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> > I'm testing out a new version of coxme and R CMD check fails with "could not find function > > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the > > line below > > > importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) > > > and nlme is declared in the DESCRIPTION

I'm testing out a new version of coxme and R CMD check fails with "could not find function ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the line below importFrom(nlme, ranef, random.effects, fixef, fixed.effects, VarCorr) and nlme is declared in the DESCRIPTION file as an import. I feel that I must be staring at some obvious (but

>>>>> Therneau, Terry M , Ph D <therneau at mayo.edu> >>>>> on Sun, 15 Feb 2015 17:31:00 -0600 writes: > I'm testing out a new version of coxme and R CMD check fails with "could not find function > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the > line below >

On 16/02/2015 8:20 AM, Therneau, Terry M., Ph.D. wrote: > >> > I'm testing out a new version of coxme and R CMD check fails with "could not find function >> > ranef" (or random.effects or fixef, or anything from nlme). The NAMESPACE file has the >> > line below >> >> > importFrom(nlme, ranef, random.effects, fixef,

Version 2.2 of coxme has been posted to CRAN, Windows versions and mirrors should appear in due course. This is a major update with three features of note: 1. A non-upwardly compatable change: Extractor functions: beta= fixed effects, b=random effects nlme lme4 coxme <2.2 coxme 2.2 lmekin 2.2 ------------------------------------------------------ beta

User of the coxme library (mixed effects Cox models) are instructed to use ranef(), fixed(), VarCorr(), etc to retrieve bits out of a fitted model; it purposely uses the same methods as nlme and/or lmer. The current behavior is to "depend" on nlme. If I defined the methods myself in coxme, then someone who had both nlme and coxme loaded will suffer from "last loaded wins",

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together

I have a two-dimensional loess fit, and need to calculate the gradient field from it. Even after looking at loess.c and loess.f, I don't understand the meaning of the returned polynomial coefficients. Or is the brute force method of using a tangential approx to the fitted values the way to go? Dieter Menne --------------------------------------- Dr. Dieter Menne Biomed Software 72074

Is there an easy way to remove data frame columns by name instead of by index? The following gives the idea remove<-c("subj","drug") data[-remove] I found a solution with a few evals and substitutes, similar to that used in reshapeLong, but there must be an easier way out. Dieter --------------------------------------- Dr. Dieter Menne Biomed Software 72074 T?bingen Tel

Dear R-Listers, to measure the psychometric curve of pitch discrimination, one sequentially presents two tones of slightly different pitch to an observer (animal will do), and asks "which is higher". The pschometric curve is the fraction of correct responses plotted against the pitch difference. It passes through 50% (pure guessing) at zero and normally approaches 100% at large

Dear R-supporters, I have results of lme similar to those shown in Fig. 1.21, p.51 of Pinheiro/Bates. However, In my data set, one of the panels is missing, leading to an ugly frame shift of the following panels. How can I replace one of the panels by an empty one to restore the raster? Dieter Menne --------------------------------------- Dr. Dieter Menne Biomed Software 72074 T?bingen Tel

Dear friends of nlme, Running quinModel (Pinheiro/Bates page 380) on R (current release, windows) gives: Nonlinear mixed-effects model fit by maximum likelihood Model: conc ~ quinModel(Subject, time, conc, dose, interval, lV, lKa, lCl) Data: Quinidine Log-likelihood: -497 Fixed: lV + lKa + lCl ~ 1 lV lKa lCl 5.382 -0.273 2.470 Random effects: Formula: list(lV ~ 1, lCl ~ 1)

Dear R's, I have a survey where customers rank a set of 5 packages for a product, so the response variable looks like a d b c a c d b d b a c Predictors variables are 4 socio-economic parameters. I have modelled the FIRST choice of each subject as a multinomial model, similar to the housing example in MASS ch7.3, , but I would prefer to use the whole rank-set instead. Can someone give me a

Using a formula converted with as.formula with lme leads to an error message. Same works ok with lm, and with lme and a fixed formula. # demonstrates problems with lme and as.formula demo<-data.frame(x=1:20,y=(1:20)+rnorm(20),subj=as.factor(rep(1:2,10))) demo.lm1<-lme(y~x,data=demo,random=~1|subj) print(summary(demo.lm1)) newframe<-data.frame(x=1:5,subj=rep(1,5))

deriv seems to have problems with a minus-sign before a bracket. Below are four examples of the same function, the top one is wrong, all others are correct (hopefully). Rest of expression not shown, it is the same for all versions. _ platform i386-pc-mingw32 arch x86 os Win32 system x86, Win32 status major 1 minor 3.0 year 2001 month 06 day 22 language R

Dear Mixed Effect Friends, Somehow, R(1021, Windows) seem to run differently from S Plus: The soybean example from Pinheiro/Bates on page 290 fails in R. (Soybean1 is Soybean with the NA and "critical" case removed. Same procedure with full Soybean). > fm1Soy.lis<-nlsList(weight~SSlogis(Time,Asym,xmid,scal),data=Soybean1) > fm1Soy.nlme<-nlme(fm1Soy.lis) Error: Singularity

I try to display the grouping variable in coplot. It work, but it's special solution and rather ugly. Any better idea? # Simulate my data frame data(state) x77<-data.frame(state.x77) x77$region<-state.region coplot(Life.Exp ~ Income | region, data=x77, show.given=F, subscripts=T, panel = function(x, y,subscripts, ...) { panel.smooth(x, y, span = 1., ...)

I am confused by the logic of renaming: # Rename individual list items? Empl<-list(employee="Anna",spouse="Fred") names(Empl)<-c("empl","spo") names(Empl) #[1] "empl" "spo" # worked like a charm... but names(Empl[1])<-"newempl" # no error message, yet .... names(Empl) #[1] "empl" "spo" #

We have extracted parameters from physiological measurements by fitting SSlogis-like curves with nlsList and nlme. We presented residuals plot in a paper, but a referee argues that these cannot be included (too technical), and r-square values should be given instead to compare the goodness of fit with those of other authors. I remember that 30 years ago in my stat 101, I learned that r-square is

I have problem getting the concept of a nested fixed variable into the nlme scheme. I fear the question is very stupid. In the past I had asked this before, and never got a reply (in other cases, the response was within hours). I also checked the S-list, where several similar enquiries of other people are orphaned. We have a cross-over design, where patient are treated two weeks with placebo,