similar to: 2 factor ANOVA and sphericity

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 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 estimate two competing simple regression models, A and B where the LHS is the same in both cases but the predictor is different ( I handle the intercept issue based on other postings I have seen ). I estimate the two models on a weekly basis over 24 weeks. So, I end up with 24 RSquaredAs and 24 RsquaredBs, so essentally 2 time series of Rsquareds. This doesn't have to be necessarily thought

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

Dear Experts, I am new to R and unfortunately cannot start with a simply statistical analysis: I manually determined the volume of the right and left hippocampus in a group of meditators and in a group of controls. My data-sheet looks as follows: observation subject group age gender hemisphere volume 1 am04 m 25 f left 3.637 2 am04 m 25 f right 3.713 3 ao08 m 47 m left 3.715 4 ao08 m 47

Dear Prof. Baron, thankyou for your pdf paper on using R, at http://www.psych.upenn.edu/~baron/rpsych.pdf I am currently interested in the sphericity notes on pp. 45-47. The equation on p. 46 suggests the following R calculation: D <- k^2 * ( mean(diag(S)) - mean(S) )^2 while the notes in the pdf indicate the following: D <- k^2 * ( mean(S) - mean(diag(S)) )^2 Can you please consider

Hi, we need some help to define planned comparisons. I've based my understanding of the problem on reading Tabachnick and Fidell (2006), ie: http://www.ablongman.com/catalog/academic/product/0,1144,0205459382,00.html I don't understand how to specify planned comparisons in R. I've not found explanations for this in MASS or elsewhere. There is only discussion of the contrast

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

Hi R Gurus! I conducted regression and anova followings : Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.07e-01 5.95e-02 10.19 < 2e-16 *** nemp 2.87e-06 1.04e-07 27.63 < 2e-16 *** as.factor(corridor1)A -8.81e-02 2.13e-02 -4.14 3.6e-05 *** as.factor(corridor1)B

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

How do we specify a contrast interaction matrix for an ANOVA model? We have a two-factor, repeated measures design, with Cue Direction (2) x Brain Hemisphere(2) Each of these has 2 levels, 'left' and 'right', so it's a simple 2x2 design matrix. We have 8 subjects in each cell (a balanced design) and we want to specify the interaction contrast so that: CueLeft>CueRght

Hi all, I have a strange problem and rigth now I can't figure out a solution. Trying to calculate an ANOVA with one between subject factor (group) and one within (hemisphere). My dependent variable is source localization (data). My N = 25. My data.frame looks like this: > ML.dist.stack subj group hemisphere data 1 1 tin left 0.7460840 2 2 tin left

Why do I see this error? > library(stats) > require(stats) [1] TRUE > > tHSD <- TukeyHSD(aov) Error in TukeyHSD(aov) : no applicable method for "TukeyHSD" In case it helps: > aov Call: aov(formula = roi ~ (Cue * Hemisphere) + Error(Subject/(Cue * Hemisphere)), data = roiDataframe) Grand Mean: 8.195069 Stratum 1: Subject Terms: Residuals Sum

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

An apparatus exists whereby a collection of balls is displaced to the top of a stack by suction. A top level (Level 1) each ball is shifted 1 unit to the left or 1 unit to the right at random with equal probability. The ball then drops down to level 2. At Level 2, each ball is again shifted 1 unit to the left or 1 unit to the right at random. The process continues for 15 levels and the balls are

I am using read.csv to read a CSV file (produced by saving an Excel file as a CSV file). The columns containing dates are being read as factors. Because of this, I can not compute follow-up time, i.e. Followup<-postDate-preDate. I would appreciate any suggestion that would help me read the dates as dates and thus allow me to calculate follow-up time. Thanks John John Sorkin M.D., Ph.D. Chief,

Dear List, I have a richness data distributing across 20 N to 20 S latitude. (120 E-140 E longitude). I would like to draw the richness in the north hemisphere and a regression line in the plot (x-axis: latitude, y-axis: richness in the north hemisphere). The above demand is done using plot. Then, south hemisphere richness and regression are required to be generated using the same y-axis above

I am having trouble selecting rows of a dataframe that will be included in a regression. I am trying to select those rows for which the variable Meno equals PRE. I have used the code below: difffitPre<-lm(data[,"diff"]~data[,"Age"]+data[,"Race"],data=data[data[,"Meno"]=="PRE",]) summary(difffitPre) The output from the summary indicates that

Widows XP R 2.3.1 I have been trying to make a data structure that will contain both the coefficients from a linear regression along with column and row titles AND the call, i.e. myreg<-lm(y~x+y+z) whatIwant<-cbind(c(summary(myreg)$call,"",""),summary(myreg)$coefficients) Neither the statement above, nor any one of twenty variations I have tried work. I would appreciate

I want to place four plots on a page, and I would like to have all four plots share a common title. I have tried the following code, but the title is centered over the fourth graph and not centered across all four plots. Does anyone have any suggestions? R 2.1.1 windows xp oldpar<-par(mfcol =c(1,4),ask=TRUE) plot(p,varp) plot(p,SEp) plot(p,CVp) plot(p,ppval) title(paste("P and 95%CI