similar to: Is this a bug in R?

Dear all, I am trying to fit a mixed-effects non linear regression, but I have some trouble with it. My data are a balanced panel of 904 subjects with 8 observations (at regular periods) per subject. The functional form of my model is Y=Aexp(-BX1)X2 +e. I want to allow parameters A and B to vary among subjects and also include an autocorrelation term. I have already fitted a standard nonlinear

Dear all, I?m analyzing this dataset containing biodiversity indices, measured over time (Week), and at various contaminant concentrations (Treatment). We have two replicates (Replicate) per treatment. I?m looking for the effects of time (Week) and contaminant concentration (Treatment) on diversity indices (e.g. richness). Initial analysis with GAM models showed temporal autocorrelation of

Hello all, I am trying to use lme to examine how a response variable (Chla) changes over time in different treatments (2 Temp & 2 Light levels). Within each treatment combination, there are two replicate tanks (each with unique TankID) with coral fragments in them. All tanks are subject to the same environment until Time=0, when treatments are imposed, and Chla is measured for each

I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

Hello Until now, I always used R for Winwows 32bits without any problem I just installed R for Windows 64bits. When I want to get Rcmdr package, an error occurs : package Hmisc is needed and it is not found When I go on the website of R-cran, I don't find Hmisc for R/Windows 64 bits Sincerely yours Andre LEBERT

I am using R 2.7.0 on a linux platform. I am trying to reproduce a 2002 example using lme from the nlme library. I want to change the otimizer from the default (nlminb) to optim. Specifically, this is what I am trying to do: R> library(nlme) R> library(car) # for data only R> data(Blackmoor) # from car R> Blackmoor$log.exercise <- log(Blackmoor$exercise + 5/60, 2) R>

I am trying to specify a mixed model for my research, but I can't quite get it to work. I've spent several weeks looking thru various online sources to no avail. I can't find an example of someone trying to do precisely what I'm trying to do. I'm hoping some smart member of this mailing list may be able to help. First off, full disclosure: (1) I'm an engineer by trade, so

Dear listers, As an exercise, I am trying to fit a logistic model with nlme. Blue tit pulli (youngs) were weighted occasionnally (for field reasons) along time in 17 nestboxes. Individuals where not idenfied but their age was known. This means that for a given age several measurements were done but individuals could not be identified from a time to the other. This makes repeated values for

Hi, I have been using the function lme() of the package nlme to model grouped data that is auto-correlated in time and in space (the data was collected on different days via a moving monitor). I am aware that I can use the correlation classes corCAR1 and corExp (among other options) to model the temporal and spatial components of the auto-correlation. However, as far as I can tell, I can only

Heya I am fitting linear mixed effects model in R and want to assess the model fit (with Animal number as random factor; repeated measures for Animals): ts.model <- lme(LOG_FOC_MW ~ R_DN_SUM + ANIMAL + SEX+ YY, data = t.data, random = ~ 1 | ANIMAL, correlation=corCAR1(0.2, form = ~1 | ANIMAL ), method='ML', na.action=na.omit)). Is there a possability to easly compute an

Hi R users, The nlme library enabled several alternate error structures useful for longitudinal or repeated-measures data. For example, a continuous AR(1) process: model_2 = update(model_1, correlation = corCAR1(form = ~ time | subject)) Does anybody know if this is available in lme4? Thank you Ben -- View this message in context:

Dear helpers, I tried these models to run in the package nlme, but allways got the same error message... I have a correlation in 5 sessions within a field (n=12) with ten traps in one field. res2a <- lme(response~x+y+z+treatment),correlation = corARMA(form = ~ session|trapfield, p = 1, q = 0), random=~1|field, na.action=na.omit, data=plates, method="ML") res2a <-

Hi folks I have some data to which I've been fitting linear mixed effects models. I am currently using a lme model in the nlme package, with terms for random effects due to repeated measures on individuals and the corCAR1 serial correlation structure. However, there is some suggestion in the data (and from theory) that a breakpoint (change point) model may be more appropriate. Scott, Norman

Hi, I am using GAMMs to show a relationship of temperature differential over time with a model that looks like this:- gamm(Diff~s(DaysPT)+AirToC,method="REML") where DaysPT is time in days since injury and Diff is repeat measures of temperature differentials with regards to injury sites compared to non-injured sites in individuals over the course of 0-24 days. I use the following

Hi colleages! I want to import data from ms-excel and other spreadsheet formats (lotus, etc). Does exist any way to do it within R? The main problem is that many spreadsheet data use comma as decimal separator and not the point as needed in R (and many software for Linux), so importing data first as a tab separated values does not solve this problem. Thanks, Tom?s Revilla

Dear all, I run a gamm with following call : result <- try(gamm(values~ s( VM )+s( RH )+s( TT )+s( PP )+RF+weekend+s(day)+s(julday) ,correlation=corCAR1(form=~ day|month ),data=tmp) )" with mgcv version 1.6.2 No stress about the data, the error is not data-related. I get : Error in isS4(x) : object 'VM' not found What so? I did define the dataframe to be used, and the

R-users - I must preface this question by saying that I'm a relative newbie to both R and mixed-modeling. I'm using lme fit an ANCOVA-like model. My data consist of bone length measurements for a developmental series of two capuchin monkey species. I'm interested in whether the rate of bone length scaling to body mass (i.e., growth) differs between species. My call to lme

Dear all, I am fitting a nonlinear mixed-effects model from a balanced panel of data using nlme. I would like to know whay would be the best options for formally testing for autocorrelation. Is it possible to carry out a Durbin-Watson test on a nlme object? As far as I've seen, I think the durbin.watson function from the car package just works on lm objects. Thank you very much, Antonio

Thankyou for your help with subsetting within a function. I have now tried to apply the same theory in the framework of an lme as follows: fit1.lme <- eval(substitute(lme(fixed=fixed, data=dataframe, random=random, correlation=corCAR1(form= corr), na.action=na.omit, subset=subset),list(subset=subs))) but I get the following error: Error in switch(mode(object), name = , numeric = , call =

Dear R-helpers, I have a time series analysis problem in R: I want to analyse the output of my simulation model which is proportional cover of shrubs in a savanna plot for each of 500 successive years. I have run the model (which includes stochasticity, especially in the initial conditions) 17 times generating 17 time series of shrub cover. I am interested in a possible periodicity of shrub