this work. I just realized that specifying one of the axes on a log scale does not guarantee that the added data for a

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

After upgrading to 2.5.0 under freebsd 6.2 I updated all the add-on packages in an R session via update.packages(...). R was unable to install the "Matrix" package because it couldn't find the package. Therefore I downloaded the *.tar.gz from the CRAN site and issued R CMD INSTALL Matrix_0.99875-1.tar.gz but the comilation ended in an error (see the complete attached

Hello, everybody I have R 2.5.1 now installed on a laptop with Windows Vista Home Premium. I have problems updating the packages. Here is what I get at the prompt when I try to update: > update.packages(ask='graphics') --- Please select a CRAN mirror for use in this session --- trying URL 'http://cran.ch.r-project.org/bin/windows/contrib/2.5/car_1.2-2.zip' Content type

Hi, I get the following error message when trying to use the display function on the ARM package: > display(model) Error in .Internal(round(x, digits)) : no internal function "round" Looks like some kind of mismatch between the ARM package and some others? Can I somehow get around it? I have learned to like the display function to print model summaries. Here is my sessionInfo():

After upgrading to R 2.6.0, I'm having trouble running lmer: model <- lmer(primed ~ log(dist.time)*role + 1|target.utt, data=data.utts) Error in UseMethod("as.logical") : no applicable method for "as.logical" So I thought I'd upgrade lme4 to the latest version, but unfortunately the compilation fails - perhaps there's a missing #include: R CMD INSTALL

Hi folks - After upgrading to the latest version of Rmetrics, I can't read in data like I used to. Is anyone seeing the following? It seems to truncate the dates after I use "as.timeSeries". -John SP500<-read.table("SP500.csv",header=TRUE,sep=",") > head(SP500) Date Open High Low Close Volume 1 08/04/06 1280.26 1292.92

Dear All, I?m using lme to analyze a time series gene expression data. I don?t have all animals in all times, the number of animals in each time is different and I have lots of NA values. An example file can be downloaded at lbmp.fcav.unesp.br/leonardo and the code used is the following: teste<-read.table(file='example.csv',sep=',',header=T) B<-1366 library(nlme)

Hi all, I compiled 64-bit R on an Apple Mac G5 running OS X, but it failed make check. Simon Urbanek suggested I post results to R-devel. > On Nov 6, 2007, at 10:23 PM, Steven McKinney wrote: > > > Hi Simon, > > > > Would you be able to give more guidance on how to compile 64-bit > > libiconv for Tiger, > > You can get the sources from Apple and compile

Hi, I understand that the glm.fit calls LINPACK fortran routines instead of LAPACK because it can handle the 'rank deficiency problem'. If my data matrix is not rank deficient, would a glm.fit function which runs on LAPACK be faster? Would this be worthwhile to convert glm.fit to use LAPACK? Has anyone done this already?? What is the best way to do this? I'm looking at very

Hi, I would have thought that these two constructions would produce the same result but they do not. Resp <- rbinom(10, 1, 0.5) Stim <- rep(0:1, 5) mm <- model.matrix(~ Stim) Xb <- mm %*% c(0, 1) ifelse(Resp, log(pnorm(Xb)), log(1 - pnorm(Xb))) pnorm(as.vector(Xb), lower.tail = Resp, log.p = TRUE) > ifelse(Resp, log(pnorm(Xb)), log(1 - pnorm(Xb))) [1] -0.6931472 -1.8410216

Hi all, I don't know if this is the correct venue for this question, but I am sure that someone will correct me if I am in the wrong list. I have been searching throughout R for a function that can find the intersection of multiple sets of "things". Say for instance, I have a list of $n$ character vectors and would like to find the intersection of all $k$ subsets. I don't

There must be a very basic thing I am not getting... I'm working with some entropy functions and the convention is to use log(0)=0. So I wrote a function: llog<-function(x){ if (x ==0) 0 else log(x) } which seems to work fine for individual numbers e.g. >llog(0/2) [1] 0 but if I try whole vectors or tables: p<-c(4,3,1,0) q<-c(2,2,2,2) llog(p/q) I get this: [1] 0.6931472

Hi, Summary: I ran into some unexpected behavior in approx() and tapply() that introduced NA's in "clean" data due to (?) numerical accuracy/round off. The culprit seems to be in match() that coerces it's arguments to character, loosing precision in the process. [R development version 1.2.0, 08 Nov 2000, on Linux] Example: > r > [1] 0.6931472 0.6931472 0.6931472

Hi, Summary: I ran into some unexpected behavior in approx() and tapply() that introduced NA's in "clean" data due to (?) numerical accuracy/round off. The culprit seems to be in match() that coerces it's arguments to character, loosing precision in the process. [R development version 1.2.0, 08 Nov 2000, on Linux] Example: > r > [1] 0.6931472 0.6931472 0.6931472