similar to: [OT] good reference for mixed models and EM algorithm

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN

Dear List, I am new to TS-Modeling in R. I would like to fit an ARMA(1,1) model for a balanced panel, running Y on a full set of unit and year dummies using an arma(1,1) for the disturbance: y_it=unit.dummies+yeardummies+e_it where: e_it=d*e_it-1+u_it+q*u_it-1 How can I fit this model in R? arma() does not seem to take covariates (or I don't understand how to specify the function so that

I have 2 questions on ANOVA with 1 between subjects factor and 2 within factors. 1. I am confused on how to do the analysis with aov because I have seen two examples on the web with different solutions. a) Jon Baron (http://www.psych.upenn.edu/~baron/rpsych/rpsych.html) does 6.8.5 Example 5: Stevens pp. 468 - 474 (one between, two within) between: gp within: drug, dose aov(effect ~ gp * drug *

I can ask my question using and example from Chapter 1 of Pinheiro & Bates. > # 1.4 An Analysis of Covariance Model > > OrthoFem <- Orthodont[ Orthodont$Sex == "Female", ] > fm1OrthF <- + lme( distance ~ age, data = OrthoFem, random = ~ 1 | Subject ) > summary( fm1OrthF ) Linear mixed-effects model fit by REML Data: OrthoFem AIC BIC

I am introducing the scan() function to my class. Consider the following file (Scanexamp.Rnw ) \documentclass[12pt]{article} \begin{document} <<>>= height = scan() 64 62 66 65 62 69 72 72 70 part = scan(what = character(0)) "Soprano" "Soprano" "Soprano" "Alto" "Alto" "Tenor" "Tenor" "Bass"

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Still having that problem with samba seemingly corrupting files, it seems to be the same files that are corrupted again and again, yet they are corrupted differently and sometimes not. I ran my md5 summer against one file ch02.pdf, and it almost contineously corrupts, I checked in a hex editor and there are some differences. Yet the problem is that

I tried to use LME (on a fairly large dataset, so I am not including it), and I got this error message: Error in lme.formula(formula(paste(c(toString(TargetName), "as.factor(nodeInd)"), : nlminb problem, convergence error code = 1 message = false convergence (8) Is there any way to get more information or to get the potentially wrong estimates from LME? (Also, the page in the

Hello. As advised by Mick Crawley in his book on S+, I'm trying to use the lme function to examine a linear relationship between two variables measured at 60 locations in 12 sites, while taking account of any spatial autocorrelation (i.e. similarity in variation between the two variables that is due to site). I am using the function as follows:

Hello, I have an irregular time series of the form : Time Data Time1 Data1 .... 1 b 1 e 7 g 4 i NA NA 5 k NA NA NA NA ... (the columns have varying length of NAs after a certain point) Converting this to regular time series with Pastecs does not seem to work, when I see the entire data as a single series. So I remove

I found myself wanting to average a vector [vec] within each level of a factor [Fac], returning a vector of the same length as vec. After a while I realised that lm1 <- lm(vec ~ Fac) fitted(lm1) did what I want. But there must be another way to do this, and it would be good to be able to apply other functions than mean() in this way. Cheers, Murray -- Dr Murray Jorgensen

Thanks a lot for the quick answerd I received and that helped me to solve my first (simple) problem with the plm package. Unfortunatley, here's another one: I do not know why, but I'm unable to estimate a simple model in the form y ~ 1 + x When I try > zz <- plm(y ~ 1 + x , data=mydata) I obtain Errore in X.m[, coef.within, drop = F] : numero di dimensioni errato which means

I've written a package to assist with using R in Hadoop Streaming. The main point of the package is to help make command-line runnable RScript files. I'd like to provide a demo RScript file, a demo data file (e.g. a plaintext file, not something already processed by R) , as well as demo bash shell scripts that demonstrate how to run the job from the command line and in a Hadoop

Does anyone know of a way of dealing with unbalanced mixed effects (fixed and random factors) for fully factorial designs. An example of such data is given below; The response variable is SQRTRECRUITS SEASON is a random factor DENSITY is a fixed factor Thus DENSITY:SEASON is a fixed factor. Therefore, whereas the effects of SEASON and DENSITY:SEASON should be tested against the overall

I'm going to try my hand at converting some Fortran programs to R. Does anyone know of any good articles giving hints at such tasks? I will post a selective summary of my gleanings. Cheers, Murray -- Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html Department of Statistics, University of Waikato, Hamilton, New Zealand Email: maj at waikato.ac.nz

I am exploring the result of clustering a large multivariate data set into a number of groups, represented, say, by a factor G. I wrote a function to see how categorical variables vary between groups: > ddisp <- function(dvar) { + csqt <- chisq.test(G,dvar) + print(csqt$statistic) + print(csqt$observed) + print(round(csqt$expected)) + round(csqt$residuals) + } > > x

Consider the following: > A <- 1:10 > A [1] 1 2 3 4 5 6 7 8 9 10 > dim(A) NULL > dim(A) <- c(2,5) > A [,1] [,2] [,3] [,4] [,5] [1,] 1 3 5 7 9 [2,] 2 4 6 8 10 > dim(A) [1] 2 5 > dim(A) <- 10 > A [1] 1 2 3 4 5 6 7 8 9 10 > dim(A) [1] 10 Would it not make sense to have dim(A) = length(A) for all vectors?

Dear R community, I have trouble implementing a nested variance-covariance matrix in the lme function. The model has two fixed effects called End and logpgc, the response variable is the logarithm to base 2 of Intensity ( log2(Intensity) ) and the random effects are called Probe and ProbeNo. The model has the following nesting structure: A Pixel is nested within the ProbeNo,the ProbeNo is

Hi R People: If you have a linear programming problem in which some of the constraints have the "<=", some have ">=" and some have "=", all in the same problem, should the solver work? I'm having trouble with that. Any help much appreciated! Sincerely, Erin Hodgess mailto: hodgess at gator.uhd.eud whoops! mailto: hodgess at gator.uhd.edu

Dear listers, I am trying to assess variance components for a nested, mixed-effects model. I think I got an answer that make sense from R, but I have a warning message and I wanted to check that what I am looking at is actually what I need: my data are organized as transects within stations, stations within habitats, habitats within lagoons. lagoons: random, habitats: fixed the question is:

The following reproducable code shows the setting of my problem: set.seed(260808) n = 50 x = rnorm(n) y = rnorm(n) z = ceiling(runif(n,0,4)) g = runif(n,0,6) G = factor(ceiling(g)) xyplot(y ~ x | G) plsy <- trellis.par.get("plot.symbol") plsy$pch = z trellis.par.set("plot.symbol",plsy) xyplot(y ~ x | G) plsy$pch = as.character(z)