similar to: my error with augPred

Dear all I encountered strange behaviour of augPred with virtually the same data First I made groupedData object. > mar.g<-groupedData(rutilizace~doba|int, data=mar) When I perform nlme on complete dataset I get an error with augPred > fit<-nlsList(rutilizace~SSasymp(doba, Asym, R0, lrc), data=mar.g) Warning message: c("1 error caught in nls(y ~ cbind(1 - exp(-exp(lrc) * x),

Hi Well, yes I tried it about two weeks ago but my post did not get through as it still awaits moderator approval. I could check which column is offending but actually it is only minor nuisance, I can live with selection of columns before fitting a model. What seems to me strange is that both full dataset and only selected colums gave me identical fit results but only one works within augPred.

Better posted on r-sig-mixed-models , no? 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 Wed, Aug 23, 2017 at 5:17 AM, PIKAL Petr <petr.pikal at precheza.cz> wrote: > Dear all > > I encountered strange

> On Aug 23, 2017, at 8:08 AM, PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Hi > > Well, yes I tried it about two weeks ago but my post did not get through as it still awaits moderator approval. It got through just fine. It appeared on Aug 15. It just didn't get any replies. As I read your original question in this thread, it was not clear to me that you had

SSweibull() :  problems with step factor and singular gradient Hello I am working with growth data of ~4000 tree seedlings and trying to fit non-linear Weibull growth curves through the data of each plant. Since they differ a lot in their shape, initial parameters cannot be set for all plants. That’s why I use the self-starting function SSweibull(). However, I often got two error messages:

Could someone explain the meaming of this error message from augPred: > augPred(area3.pen.nlme, primary=~day) Error in predict.nlme(object, value[1:(nrow(value)/nL), , drop = FALSE], : Levels 1,2,3 not allowed for block > predict.nlme(area3.pen.nlme) does not produce an error. area3.pen.nlme was created with: > area3.pen.nlme <- nlme(area ~ SSlogis(day, Asym, xmid, scal),

Dear all I am trying nlme together with Pinheiro/Bates book. I constructed grouped data object with suitable plotting layout (according to some common factor, panels from bottom to top are in increasing order). When I do nlme(... some stuff...) I get fitted object which I can plot with plot(augPred(fit.nlme6, level=0:1)) but it results in completely different ordering. Is there any way

Hello, I want to use lme to fit two (or more) models, and then compare the fits on each individual. I know how to write my own code to do this (for each individual, plot the raw data, followed by lines() to plot each fitted curve) but I would like to use plot(augPred(... as it produces a nice trellis plot. I thought I could do this with par(new=T) but it does not seem to work.

All, I have a question RE plotting the prediction lines of a random effects model via augPred. I'll illustrate via the Pixel dataset within the nlme package: library(nlme) attach(Pixel) fm1Pixel = lme(pixel ~ day + I(day^2), data = Pixel, random = list(Dog = ~ 1)) plot(augPred(fm1Pixel)) ### 10 fitted lines since there are 10 dogs fm2Pixel = update(fm1Pixel, . ~ . + Side)

Hi List, I have to compare a relationship between y and x for two locations. I found logistic regression fits both datasets well, but I am not sure how to test if relationships for both sites are significantly different. I searched the r site, however no answers exactly match the question. I used Tukey's HSD to compare two means, but the relationship in my study was not simply linear. So I

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

Dear all as I did not get any response on my post about abline and plot(augPred)) I try again. I hope I do not break some posting guide rules. I would try to contact package maintainer directly but there is stated to be R-core people, so I feel R-help list shall be OK. I need to draw straight lines through augPred plotted panels (vertical or horizontal) at specified point. I know I shall

I read the posts about augPred with lme, but does anyone know if there is a correlate for augPred for lmer? Specifically, I want to be able to use it to plot projections for all groups in an lmer class object using plot(augPred(lmer.object)). Thanks, Kyle *************************************** J. Kyle Roberts, Ph.D. Baylor College of Medicine Center for Educational Outreach One Baylor Plaza,

Hi using this code example: library(nlme) fm1 <- lme(Orthodont, random = ~1) plot(augPred(fm1)) is there any way to have the plots in each cell labelled and ordered according to Orthodont$Sex? I.e. in addition to the bar with the label for Orthodont$Subject there is another bar labelling the Sex of the subject? thanks a lot christoph --

Hello, Is there a way to get augPred to work with lme if a subset statement is used? For example, if I modify the example from ?augPred.lme to include a subset statement, I get the following error: fm1 <- lme(Orthodont, random = ~1, subset=distance>19) augPred(fm1, length.out = 2, level = c(0,1)) Error in tapply(object[[nm]], groups, FUN[["numeric"]], ...) : arguments

Dear R-users, I am a newbie to this site and a relative new-comer to S/R, so please tread lightly, for you tread... There have been several posting relating to problems with augPred() from the nlme library. Here is a "fix" for one of these problems which may lie at the root of others. In my case the problem with augPred() lay in gsummary(), which augPred() uses, causing it to fail.

Dea-R community. I'd like to draw your attention to an issue I have recently encountered while doing my current data analysis. I've got an unexpected (to me) result from the command: > augPred(lmList(my.object)), 'my.object' being a grouped data frame of class: > class(my.object) [1] "nfnGroupedData" "nfGroupedData" "groupedData"

Hi, I'm new at this, I'm very confused, and I think I'm missing something important here. In our pet example we have this: > fm <- lme(Orthodont) > plot(Orthodont) > plot(augPred(fm, level = 0:1)) which gives us a trellis plot with the females above the males, starting with "F03", "F04", "F11", "F06", etc. I thought the point of

All, I'm trying to create an augPred plot in the nlme library, similar to the plot on p.43 of Pinheiro & Bates (Mixed Effects Models in S and S-Plus) for their Pixel data. My data structure is the same as the example but I still get the error msg below. > comp.adj.UKV <- groupedData(adj.UKV ~ Time | Patient_no/Lisinopril, data = comp.adj.UKV.frm, order.groups = F) >

Dear list members, I am working with a multilevel growth curve, that in its simplest form goes like follows: Yit = Ai + Bi t + eit (the error term is assumed to follow an AR(1) autorregressive process) One major topic in my research is the convergence in the values of Y over time. Thus, I am interested in the relationship between the random effects for the intercept and the slope, and I