similar to: R 1.5.0 terminated while plotting groupedData

Dear R list members; I have a 10 x 423 data frame which consisting of response, time, subject, site, plot and covariates (continueous and categorical) measured at the plot level. When the data frame was converted into a groupedData object, a warning appeared > A <- groupedData(ht ~ time | Subject, data = tt, outer = ~ site * plot, + labels=list(y = "Height", x =

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

Dear Experts, May be the problem is still solved, however I tried to find the answer in the archives: I use: > R.version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 7.1 year 2003 month 06 day 16

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 =

Hello all, I am plotting a groupedData object and the key at the top of the graph runs off the page. I don't want to set key=F because it is useful (or would be if I could see it). Is it possible to explicitly cause the key to wrap? I have used this function before with no trouble but now I have just 5 groups with rather long descriptions (which I can't meaningfully shorten).

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,

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

Hallo! I want to plot multiple grouped data in a postscript file using a loop. As I use a loop no plot (or just one empty plot) is generated. Here an example: library(nlme) data(Loblolly) # example data from nlme postscript("PSFile.ps") for (i in 1:1) # just as example { plot(Loblolly) } dev.off() Result: Just an empty PSFile.ps. (Withoput the loop it works. May anybody help? Karl

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

Hi, Does anyone know how to make the color of the lines all black when plotting groupedData with an outer factor: For example, library(nlme) plot(Dialyzer, outer=~QB, key=F) This generated colored curves in R.2.4.1. How to make all the curves black ? (or how to alter the color (type) of lines for the nlme groupedData plotting function in general?) Thanks Qiong

I've been using par() to check the graphics parameters associated with both plot(<fitted linear model>) and plot(<grouped data object>). AFAIK the only differences are in the $cxy, $usr, $xaxp, and $yaxp parameters but the background color for the grouped data plot is grey while the linear model plot has a white background. When I've tried par(bg = "white") prior to

Dear all; Using read.ssd, I tried to import a SAS dataset from a network drive; an error occurred: > stemattr <- read.ssd("g:/olmn2/dyang/siteprod/datasasv8", "stemattr") SAS failed. SAS program at C:\DOCUME~1\dyang\LOCALS~1\Temp\file7785.sas a log and other error products should be in the vicinity Warning messages: 1: sas not found 2: ls not found 3: SAS return

R-helpers; I tried to get a superscripted 3 in the following xyplot example but failed: >data(whiteside) >xyplot(Gas ~ Temp | Insul, whiteside, panel = function(x, y, ...) { panel.xyplot(x, y, ...) panel.lmline(x, y, ...) }, xlab = "Average external temperature (deg. C)", ylab = paste(paste("Gas consumption (1000", expression(ft^3),")"), aspect

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:

Hello, I run R on Windows 2000. When I load lattice library and plot some lme class objects, the graphic window displays a dark-green color. Graphics look ok on the screen, but lines and points become invisible whem I send the plot to a printer because of the background color. Are there ways to reset the background color? Both par(bg=) and windows() do not work. Any ideas? Richard

R-Helpers; This seems simple to set graphic parameter, but I tried plot a graph with 10 intervals in x axis with par(xaxp = c(0, 150, 10) but to no avail: par(xaxp =c (0, 150, 10)) plot(age, y18, type="n", ylab="Height (m)", xlab=" age (Yrs)", font = 2) lines(age, y6, lty = 4, lwd = 2) lnes(age, y10, lty = 1, lwd =2) lines(age, y14, lty = 2, lwd =2)

I generate a groupedData object library(nlme) obj <- groupedData(mg10 ~ time | gp, data = common, outer = ~pct) gp has 101 levels, and pct has 3. There are 38, 25, 38 gps in each of the levels of pct respectively. I fit my model fit.rtg <- lme(mg10 ~ time * group, data = obj, random = ~time * group | gp) Now I try to plot the results. I would like to print 40 panels on each

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

Hello, I'm trying to use the groupedData function and R is giving me the message: Error: can not find function "groupedData" The code is coming from a textbook so I think it should be correct: pigs<-data.frame(cbind(pig.time,pig.id,pig.wt)) pig.growth<-groupedData(pig.wt~pig.time|pig.id,data=pigs) I have added the package "nlme" and to confirm that it was installed

Hi I use gnls to fit non linear models of the form y = alpha * x**beta (alpha and beta being linear functions of a 2nd regressor z i.e. alpha=a1+a2*z and beta=b1+b2*z) with variance function varPower(fitted(.)) which sounds correct for the data set I use. My purpose is to use the fitted models for predictions with other sets of regressors x, z than those used in fitting. I therefore need to