similar to: lme between-group and within-group covariance

Dear R-users, I have longitudinal data and would like to fit a model where both the variance-covariance matrix of the random effects and the residual variance are conditional on a (binary) grouping variable. I guess the model would have the following form (in hierarchical notation) Yi|bi,k ~ N(XiB+Zibi, sigmak*Ident) bi|k ~ N(0, Dk) K~Bernoulli(p) I can obtain different sigmas (sigma0 and

Dear all, I am working on a data set in which I have sequentially measured egg temperatures ("eggtemp") in birds incubating in different ambient temperatures ("treat", sample data set below), "id" is not replicated within treatment. id treat eggtemp 1 79 3 30.90166 2 42 3 34.94044 3 10 3 32.69945 4 206 3 36.64127 5 23 3 31.80055 6

Dear R-users, I'm currently having trouble with the implementation of a groupedData object in the lme() function. Executing the following function > applyScalingSimp <- function(input.population) > { > ## GA is a time value > varInOrder <- c("GA","weight","grouping","sex") > modelVar <-

Dear R-users, I recently began using the ggplot2 package and I am still in the process of getting used to it. My goal would be to plot on the same grid a number of curves derived from two distinct datasets. The first dataset (called molten.data) looks like this : Column names : Perc, Week, Weight P10 21 333.3554 P90 21 486.0480 P10 22 452.6347 P90 22 563.8263 P10 23 575.0960 P90

Dear R-users, I would have another question about the ggplot() function in the ggplot2 package. All the examples I've read so far in the documentation make use of a single neatly formatted data.frame. However, sometimes, one may be interested in plotting on the same grid information or objects derived from two totally different datasets and customize both displays. I still cannot tell how

Hello, We are attempting to use nlme to fit a linear mixed model to explain bird abundance as a function of habitat: lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) The data consist of repeated counts of birds in sample units across multiple years, and we have two questions: 1) Is it necessary (and, if so, how) to specify the repeated measure (years)? As written,

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to

Dear all, I am studying the possibility of using the nlme package in R to analyse field trials of agricultural crops. I have a problem with the syntax for the modelling of variance covariance structures. I can model the within-group covariance structure using the correlation argument and the covariance structure between different random effects of the same grouping level using

Hi all, I've searched threads and read up on some ways of doing this but I'm having a hard time to get it to work. Here's my basic problem. I have the following linear mixed model y = Xb+Zu+e where u~N(0,s^2*K) where K is a matrix. I read a thread that basically suggested to decompose Zu into ZPD^(1/2) D^(-1/2)P'u so that (D^(-1/2)P'u)~N(0,s'^2I) but I'm not sure

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Dear R-specialists, I am trying to create a Makefile that will first convert all my .Rnw files into .tex files and then, that will run the LaTeX compiler to produce a pdf document. This issue has been discussed before. Hence, I've basically adapted a Makefile I found at http://n4.nabble.com/R-Sweave-R-and-complex-latex-projects-td810020.html#a810023 to make it compatible with a Windows

Hi all, I've been using SAS proc mixed to fit linear mixed models and would like to be able to fit the same models in R. Two things in particular: 1) I have longitudinal data and wish to allow for different repeated measures covariance parameter estimates for different groups (men and women), each covariance matrix having the same structure. In proc mixed this would be done by specifying

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

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

I don't think, this has been answered: > I'm trying to run a 3-way within-subject anova in lme with 3 > fixed factors (Trust, Sex, and Freq), but get stuck with handling > the random effects. As I want to include all the possible random > effects in the model, it would be something more or less > equivalent to using aov > > > fit.aov <- aov(Beta ~ >

Dear listers, I can't find the variance or se of the coefficients in a multilevel model using lme. I want to calculate a Chi square test statistics for the variability of the coefficients across levels. I have a simple 2-level problem, where I want to check weather a certain covariate varies across level 2 units. Pinheiro Bates suggest just looking at the intervals or doing a rather

Hi All. I need to create a first-order autoregressive covariance matrix (AR(1)) for a longitudinal mixed-model simulation. I can do this using nested "for" loops but I'm trying to improve my R coding proficiency and am curious how it might be done in a more elegant manner. To be clear, if there are 5 time points then the AR(1) matrix is 5x5 where the diagonal is a constant

Hi R-masters! I trying model Heart disease mortality in my country with a lme model like this: m1.lme<-lme(log(rdeath)~age*year,random=~age|year,data=dados) where: rdeath is rate of mortality per 100000 person per age and year age: age of death (22 27 32 37 42 47 52 57 62 67 72 77 82) year: year of death (1980:2002) I don?t have problem to fit the model, but in

Hi R-users, I am using lme to fit a linear mixed model with the nlme package, does anyone know if it is possible to obtain standard error estimates of the variance components estimators and an adequate method to test the significance of the variance component? Thanks, Berta. [[alternative HTML version deleted]]