similar to: Post hoc tests with lme

In the 'doBy' package there is an esticon() function for calculating linear contrasts for various model types. I have defined an S3-method 'esticon.mer()' for 'mer' objects from the lme4 package. Building the package and invoking the method gives: > esticon(fm1, c(1,1)) Confidence interval ( WALD ) level = 0.95 Error in as.integer(x) : cannot coerce type 'S4'

Hi, I want to test for a contrast from a regression where I am regressing the columns of a matrix. In short, the following. X <- matrix(rnorm(50),10,5) Y <- matrix(rnorm(50),10,5) lm(Y~X) Call: lm(formula = Y ~ X) Coefficients: [,1] [,2] [,3] [,4] [,5] (Intercept) 0.3350 -0.1989 -0.1932 0.7528 0.0727 X1 0.2007 -0.8505 0.0520

Hi, Is there a simple command for computing the standard error of a combination of parameter estimates in a GLM? For example: riskdata$age1 <- riskdata$age riskdata$age2 <- ifelse(riskdata$age<67,0,riskdata$age-67) model <- glm(death~age1+age2+ldl, data=riskdata,family=binomial(link=logit)) And we want to find the standard error of the partial linear predictor for

I've moved nlme from Depends to Imports in my coxme package. However, a few of the examples for lmekin use one of the data sets from nlme. This is on purpose, to show how the results are the same and how they differ. If I use data(nlme::ergoStool) the data is not found, data(nlme:::ergoStool) does no better. If I add importFrom(nlme, "ergoStool") the error message is that

Dear R users, I would like to exactly reproduce a figure like the 1.5 or 1.9 or 4.13 from the book Mixed effects models in S and S-Plus. Not for the sake of it, but because I have my own data I would like to plot in that fashion (no colors) If I write plot(ergoStool) I can get a good informative plot with colors, but I would like to have a B&W one instead. I've played a little with

Hi all, I have a questions regarding mixed effects models: I'm trying to plot e.g. fitted vs residuals for each level of the random effects, and i'm getting the same error. I guess this might be a problem of the graphic capabilities of R. Is there any way to obtain those plots? library(nlme) attach(ergoStool) names(ergoStool) [1] "effort" "Type"

Is there a function/package that will allow you to test the hypothesis beta1 = x in a simple linear regression, where x is a constant? The AOV tests if beta1 in different from 0 but what if I want to know if beta1 one is different from 1? With best wishes and kind regards I am Sincerely, Corey A. Moffet Instructor Department of Range, Wildlife, and Fisheries Management Mail Stop 2125 Texas

Greetings- I'm working through Pinheiro and Bates' _Mixed Effects Models in S and S-Plus_ using R (1.3.1 for linux). On page 13 (okay, so I haven't got that far :)) is: plot.design( ergoStool) which returns on my system: > plot.design(ergoStool) Error: couldn't find function "plot.design" any ideas? Thanks.

Hi, I am trying to test some contrasts, using glht() in multcomp package on fixed effects in a linear mixed model fitted with lme() in nlme package. The command I used is: ## a simple randomized block design, ## type is fixed effect ## batch is random effect ## model with interaction dat.lme<-lme(info.index~type, random=~1|batch/type, data=dat) glht(dat.lme, linfct = mcp(type

There has been some recent discussion on this list about the value of using intervals with lme() to check for whether a model is ill-defined. My question is, what else can drive very large confidence intervals for the variance components (or cause the error message "Error in intervals.lme(Object) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate

Hello All, Is there a way of producing an ANOVA table split into contrasts, thus showing the contrasts sums of squares and associated p-values? Thanks, Martin. Martin Hoyle, School of Life and Environmental Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK Webpage: http://myprofile.cos.com/martinhoyle

Hello I want to compare the slope (let's say 'b') of a linear model obtained with lm() to a theoretical value (let's say 'th'). To do so, I think I should compute a 't value' using something like : (b - 'th')/standard.deviation(b) and then look at the p-value of this computed t. I don't understand how to do this in a simple way, just using lm()

Hello, suppose I have a multivariate multiple regression model such as the following: > DF<-data.frame(x1=rep(c(0,1),each=50),x2=rep(c(0,1),50)) > tmp<-rnorm(100) > DF$y1<-tmp+DF$x1*.5+DF$x2*.3+rnorm(100,0,.5) > DF$y2<-tmp+DF$x1*.5+DF$x2*.7+rnorm(100,0,.5) > x.mlm<-lm(cbind(y1,y2)~x1+x2,data=DF) > coef(x.mlm) y1 y2 (Intercept)

Version 0.6.0 of the gregmisc package has just been released on CRAN. Description: =========== Package: gregmisc Description: Misc Functions written/maintained by Gregory R. Warnes Title: Greg's Miscellaneous Functions Version: 0.6.0 Date: 2002/09/23 Maintainer: Gregory R. Warnes <Gregory_R_Warnes at groton.pfizer.com> Author: Gregory R. Warnes. Includes code provided by Ben

Version 0.6.0 of the gregmisc package has just been released on CRAN. Description: =========== Package: gregmisc Description: Misc Functions written/maintained by Gregory R. Warnes Title: Greg's Miscellaneous Functions Version: 0.6.0 Date: 2002/09/23 Maintainer: Gregory R. Warnes <Gregory_R_Warnes at groton.pfizer.com> Author: Gregory R. Warnes. Includes code provided by Ben

Dear Greg, Thanks for the new release. The decomposition of the SSQ is just what I need! Regards, Martin. Martin Hoyle, School of Life and Environmental Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK Webpage: http://myprofile.cos.com/martinhoyle >>> gregory_r_warnes at groton.pfizer.com 10/30/02 07:16PM >>> Version 0.7.3 of the gregmisc package

I'm looking at Pinheiro & Bates "Mixed-Effects Models in S and S-PLUS" at the moment. Several datasets are used, one of which is called "PBIB" (a partially balanced incomplete block design). All the other datasets can be found somewhere or other in R. However, I cannot locate PBIB, and it does not seem to be mentioned in the latest edition of the R Full Reference

I'm looking for a tutorial or notes on the use of contrasts factor in linear model in R, I've found some mails and infos about in various documents about R, but I've probably missed a good review on this subject. -- Robert Espesser Laboratoire Parole et Langage UMR 6057, CNRS 29 Av. Robert Schuman 13621 AIX (FRANCE) Tel: +33 (0)4 42 95 36 26 Fax: +33 (0)4 42 59 50

Hello, I'm wondering what's the best way to analyse an unbalanced design with a low number of replicates. I'm not a statistician, and I'm looking for some direction for this problem. I've a 2 factor design: Factor batch with 3 levels, and factor dose within each batch with 5 levels. Dose level 1 in batch one is replicated 4 times, level 3 is replicated only 2 times. all

Version 0.8.0 of the gregmisc package is now (or will shortly be) available on CRAN. New in this release: - Enhanced and bug-fixed 'CrossTable' function (contributed by Marc Schwartz) - Augmented the 'barplot2' function with an 'add' argument to allow for the addition of a barplot to an existing graphic. (contributed by Marc Schwartz) - Added the