similar to: lme with/without varPower - can I use AIC?

Back on January 16, a message on R-help from Ravi Varadhan described a problem with gnls using weights=varPower(). The problem was that the fit failed with error Error in eval(expr, envir, enclos) : Object "." not found I can reliably get this error in version 2.0.1-patched 2004-12-09 on Windows XP and 2.0.1-Patched 2005-01-26 on Linux. The key feature of that example is that the

Hello, I've been fitting a random effects model using nlme to some data, but I am discovering that the variation in my random effect is very small. As a result, I would like to replace it as a fixed effect (i.e. essentially fit the same model but with no random effect). As I understand it I could do this using nls(), but I'm using a number of options such as weights = varPower()

This to announce a test release of 2.1.0p1 before making it widely available. This release includes many fixes to problems reported over the last week. In particular: - spurious error and coredumps caused by the inbuilt entropy gathering - RSAref detection - Compilation fixes for Solaris and others It also contains (completely untested) support for compiling without RSA support. This may be

Hi guys, I''m part way through a RoR application, for some reason whenever I add new controllers (using scaffold) I get an uninitialized constant [name of controller] error. The first few controllers work fine, the only thing I can see that I''ve changed is the layout file (but scaffold creates a new layout for each controller so can''t see that being the problem). You

I am using rails 2.3.5 and restful_authentication plugin. User model is inherited by visitor, spotter and subscriber. How to implement scoped_views for different user(visitor, spotter and subscriber) registration. -- You received this message because you are subscribed to the Google Groups "Ruby on Rails: Talk" group. To post to this group, send email to

Hello, I am trying to design new variance structures by using fixed effects variables in combination with the VarPower function. That is, I would like to create and evaluate my own variance function in the data frame and then incorporate it into the model using varPower, with value=.5. As a start, I am trying to recreate the function of VarConstPower by introducing two new variables in the

Hello, I am having some difficulty converting my gam code to a correct gamm code, and I'm really hoping someone will be able to help me. I was previously using this script for my overdispersed gam data: M30 <-gam(efuscus~s(mic, k=7) +temp +s(date)+s(For3k, k=7) + pressure+ humidity, family=negbin(c(1,10)), data=efuscus) My gam.check gave me the attached result. In order to

Hi, I guess this is a final plea, and maybe this should go to R-help but here goes. I am writing a set of functions for calibration and prediction, and to calculate standard errors and intervals I need the variance function to be evaluated at new prediction points. So for instance fit<-gnls(Y~SSlogis(foo,Asym,xmid,scal),weights=varPower())

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

Dear R members, I tried to fit an lme model and to use the glht function of multcomp. However, the glht function gives me some errors when using weights=varPower(). The glht error makes sense as glht needs factor levels and the model works fine without weights=. Does anyone know a solution so I do not have to change the lme model? Thanks Sibylle --> works fine

Dear all, I've got a problem in including square variables in lme function. I've tried to work on Dialyzer data of Pinheiro and Bates'book. We fit the heteroscedastic model with: > data(Dialyzer) > fm2Dial.lme<-lme(rate~(pressure+pressure^2+pressure^3+pressure^4)*QB, + Dialyzer,~pressure+pressure^2,weights=varPower(form=~pressure)) We Obtain > fm2Dial.lme Linear

Dear R developers, first of all let me join the chorus of congratulations for the release of R 1.0.0. Well, done! Unfortunately, I find it necessary to e-mail in a bug report regarding the `nlme' package. On my office machine I experience the following trouble: bossiaea:/opt/R$ R CMD check -c nlme Checking package `nlme' ... Massaging examples into `nlme-Ex.R' ... Running

Dear R-user I used lme to fit a linear mixed model inlcuding weights=varPower(). Additionally I wanted to use glht to calculate Tukey-Kramer multiple comparision. error: > glht(modelF, linfct=mcp(Species="Tukey")) Error in glht.matrix(model = list(modelStruct = list(reStruct = list(SubPlot = -0.305856275920955, : ?ncol(linfct)? is not equal to ?length(coef(model))? >

I'm getting some odd results computing log-likelihoods with gls using splines with increasing degrees of freedom -- the deviance *increases* substantially with increasing df. (Since spline models with increasing df aren't nested, it need not decline monotonically but I would expect it to have a decreasing trend!) I may just be confused, but I *think* the issue is somewhere within the

Hi dear R community, I have up to 12 measures of a protein for each of 6 patients, taken every two or three days. The pattern of the protein looks periodic, but the height of the peaks is highly variable. I'm testing for periodicity using a Monte Carlo simulation envelope approach applied to a cumulative periodogram. Now I want to predict the location of the peaks in time. Of course, the

Dear R users, I am currently analyzing a dataset using lme(). The model I use has the following structure: model<-lme(response~Covariate+TreatmentA+TreatmentB,random=~1|Block/Plot,method="ML") When I plot the residuals against the fitted values, I see a clear positive trend (meaning that the variance increases with the mean). I tried to solve this issue using weights=varPower(),

Hi there I am a Masters student at the University of Stellenbosch. I have been using R to analyze the data, using the GLS model, of one of my experiments. The problem that I am having is that whenever I run my model using: fit.glsmodel1<-gls(Number~as.factor(Season)+as.factor(Depth)+as.factor(Orientation), data=Number, weights=varPower(), method="ML") I get the error: Error in

Dear R-help, Can anyone explain the meaning of the warning, Singular precision matrix in level -1, block 1 ? Or how to track down where it comes from? More precisely, using the nlme package, I'm issued with the warning itt2 <- lme(lrna~rx.nrti+lbrna, random=~1|patid, cor=corExp(form=~days|patid,nugget=T), weights=varPower( form=~lbrna),data=rna3) Warning messages: 1: Singular

Hi, I am trying to add multiple variance structures such as the first example below: vf1 <- varComb(varIdent(form = ~1|Sex), varPower()) However my code below will not work can anybody please advise me? VFcomb<-varComb(varExp(form=~depcptwithextybf),varFixed(form=~FebNAO)) also if you have two variables with the same weights function would you write that as:

Hi: I have data from an assay in the form of two vectors, one is response and the other is a predictor. When I attempt to fit a 5 parameter logistic model with "nls", I get converged parameter estimates. I also get the same answers with "gnls" without specifying the "weights" argument. However, when I attempt to use the "gnls" function and try to