similar to: mgcv bam() with grouped binomial data

Dear All, Does anybody have experience with R package pbatR (http://cran.r-project.org/web/packages/pbatR/index.html)? I am trying to use it to analyze the family-based case-control data, but the package totally doesn?t work on my computer. I contacted the authors of the package, but I haven?t heard anything from them. Following the package manual, I tried the simple example as below:

Dear useRs, I reran an analysis with bam (mgcv, version 1.7-17) originally conducted using an older version of bam (mgcv, version 1.7-11) and this resulted in the same estimates, but much lower standard errors (in some cases 20 times as low) and lower p-values. This obviously results in a larger set of significant predictors. Is this result expected given the improvements in the new version? Or

Dear all, I am trying to use 'bam' to run the following generalized additive model: ### m <- bam(result ~ factor(city) + factor(year) + lnpopulation + s(lnincome_pc) + ,data=full_df,na.action=na.omit,family=ziP(theta = NULL, link = "identity",b=0)) But getting the following error: ### Error in bam(result : extended families not supported by bam The documentation for

Do you have the mgcv package installed (I think it's part of the standard distro, though) /loaded? ziP is there, not in BAM. Other than that, sorry, no clue. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon, Nov 6,

Dear useRs, This is the first time I post to this list and I would appreciate any help available. I've used the excellent mgcv package for a while now to investigate geographical patterns of language variation, and it has has always worked without any problems for me. The problem below occurs using R 2.14.0 (both 32 and 64 bit versions in Windows and the 64 bit version in Unix) and mgcv (both

Hi R users, I'm using the 'bam' function in mgcv to examine trends in a remotely sensed vegetation index. I have one random effect variable, 'cons' (with six levels) which identifies different subjects within this analysis. My model is specified as follows: rm4<-bam(trend~factor(zone)+s(cons,bs="re")+s(year,

Hi all: I've got a data of ".bam" which is created from my partner under linux sysyem. My system is window xp, and I wanna know how to read the .bam file. Many thanks! My best [[alternative HTML version deleted]]

I am newbie in R, trying to understand and compare syntax in nlme and lme4. lme() model from the nlme package I am interested in is: lme.m1.1 = lme(Y~A+B+C,random=~1|D/E,data=data,method="ML") (for simplicity reason, I am giving generic names of factors) If I understand well, there are three fixed factors: A, B and C, and two random factors: D and E. In addition to that, E is nested in

Hello R users, I have a quick question I was hoping to get your input on. I am new to R and the smooth statistical regression world, and am trying to wrap my mind around the issues concerning using splines for mixed effect modeling. My question is the following: in the ?gamm? function, generalized additive mixed models can be estimated by including random components. These can be explicitly

Hello everyone. I am doing a logistic gam (package mgcv) on a pretty large dataframe (130.000 cases with 100 variables). Because of that, the gam is fitted on a random subset of 10000. Now when I want to predict the values for the rest of the data, I get the following error: > gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1, +

Hello, I am trying to update the mgcv package on my Linux box and I keep getting an "Out of memory!" error. Does anyone know of a fix for this? Below is a snippet of the message that I keep getting: Thank you. Geoff ** R ** inst ** preparing package for lazy loading ** help *** installing help indices >>> Building/Updating help pages for package 'mgcv' Formats:

Dear Dr Skaug and R users, just discovered glmm.admb in R, and it seems a very useful tool. However, I ran into a problem when I compare two models: m1<-glmm.admb(survival~light*species*damage, random=~1, group="table", data=bm, family="binomial", link="logit") m1.1<-glmm.admb(survival~(light+species+damage)^2, random=~1, group="table", data=bm,

Hi I'm missing something here but I cannot figure out what. What I can see is that the same code works when I load it via source(...) yet fails when I execute it after loading the package I have built (which includes the code. Below is a transcript of my R session. First I load the code from a file, using source(). Then I execute it fine. Then I remove the function object, I load the

Hello, I'm trying to set up an alternate user with a different inbox using passwd-file and userdb extra fields and the extra field settings don't seem to be taking effect. I've used auth_debug and mail_debug and I'm unclear how I can further troubleshoot this. Is it possible to have inbox=yes in one namespace globally and then override this for one user by setting inbox=no for

Hi, I am getting a warning message when I am fitting a generalized linear mixed model (m1.2 below). CHOLMOD warning: matrix not positive definite Error in objective(.par, ...) : Cholmod error `matrix not positive definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614 Any idea? Thanks for your help, Reza > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32

mgcv: Constructing probabilities for binomial GAM with crossed random intercepts and factor by variable Hello, (I'm sorry if this has been discussed elsewhere; I may not have been looking in the right places.) I ran a binomial GAM in which "Correct" is modelled in terms of the participant's age and the modality in which the stimulus is presented (written vs spoken).

Dear list, I am trying to re-analyse something. I do have two time series, one of which (ts.mar) might help explaining the other (ts.anr). In the original analysis, no-one seems to have cared about the data being time-series and they just did OLS. This yielded a strong positive correlation. I want to know if this correlation is still as strong when the autocorrelations are taken into account.

Hi, Is the stars command in the base package or do I need to download? I am looking to make star/radar/spider charts. Thanks! Britt Britt Aronovich Marketing Analyst BAM (Brooklyn Academy of Music) ... Peter Jay Sharp Building 30 Lafayette Ave. Brooklyn, NY 11217-01486 ... P: 718.724.8038 E: baronovich@BAM.org<mailto:baronovich@BAM.org> BAM.org Facebook.com/BAMstage

Hi, The problem is most likely, you need to call a R CMD BATCH with your arguments and the R-script inside of a shell script that you submit to your qsub. Unfortunately we don't use qsub anymore so can't test it, but it should be as follows: R-script eg. test.R: > ##First read in the arguments listed at the command line > args=(commandArgs(TRUE)) > > ##args is now a list of

Dear all, please could you advise me on the following : I've written a R script that reads 3 arguments from the command line, i.e. : " args <- commandArgs(TRUE) TUMOR <- args[1] GERMLINE <- args[2] CHR <- args[3] ". when I submit the R script to a PBS HPC scheduler, I do the following (below), but ... I am getting an error message. (I am not posting the error message,