similar to: Pinheiro/Bates Soybean nlme failure

How to set the nlme() function to return the answer without the intercept parametrization? #========================================================================================= library(nlme) Soybean[1:3, ] (fm1Soy.lis <- nlsList(weight ~ SSlogis(Time, Asym, xmid, scal),                        data = Soybean)) (fm1Soy.nlme <- nlme(fm1Soy.lis)) fm2Soy.nlme <- update(fm1Soy.nlme,

This is only partly a question about R, as I am not quite sure about the underlying statistical theory either. I have fitted a non-linear mixed-effects model with nlme. In the fixed part of the model I have a factor with three levels as explanatory variable. I would like to use Tukey HSD or a similar test to test for differences between these three levels. I have two grouping factors:

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

Mmmh, seems as if I really should change my options as I seem to keep sending off empty bug-reports ;-/ Sorry guys. Here is the content that should have been in the last e-mail: `R CMD check nlme' fails on my machine. The final output in nlme-Ex.Rout is: > library(nlme) > data(Soybean) > fm1 <- nlme(weight ~ SSlogis(Time, Asym, xmid, scal), data = Soybean, + fixed =

I am trying to follow an example in Pineiro & Bates's book library(nlme) data(Soybean) soy.lis<-nlsList(weight~SSlogis(Time,Asym,xmid,scal),data=Soybean) soy.nlme<-nlme(soy.lis) Error: Calloc could not allocate (151974496 of 8) memory This is not a large problem-- only 412 observations. I am using R-1.2.3 with automatic memory allocation. Does this message mean that I need to run

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

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 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 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 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)

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., ...)

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

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" #

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,

I am interested in testing two similar nls models to determine if the lines are statistically different when fitted with two different data sets; one corn, another soybean. I know I can do this in linear models by testing for interactions. See Introductory Statistics with R by Dallgaard p212-218 for an example. I have two different data sets I am comparing to lai. ci.re should have very

Hello everybody, I'm testing the randomForest package in order to do some simulations and I get some trouble with the prediction of new values. The random forest computation is fine but each time I try to predict values with the newly created object, I get an error message. I thought I was because NA values in the dataframe, but I cleaned them and still got the same error. What am I

Dear group, I have these 2 following data frame: allcon <- structure(list(DESCRIPTION = structure(1:17, .Label = c("COFFEE C Jul/10", "COPPER May/10", "CORN Jul/10", "CORN May/10", "COTTON NO.2 Jul/10", "CRUDE OIL miNY May/10", "GOLD Jun/10", "HENRY HUB NATURAL GAS May/10", "ROBUSTA COFFEE (10)

Doug, I have been attempting to learn a little bit about nlme without too much documentation except the online help. The Latex file in the nlme directory looks interesting but uses packages that I do not have so that I have not been able to read it. I have run the example from gnls to compare it with the results I get from my libraries (code below - I have not included output as it is rather