similar to: Fix for augPred/gsummary problem (nlme library)

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),

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

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.

Full_Name: Ben Saville Version: 2.1 OS: Windows XP Submission from: (NULL) (152.2.94.145) I'm using the gsummary function to calculate a sum of V1 (column one) from my data 'mytest' by group (V2,or column 2). If V1 (the variable of interest) is all the same value (in this case all 2's), I do not get back the correct summation. If there is at least one difference in V1 (all

Could someone give me a hand with the format of the gsummary function? Basically, I have a large set of xyz coordinates generated by LiDAR data (>37 million points) and I am trying to derive various summary statistics on the z-coordinates by a grid cell. I wrote a function to do this by creating factors from the x- and y- coordinates and then using gsummary. However, I want the function to

dear list: I used the nlme library according to the great Pinheiro/Bates book, on R2.3, WinXp Lac.lme is an lme object with unbalanced data, group is a factor variable with three levels, when I tried to plot the residuals by group I got this error msg: >plot(Lac.lme,resid(.,type='p')~fitted(.)|group) Error in limits.and.aspect(prepanel.default.xyplot, prepanel = prepanel, :

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

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)

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,

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.

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"

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

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

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 --

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

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) >

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 try to refine my nlme models and with partial success. The model is refined and fitted (using Pinheiro/Bates book as a tutorial) but when I try to plot plot(augPred(fit4)) I obtain Error in predict.nlme(object, value[1:(nrow(value)/nL), , drop = FALSE], : Levels (0,3.5],(3.5,5],(5,7],(7,Inf] not allowed for vykon.fac > Is it due to the fact that I have unbalanced

Using formulas constructed from strings only partially works for me in lme: library(nlme) data(Orthodont) fm2<-lme(as.formula("distance~age"),data=Orthodont,random=~1|Subject) summary(fm2) # works augPred(fm2) # fails #Error in inherits(object, "formula") : #Argument "object" is missing, with no default I assume that my use of as.formula is wrong, but