similar to: Correction of degrees of freedom in repeated measure aov

I have 3 questions (below). Background: I am teaching an introductory statistics course in which we are covering (among other things) repeated measures anova. This time around teaching it, we are using R for all of our computations. We are starting by covering the univariate approach to repeated measures anova. Doing a basic repeated measures anova (univariate approach) using aov() seems

I'm an experimental psychologist and when I run ANOVA analysis in SPSS, I normally ask for a test of non-sphericity (Box's M-test). I also ask for output of the corrections for non-sphericity, such as Greenhouse-Geisser and Huhn-Feldt. These tests and correction factors are commonly used in the journals for experimental and other psychology reports. I have been switching from SPSS to R

Hi all, I was wondering whether there are any packages that provide for the Greenhouse-Geisser correction, an adjustment used in univariate repeated measures when the sphericity assumption is violated (both numerator and denominator degrees of freedom are multiplied by GG-epsilon, and the significance of the F ratio is evaluated with the new degrees of freedom)? I have seen a few emails with

Hi, I've looked in the mailing list archives and found some questions related to Greenhouse-Geisser epsilon correction. Are there any packages providing that? Thanks, Sven -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi, sorry if this is explained somewhere but I didn't find anything. How can I use "Anova" from the car package to test a modell without between subject's factors? Suppose I have the following data mat.1 mat.2 mat.3 di ex 1 85 85 88 1 1 2 90 92 93 1 1 3 97 97 94 1 1 4 80 82 83 1 1 5 91 92 91 1 1 6 83 83

I have a between-subjects factor, A, with three levels, and a within-subjects factor with 4 levels. There are 6 subjects at each A level. I get the correct F tests using the aov function with the stack form of the data frame but I want the epsilon adjustment (Greenhouse-Geisser, Huynh-Feldt). for nonspheriicty as well. I got the SPSS results using the standard broad data frame (a row for

Hi, we are just about to evaluate R as a standard statistics package for our institute. We looked around the help - manual, the FAQ, etc. and did not find anything for the topics of random factors in ANOVA, repeated measures in ANOVA, or within subjects designs in R. Could anyone point us to any information concerning - univariate approach to repeated measures anova - multivariate

Dear R folk, First off I want to thank those of you who responded with comments for my R quick and dirty stats tutorial. They've been quite helpful, and I'm in the process of revising them. When it comes to repeated measures MANOVA, I'm in a bit of a bind, however. I'm beginning to see that all of the documentation is written for psychologists, who have a slightly

Hi, I'm running a repeated measure ANOVA to test the effects of the within-subjects factors "congr", "isi" and their interaction, on the variable "latencies" aov(latencies ~ congr*isi + Error(subj/(congr*isi)),data=dats) "congr" has 4 levels, while "isi" has "3" levels. I would like to check for possible violations of the

Hi, I'm still trying to figure out that GLM procedure in SAS. Let's start with the simple example: PROC GLM; MODEL col1 col3 col5 col7 col9 col11 col13 col15 col17 col19 col21 col23 =/nouni; repeated roi 6, ord 2/nom mean; TITLE 'ABDERUS lat ACC 300-500'; That's the same setup that I had in my last email. I have three factors: facSubj,facCond and facRoi. I had this pretty

Dear all, I apologize for my basic question. I try to calculate an anova for repeated measurements with 3 factors (A,B,C) having 2, 2, and 7 levels. or with an additional fourth between subjects factor D. Everything works fine using aov(val ~ A*B*C + Error(subject/ (A*B*C) ) ) or aov(val ~ (D*A*B*C) + Error(subject/(A*B*C)) + D ) val, A, B, C, D and subject are columns in a data.frame. How

Dear R Masters, I'm an anesthesiology resident trying to make his way through basic statistics. Recently I have been confronted with longitudinal data in a treatment vs. control analysis. My dataframe is in the form of: subj | group | baseline | time | outcome (long) or subj | group | baseline | time1 |...| time6 | (wide) The measured variable is a continuous one. The null hypothesis in

Data set up as one observation/subject looks like (with a total of 10 subjects) Two treatments: shoe type with 3 categories and region with 8 categories ==> 24 "treatment" columns Subject PHallux PMidToes PLatToe PMTH1 PMidMTH PLatMTH PMidfoot PRearfoot LHallux LMidToes LLatToe LMTH1 LMidMTH LLatMTH LMidfoot LRearfoot DHallux DMidToes DLatToe DMTH1 DMidMTH DLatMTH

Hi, yet another anova.mlm problem - it doesn't seem to end. This time, I have a setup with a few within-subject factors and a between-subject factor (SGROUP). Consider the most simple case with only one within-factor (apo): > mlmfit0 <- lm(data.n ~ 0 + SGROUP) > mlmfit1 <- lm(data.n ~ 1 + SGROUP) > anova(mlmfit1,mlmfit0,test="Spherical",M=~hemi,X=~1) Analysis of

I have a problem moving from multistratum aov analysis to lmer. My dataset has observations of ampl at 4 levels of gapf and 2 levels of bl on 6 subjects levels VP, with 2 replicates wg each, and is balanced. Here is the summary of this set with aov: >> summary(aov(ampl~gapf*bl+Error(VP/(bl*gapf)),hframe2)) > >Error: VP > Df Sum Sq Mean Sq F value Pr(>F) >Residuals

Hello, I've just carried out my first good-looking model using glmmPQL, and the output makes perfect sense in terms of how it fits with our hypothesis and the graphical representation of the data. However, please could you clarify whether my degrees of freedom are appropriate? I had 106 subjects, each of them was observed about 9 times, creating 882 data points. The subjects were in 3

To whom it may concern I made some analysis with R using the command Anova. However, I found some problmes with the output obtained by selecting type II o type III sum of squares. Briefly, I have to do a 2x3 mixed model anova, wherein the first factor is a between factor and the second factor is a within factor. I use the command Anova in the list below, because I want to obtain also the sum

Hi, I'm currently working on porting some SAS scripts to R, and hence need to do the same calculation (and get the same results) as SAS in order to make the transition easier for users of the script. In the script, I'm dealing with a two-factorial repeated-measures anova. I'll try to give you a short overview of the setup: - two between-cell factors: facBetweenROI (numbering

Dear R-help, I'm facing a problem with defining a repeated measures anova with weighted data. Here's the code to reproduce the problem: # generate some data seed=11 rtrep <- data.frame(rt=rnorm(100),ti=rep(1:5,20),subj=gl (20,5,100),we=runif(100)) # model with within factor for subjects/repeated measurements, no problem aov(rt~ti + Error(subj/ti),data=rtrep) #model with weights

Dear R-devel'ers Below is an outline for a set of routines to improve support for multivariate linear models and "classical" repeated measurements analysis. Nothing has been coded yet, so everything is subject to change as loose ideas get confronted by the harsh realities of programming. Comments are welcome. They might even influence the implementation... -pd General