similar to: {nlme} Multilevel estimation heteroscedasticity

Dear helpers, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Hi, I would like to know whether it is possible to obtain a value of significance for random effects when aplying the lme or related functions. The default output in R is just a variance and standard deviation measurement. I feel it would be possible to obtain the significance of these random effects by comparing models with and without these effects. However, I'm not used to perform

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Dear ListeRs, I am trying to fit a heteroscedastic multilevel model using lmer{lme4- package). Take, for instance, the (fictive) model below. lmer(test.result ~ homework + Sex -1 + (1 | School)) Suppose that I suspect the error terms in the predicted values to differ between men and women (so, on the first level). In order to model this, I want the 'Sex'-variable to be random on

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do

Dear All, I am using the function gls (in the nlme package) and I would like to fit a heteroscedastic model, with different variances for each of the levels of two stratification variables. In p. 210 of Pinheiro & Bates ("Mixed effects models in S and S-Plus", 2000, Springer), the authors show the use of the "*" operator. However, that is not what I want, because it

Dear expeRts, I am trying to plot a lme-object {package nlme) in such a way, that on a selected level the x-axis represents the value on a selected predictor and the y-axis represents the predicted-outcome variable. The graphs would than consist of several lines that each represent one group. I can't find such a plotting function. I could write such a function myself, based on

Hello, As I am relatively new to the R environment this question may be either a) Really simple to answer b) Or I am overlooking something relatively simple. I am trying to add a VarIdent structure to my gam model which is fitting smoothing functions to the time variables year and month for a particular species. When I try to add the varIdent weights to variable Month I get this error returned.

Dear R-expert I'm fitting a non linear model (energy allocation model to individual growth data) using your nlme routine. For each individual I have thus a number of observations (age and size) to which I fit the nonlinear function, with random effects for the individuals on the estimated parameters (individual as the grouping factor). The sampling of these individuals was stratified (size

Hi, I am running R2.8.1 under Linux, and I am having trouble using the variance functions in nlme My basic model was something like: model0 <- lme( log(growth) ~ light * species.group , data=data, random=~light|species ) # with 20 odd species divided in 2 groups Following the methods in Pinheiro&Bates I tried to put a variance function in the model: model1 <- update(model0,

My data have heterogeneity of variance (in a categorical variable), do I need to specify a variance structure accounting for this in my model or do GLMMs by their nature account for such heterogeneity (as a result of using deviances rather than variances)? And if I do need to do this, how do I do it (e.g. using something like the VarIdent function in nlme) and in what package? This is my first

I was reading the help but just did not get how to specify starting values for varIdent() of the lme() function, although I managed to do it for corSymm(). Do I specify the values just as they are printed out in an output, like c(1, 1.3473, 1.0195). Or do I need to take the residual and multiply it with these like c(0.2235, 0.2235*1.3473, 0.2235*1.0195) or any other form that I dont know of?

Hello R users- I am new to R, and tried searching the archives and literature for an answer to this - please be patient if I missed something obvious. I am fitting a logistic regression model, and would like to include variance functions (specifically the varIdent function). I cannot figure out how to do this either in glmmPQL (or something similar) for the model with random effects, or in glm

Deal all, new as I am to developing packages for R-Project, I apologize on beforehand for questions that are too obvious. I am trying to 'add' a PDF document containing some detailed information to a package. The way I understand the Rexts.pdf document, I should add my .PDF document to the /inst/doc/ folder, and links to the files should be build automatically. However, after

 Hello everyone, Apologies in advance, as this is partially a stats question and partially an R question.  I have been using a GAM to model the activity level of bats going into and coming out from a forested edge.  I had eight microphones set up in a line transect at each of eight sites, and I am hoping to construct a model for each of 7 species.  My count data has a reverse J-shaped skew and

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 R users, I am trying to get data from the clipboard into R on MacOSX. I tried the following, but got an error message: read.delim("clipboard") Error in file(file, "r") : unable to open connection In addition: Warning message: unable to contact X11 display Obviously, I'm not running R using X11. I'm wondering, can I import data from the clipboard on MacosX?

Dear R-help, I am trying to set up linear mixed effects models in R using the (recommended) nlme package (R version 2.3.1 on a Linux platform). When trying to reproduce an example from Jose Pinheiro & Douglas Bates (2000, p 210) I get the following error message (code to produce message pasted as well): library("nlme") data("Orthodont") vf1Ident <- varIdent(

I've got a gls formula that's a mix of continuous and ordered variables. I wanted to use gls because I wanted to use the varIdent structure. Anyway, attempts to use "predict.gls" choke with the error that the levels I use are not allowed for one of them -- the first one alphabetically, so I'd guess the second would have the same problem. So I have three linked questions --