similar to: Crossed random factors in lme

Dear all, I am trying to fit a model with crossed random effects using lme. In this experiment, I have been measuring oxygen consumption (mlmin) in bird nestlings, originating from three different treatments (treat), in a respirometer with 7 different channels (ch). I have also measured body mass (mass) for these birds. id nest treat year mlmin mass ch hack 1EP51711 17

On page 165 of Mixed-Effects Models in S and S-Plus by Pinheiro and Bates there is an example of using lme() in the nlme package to fit a model with crossed random factors. The example assumes though that the data is grouped. Is it possible to use lme() to fit crossed random factors when the data is not grouped? E.g., y <- rnorm(12); a=gl(4,1,12); b=gl(3,4,12). Can I fit an additive model

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

I have an interesting lme - problem. The data is part of the Master Thesis of my friend, and she had some problems analysing this data, until one of her Jurors proposed to use linear mixed-effect models. I'm trying to help her since she has no experience with R. I'm very used to R but have very few experience with lme. The group calls of one species of parrot were recorded at many

I would like to be able to use lme to fit random effect models In which some but not all of the random effects are constrained to be independent. It seems as thought the pdMat options in lme are a promising avenue. However, none of the existing pdMat classes seem to allow what I want. As a specific example, I would like to fit a random intercept/slope mixed model to longitudinal observations in

I want to fit a random effects model with two separate nested random effects. I can easily do this using the `lmer` package in R. Here's how: model<-lmer(y ~ 1 + x + (1 | oid/gid) + (1 | did/gid), data=data) Here, I'm fitting a random intercept for `oid` nested within `gid` and `did` nested within `gid`. This works well. However, I want to fit a model where the variance of the

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

Hi- I want to fit a model with crossed random effects and heteroskedastic level-1 errors where inferences about fixed effects are of primary interest. The dimension of the random effects is making the model computationally prohibitive using lme() where I could model the heteroskedasticity with the "weights" argument. I am aware that the weights argument to lmer() cannot be used to

Dear R-listers, I am fitting bivariate mixed models for cost-effectiveness data of cluster randomized trials using lme in R. So I have individuals nested within clusters. My response variable is a vector with bivariate response (individual level costs and effects) stacked into a single column. The covariates in my models are a constant and a treatment term. They are response-specific, e.g. a

Dear all, In the SASmixed package there is an example of an analysis of a split-plot experiment. The model is fm1Semi <- lme( resistance ~ ET * position, data = Semiconductor, random = ~ 1 | Grp) where Grp in the Semiconductor dataset is defined as ET*Wafer. Is it possible to specify the grouping directly some way, e.g. like fm1Semi <- lme( resistance ~ ET * position, data =

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

Hi, lme() users, Can some one tell me how to do this. I model Orthodont with the same G for random variables, but different R{i}'s for boys and girls, so that I can get sigma1_square_hat for boys and sigma2_square_hat for girls. The model is Y{i}=X{i}beta + Z{i}b + e{i} b ~ iid N(0,G) and e{i} ~ iid N(0,R{i}) i=1,2 orth.lme <- lme(distance ~ Sex * age, data=Orthodont, random=~age|Subject,

Hi, I am working with R version 2.1.0, and I seem to have run into what looks like a bug. I get the same error message when I run R on Windows as well as when I run it on Linux. When I call anova to do a LR test from inside a function, I get an error. The same call works outside of a function. It appears to not find the right environment when called from inside a function. I have provided

I have a data set with 50 different x values and 5 values for the sampling variance; each of the 5 sampling variances corresponds to 10 particular x values. I am trying to fit a mixed effect linear model and I'm not sure about the syntax for specifying the fixed variance structure. In Pinheiro's book my situation appears to be similar to the example used for varIdent, where there is a

Hi All I was wondering how to get the same results with gls and lme. In my lme, the design matrix for the random effects is (should be) a identity matrix and therefore G should add up with R to produce the R matrix that gls would report (V=ZGZ'+R). Added complexity is that I have 3 levels, so I have R, G and say H (V=WHW'+ZGZ'+R). The lme is giving me the correct results, I am

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

Hi everyone, I'm trying to fit a of piecewise regression model on a time series. The idea is to divide the series into segments and then to apply linear regression models on each segment but in a "global way" and considering heteroskedasticity between the segments. For example, I build a time series y with 3 segments: segment1=1:20+rnorm(20,0,2) segment2=20-2*1:30+rnorm(30,0,5)

Hello, I have a simulated data structure in which students are nested within teachers, and with each student are associated two test scores. There are 20 classrooms and 25 students per classroom, for a total of 500 students and two scores per student. Here are the first 10 lines of my dataframe "d": studid tchid Y time 1 1 1 -1.0833222 0 2 1 1

Hello: I have an lme object, say lme_res2, which was generated using the varIdent. I'm trying to extract the double 1.532940 from the object, but I can't find it by attributes(lme_res2) or attributes(summary(lme_res2)). How can I pull it out (so that I can save it to another variable)? Thanks. Shin Linear mixed-effects model fit by REML Data: dat Log-restricted-likelihood: