search for: pinhiero

Hi, I have a question about how to do case weight in mixed model using R. I read Pinhiero and Bates (2000) Mixed-Effects Models in S and S-Plus. The weight functions in there are not what I wanted. Can R do case weight, giving each observation my own weight? If so, could you give me some references and examples? I'm looking forward to hearing from you! Tianyue

Dear lattice wizards, I am trying to figure out how to plot predicted values in xyplot, where the intercept, but not the slope, varies among conditioning factor levels. I am sure it involves the groups, but I have been unsuccessful in my search in Pinhiero and Bate, in the help files, or in the archive, or in my attempts on my own. My example follows: FACT is a factor with levels a,b,c COV is the covariate mod ~ lm(Y ~ COV + FACT) #The following draws the right predictions if the relation is the same for all factor levels, but I can't fi...

To anyone who can help, I have two stupid questions, and one fairly intelligent question Stupid question (1): is there an R function to calculate a factorial of a number? That is...is there a function g(.) such that g(3) = 6, g(4) = 24, g(6) = 720, etc? Stupid question (2): how do you extract the estimated covariance matrix of the random effects in an lme object? Intelligent question

I asked this before and I am going to try again in more applied terms. I am trying to use R to extract variance components for a two-factor random effects model with both factors crossed. It would also be nice to generate some confidence intervals as well. For example, a data set using SAS Proc Mixed is below followed by the four variance component estimates and the respective confidence

...orrectly handle unbalanced panel data: “Moreover, economic panel datasets often happen to be unbalanced (i.e., they have a different number of observations between groups), which case needs some adaptation to the methods and is not compatible with those in nlme” (pg. 2, Croissant 2008). However in Pinhiero and Bates (2000, pg 24) the authors state that the lme() does generate sensible Ml and REML estimates for unbalanced data. I would greatly appreciate any insight into how and when the nlme package or functions within it do not produce sensible / accurate results. Sincerely, Anthony Pezzo...

...estimate them with the same action. Working on a paper, I naturally want to be able to do some sort of discussion on the impact of covariates... ;-) What is the wise solution? Or, if this is trying to make other people do my homework, could anyone tell me where the homework is? (I??ve got both Pinhiero & Bates and MASS as well as some others in the bookshelf.) Cheers /CG -- CG Pettersson MSci. PhD.Stud. Swedish University of Agricultural Sciences (SLU) Dep. of Crop Production Ecology (VPE). http://www.slu.se/ cg.pettersson at evp.slu.se

Hi, I would be grateful for help with a problem which is irritating me. I am quite sure that I am doing something stupid, but I can't see what it is. I am running R 1.7 on Windows 2000. The graphics device is the PC screen. The graphics from the nlme demonstration in Bates an Pinheiro's manual work just as advertised. The CO2 data and the Orthodont data dsiplay

Hallo! I have data of the following design: NSubj were measured at Baseline (visit 1) and at 3 following time points (visit 2, visit 3, visit 4). There is or is not a treatment. Most interesting is the question if there is a difference in treatment between the results of visit 4 and baseline. (The other time points are also of interest.) The level of significance is alpha=0.0179 (because of an

I am trying to understand better an analysis mean RT in various conditions in a within subjects design with the overall mean RT / subject as one of the factors. LME seems to be the right way to do this. using something like m<- lme(rt~ a *b *subjectRT, random= ~1|subject) and then anova(m,type = "marginal"). My understanding is that lme is an easy interface for dummy coding

Dear all, Last autumn there was some discussion on the list of the warning Warning message: fitted probabilities numerically 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y, when fitting binomial GLMs with many 0 and few 1. Parts of replies: "You should be able to tell which coefficients are infinite -- the coefficients and their standard errors will

I am using the package nlme to fit a simple random effects (variance components model) with 3 parameters: overall mean (fixed effect), between subject variance (random) and within subject variance (random). I have 16 subjects with 1-4 obs per subject. I need a 3x3 variance-covariance matrix that includes all 3 parameters in order to compute the variance of a specific linear

Dear all, I'm looking for how can I estimate a linear model with ar(ma) errors : y(t)=a*X(t)+e(t) with P(B)e(t)=Q(B)u(t) where u is a white noise and P, Q are some polynomes. Could you help me ? Gr?gory Benmenzer

Dear All, I need to calculate the variance components in a mixed effect model (one fixed and one random effect) with REML (maximizing the proportion of the likelihood that does not depend on the fixed effects). In S+ there is the varcomp function, but I would like to do it in R. Is there a way to do that? Thanks! Katalin ___ Katalin Csillery Division of Biological Sciences University of

Dear all, I would like to simulate data from a nlme model using fixed effects and the variances of the random effects distribution. I scanned the help files and the mailing lists with no succes. Thanks for your help! Peter

I was recently asked to perform a GLM analysis (the person comes from the JMP world) on a repeated measures design. I have found some things using aov but I cannot find anything with glm. In fact, multiple regressions in general with repeated measures seems to be poorly covered in documentation. I remember SPSS has separate commands to handle them. I have within variables SOA and ec and a

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]]

Hello, I am trying to fit a repeated measures linear mixed model (using lme) but I don't want to include any random effects. I'm having trouble (even after consulting Pinheiro & Bates 2000) figuring out how to specify the repeated measure without including it in the specification of a random effect. My data consist of repeated "counts" in "plots" that I wish

One of my colleagues has a data set from a two-level nested design from which we would like to estimate variance components. But we'd like some idea of what the inevitable outliers are doing, so we were looking for something in R that uses robust (eg Huber) treatment and returns robust estimates of variance. Nothing in my collection of R robust estimation packages (robust, robustbase and MASS

Hi, I have a data set collected from 10 measurements (response variables) on two groups (healthy and patient) of subjects performing 4 different tasks. In other words there are two fixed factors (group and task), and 10 response variables. I could analyze the data with aov() or lme() in package nlme for each response variable separately, but since most likely there are correlations among the 10

Hello, I apologize in advance if what I'm about to ask is trivial or has been answered before. In the latter case I would appreciate a pointer to the right list/location I'm trying to model the following experimental design with groupedData and lme in R: Subjects were measured on two tasks (continuous outcome variable is "Prob", tasks are coded as within-subjects factor