similar to: Extracting the full coefficient matrix from a gls summary?

Dear R users, I have used the following model: M1 <- gls(Nblad ~ Concentration+Season + Concentration:Season, data=DDD, weights=varIdent(form=~ 1 | Season*Concentration)) to assess the effect of Concentration and Season on nitrogen uptake by leaves (Nblad). I accounted for the difference in variance across the factor levels by using the varIdent function. Then I wanted to perform multiple

Hi, Can you please help me with this please? What I am trying to do is call a vector from R function and used in the new function So I create 4 functions with these arguments M11 <- function(TrainData,TestData,mdat,nsam) { ls <- list() I have few statments one of them is vectx <- c(,1,2,3,4,5,6,6) vectz <- c(12,34,5,6,78,9,90) and then................ ls(vectx=vtecx,vectz=vectz)

Hello, I am analyzing some climate time series data using the Mann Kendall package and was wondering if there was a way to calculate the trend using Sen's nonparametric estimator slope in R? Thank you in advance, Barry _________________________ Barry Baker, Ph.D. Global Climate Change Initiative The Nature Conservancy 2424 Spruce St., Suite 100 Boulder, CO 80302 Tel: (303)-541-0322 Fax:

Hi, I am fitting a weighted least square regression and trying to compute SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know what I am coding wrong. Can you help please? xnam <-colnames(X) # colnames Design Matrix fmla1 <- as.formula(paste("Y ~",paste(xnam, collapse=

I'm working on some functions for generalized canonical discriminant analysis in conjunction with the heplots package. I've written a candisc.mlm function that takes an mlm object and computes a candisc object containing canonical scores, coeficients, etc. But I'm stumped on how to construct a mlm for the canonical scores, in a function using the *same* right-hand-side of the model

Dear alltogether, how can a delay like possible with par(ask=TRUE) be attained while using trellis-plots within a loop or something like that? the following draws each plot without waiting for a signal (mouse-klick), so par() does not work for that: library(nlme) for(i in 1:3) { fitlme <- lme(Orthodont) par(ask=TRUE) # does not work with trellis.... print(

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Hello all, I have a question using PAM to authenticate on Solaris 10, with 2.0rc2. I need to set the user for auth-worker to root, else it will return "Unknown user". Is that expected? I'm using PAM, so why does it need access to /etc/shadow? TIA! Laurent service auth-worker { # Auth worker process is run as root by default, so that it can access # /etc/shadow. If this

Thanks to some comments from Brian D. Ripley, I found my error: I should not have given a data argument to lm() after creating a formula-object. This obviously confused things... Thanks again, I've really learnt again a bit more on R-programming... Cheers, Winfried --------------------------------------------------------------------- E-Mail: Winfried Theis <theis at

Hi all: I am a user of "JM" package. Here's the problem of "sample size". The warning is: Error in jointModel(fitLME, fitSURV_death, timeVar = "time", method = "piecewise-PH-GH") : sample sizes in the longitudinal and event processes differ. According to the suggestion of "missing data",I use the same data set(data_JM) without any

I contacted the package developer and that lead to me removing events at time 0 (or subjects with only 1 longitudinal measurement). I then still had the error message "Can't fit a Cox model with 0 failures" which I have managed to avoid by adding 1.8*10^(-15) to all my survival times, any number greater than this also works but nothing smaller! Any explanation of this would help!

I have used LME to fit a mixed effects model on my data. The data has 274 subjects with 1 to 6 observations per subject. Time is not linearly associated with the outcome, so I used ns to fit a natural cubic spline with 3 auto knots. Subject and the natural cubic time of spline are both treated as random effects. This model has run without any problem, but now I would like to plot trajectories for