similar to: power for repeated-measures ANOVA lacking sphericity

In the hypothetical example below, how do I add two segments() into the two panels, respectively? Say segments(x0=5, y0=10, x1=5, y1=20) on the left and segments(x0=15, y0=-10, x1=15, y1=-2) on the right? Many thanks in advance, Yuelin Li. ps. part of the code came from a solution given by Deepayan Sarkar. ------------------- library(lattice) set.seed(12345) x <- 0:20 y.male.obs <- - 1.2

I haven't been able to find an answer on this that's direct, only implied. In several places I have read that when people asked for sphericity tests they were guided toward lme or mlm models. But, there is no direct indication that the lme method is not subject to the sphericity assumption. In fact, it seems like it should be. Its just a linear model that handles random and

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

Hello and thanks in advance I am running a glm in R the code is as follows with residual diagnostic code below model4<-glm(Biomass~(Treatment+Time+Site)^2, data=bobB, family=quasi(link="log", variance="mu")) par(mfrow=c(2,2)) plot(model2) to test the effect of grazing exclusion of feral horses for a Phd with following factors: Treatment - 3 levels which are grazed

Hi, Is there a way to do a sphericity test in R for repeated measures ANOVA (using aov or lme)? I can't find anything about it in the help. Thanks Orou [[alternative HTML version deleted]]

Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems that I am not getting something

[corrected dataset below] Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems

Does anyone have any suggestions for how to test for violation of the sphericity assumption and apply the Huynh-Feldt correction to a repeated-measure model? My model follows the format below: Rep.anova=aov(Y~X1*X2+Error(Subject/(X1*X2),data=DataFile)) I've already looked at the explanations aov() and Error() in Baron's “Notes on the use of R for psychology experiments and

I have been trying to analyse an unreplicated repeated measures 2-level factorial design with 11 factors using aov() with Error(), similar to that described in "Notes on the use of R for psychology experiments and questionnaires" by Jonathan Baron and Yuelin Li (see the example of Hay's) I have found that there seems to be a limit, in the number of factors in Error() . For

I forgot to mention that this analysis was carried out using the Windows version . Perhaps this problem is windows specific. Peter Peter Ho wrote: > Prof. Ripley, > > I am sending this to you again, as the first one was undelivered. > > > Peter > > ripley at stats.ox.ac.uk wrote: > > >>> There is no intended limit, but there was a bug fix in 1.5.0. If

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

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

I am having trouble compiling R-1.1.0 on a Sun Ultra 60 machine running Solaris 7. Suggestions are greatly appreciated. I modified the CPPFLAGS and LIBS variables in the config.site file. As far as I can tell, I have included all the non-standard headers and libraries installed on my Sun station. My GNU stuff is installed in /opt/Summertime_98.sparc/, and at the bottom of this message I

Dear R people: I wish to switch from SPSS to R, but there is one particular type of ANOVA design that cannot be done in R. Or more likely, it can be done, but it is nowhere documented. The problem is typical for psychologists: You have a repeated measures design with different groups of subjects. Now, this can be done with the aov command, but the number of subjects in both groups must be

Has anyone written a function that returns all possible subsets of r elements out of a vector that contains n elements? I have not been able to find an answer in the FAQ or the archives. Say I have a vector: letters[1:10]. I want to get a matrix of 120 rows that contains all possible subsets of 3 letters out of the 10. And I would like to be able to generalize to any r out of n, like

Dear List, I am trying to analyze a dataset where I have 1 continuous between-item variable (C), and 2 factorial within-item variables (3- and 2-level: F3, F2). I'm interested in whether slope of C is different from 0 at different combinations of F3 and F2, and whether it varies between these combinations. And unfortunately I need a decent anova-like table with p-values. The reason is that

This question is about compiling R-1.6.2 from source on a Sparcstation-20 machine running Solaris 7, gcc-2.95.3 and readline-4.2 (both gcc and readline from sunfreeware.com). I searched the r-help archive and the FAQ. The errors seem to be related to the binary readline libraries, and I have /usr/local/lib/libreadline.a,libreadline.so@, and libreadline.so.4. The attached errors are from

I believe that the following function is a good function for eyeballing sphericity. But, I can't find functions for tests anywhere (g-g or h-f). Anyone who wants to can feel free to use it. It is bad that R does not provide any tests for sphericity AND cannot do a MANOVA on repeated measures (although lme is an alternative add on package). One of these solutions must be provided in the

I remember reading about post hoc statistical power on R-help. But I can't seem to find them with RSiteSearch("post hoc statistical power") and variations of it. I would like to learn more about post hoc statistical power, its meaningfulness, advantages and disadvantages. I thought the issue was settled after Tukey's 1993 paper about post hoc statistical power being

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