similar to: limitation in the number of covariates in nlme

Dear All I'm attempting to speed up my model building procedure, but need some help with the loops I've created...please bear with me through the explanation! My basic model call is something like: m0sulf.nlme<-nlme(conc~beta0*exp(-beta1*day)+beta2*exp(-beta3*day), data=m0sulf, fixed=(beta0+beta1+beta2+beta3~1),

Hi everybody, as you may be aware the function augPred.lme does not work as soon as the covariate is a factor. The problem lies in the line newprimary <- seq(from = minimum, to = maximum, length.out = length.out) which does not make sense for factors. I think augPred.lme can be useful for models with a factor covariate as well. Thus, I propose to change the code to: augPred.lme <-

Dear R users, I am using nlme to fit a pharmacokinetic model. The base model is parameterized in terms of CL, V1, V2 and Q. basemodel<-nlme(Conc ~TwoCompModel(CL,Q,V1,V2,Time,ID), data = data2, fixed=list(CL+Q+V1+V2~1), random = pdDiag(CL+V1+V2~1), start=c(CL=log(20),Q=log(252),V1=log(24.9),V2=log(120)), control=list(returnObject=TRUE,msVerbose=TRUE, msMaxIter=20,pnlsMaxIter=20,pnlsTol=1),

HLM question? Is there a minmum number of observations required for a category..I have individusals in work teams.I have incomplete data for all the teams ..sometimes I only have data for one person in a team.I assume that HLM can't work here! But what would be the mimimal.at the moment I have a sample of about 240 in about 100 teams with teamsizes form 2 to 5. Any advice? Thanks

Dear list In HLM, one can specify a covariate at one of the "levels". For example, if the data structure are repeated observations nested within students nested within schools, school size might be a covariate that is used at level 3, but not at the other levels. In HLM this is rather easy to do. However, how can one specify a covariate in R for only one of the levels? I have a

Hello, I'm trying to adjust a non linear model in which the biological response variable (ratio of germinated fungus spores) is dependent on 2 covariates (temperature and time). The response to temperature is modeled by a kind of beta function with 2 parameters (optimal and maximum temperatures) and the time function is a 2-parameter Weibull. Adjustments with nls or gnls work, but I need to

Hi all, I've been struggling with trying to specify a diagnoal matrix for linear mixed effects model. I think I've got nearly everything correct, except the following message appears: In lme.formula(fixed = fwave ~ sex + sexXbulbar + visit + age + : Fewer observations than random effects in all level 1 groups Not sure if i've provided enough details, but I'm basically trying

I am trying to explore nlmer by running some nlme examples from Pinheiro & Bates (2000). I do not seem to find information how to specify fixed effects covariates to nlmer models. Specifically, I tried to run the "Carbon Dioxide Uptake" example from p. 368 onwards in the PB200 book. The model without fixed effects covariates runs well but how to tell nlmer to include Type and

In the non linear mixed effects package a groupedData object can be created to facilitate modeling. The gD object includes a formula of the form 'response variable' ~ 'primary covariate' | 'grouping factor'. In experiments creating response surfaces there are 2 or more primary covariates. Is there any way to use the groupedData() function to include 2 primary

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 R community, I am trying to fit an nlme model where I want to estimate the fixed effects of two treatments on the parameters on the following equation Photo~(a*(1-exp(-c*PARi/a)))-b I was able to fit a simple model without covariates following the method described in Mixed-Effects Methods and Classes for S and S-PLUS, version 3.0, but when I add the covariates, I get the error "

Hi I am following the model building strategy that is outlined in the Pinheiro and Bates book wrt including covariates but am having a problem with the plot. Basically I am using 4 covariates (1 of them is continuous) and 3 of them are fine but the 4th one is being shown as a scatterplot despite the fact that it is a factor. I have explicitly declared this to be a factor (pcat<-as.factor(pcat))

Dear All, I am doing a growth modeling using NLME. I have three levels in my data: observation, individual, household. About half of my total sample have missing values in my household-level covariates. Under this situation, the best way to go is probably to multiply impute the data (for, say, 5 times), estimate the same model separately on each model using LME function, and merge the results. My

Hi I have been asked by a colleague to perform a statistical analysis which uses random effects - but I am struggling to get this to work with nlme in R. Help would be very much appreciated! Essentially, the data consists of: 10 patients. Each patient has been given three different treatments (on three separate days). 15 measurements (continuous variable) have been taken from each patient

Hello! I have something like this: test1 <- data.frame(intx=c(4,3,1,1,2,2,3), status=c(1,1,1,0,1,1,0), x1=c(0,2,1,1,1,0,0), x2=c(1,1,0,0,2,2,0), sex=c(0,0,0,0,1,1,1)) and I can easily fit a cox model: library(survival) coxph(Surv(intx,status) ~ x1 + x2 + strata(sex),test1) However, I want to

Answering on a mail from >From Keith Wong <keithw_at_med.usyd.edu.au> Date Sun 04 Jul 2004 - 17:21:36 EST Subject [R] Random intercept model with time-dependent covariates, results different from SAS Hi all I've got a question about the degrees of freedom in a mixed model, calculated with lme from the lme4 package. Since I've no access to the original data

I have a fairly large model: > length(Y) [1] 3051 > dim(covariates) [1] 3051 211 All of these 211 covariates need to be nested hierarchically within a grouping "class", of which there are 8. I have an accessory vector, " cov2class" that specifies the mapping between covariates and the 8 classes. Now, I understand I can break all this information up into individual

Hello, I am trying to model an intensity function with time-varying covariates. Before, I have successfully defined a log likelihood function for a Power-Law Process (lambda(t)=alpha*beta*t^(beta-1)) with two paramters and no covariates for a repairable systems with failure times (t). This function was maximized with R optim. No problem! But now I want to include a covariate indicating a

Hello I am using glmmBUGS to fit a multilevel model. Treatments are nested in Course are nested in Patients. The predicted variable in total EEG duration. The predictors are: at the observation level : Medication dose at the Course level: Weight in KG and Age at the Patient level: Weight in KG and Age I am trying to fit a multilevel model as in lmer, but in BUGS. Here is an example of the

Hi. I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)