similar to: repeated measures regression

Hi, I would like to compare two models in R with the same dependant variable but different predictors (two different types of frequency and reaction times as RT). An editor told me to have a look at Lorch and Myers 1990. Lorch and Myers use the following technique: 1) they perform regressions on individual subjects' data 2) they extract the beta weights 3) they run t-test on these beta

Hi everyone, I am fairly new to R, and I am aware that others have had this problem before, but I have failed to solve the problem from previous replies I found in the archives. As this is such a standard procedure in psychological science, there must be an elegant solution to this...I think. I would much appreciate a solution that even I could understand... ;-) Now, I want to calculate a

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

Hi, all. I have a simple design I'm comparing to output from SPSS. the design is 1 repeated measure (session) and 1 between measure (cond). my dependent measure is rl. here is the data I'm using (in a data.frame): mig <- data.frame(subj=factor(rep(subj,3)), cond=factor(rep(cond,3)), session=factor(c(rep(1,nsubj),rep(2,nsubj),rep(3,nsubj))),

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

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

I was recently asked to perform a GLM analysis (the person comes from the JMP world) on a repeated measures design. I have found some things using aov but I cannot find anything with glm. In fact, multiple regressions in general with repeated measures seems to be poorly covered in documentation. I remember SPSS has separate commands to handle them. I have within variables SOA and ec and a

Dear all, I am having quite a hard time in trying to figure out how to correctly spell out a model in R (a repeated-measures anova with a within-subject covariate, I guess). Even though I have read in the posting guide that statistical advice may or may not get an answer on this list, I decided to try it anyway, hoping not to incur in somebody's ire for misusing the tool. For the sake of

Hi all, I'm doing a Repeated-Measures ANOVA, but I don't know how to test its contrasts or where to find the p-values of its coefficients. I know how to find the coefficient estimates of a contrast, but not how to test these estimates. First I do something like: y.aov <- aov(y ~ fac1 * fac2 + Error(subj/(fac1 * fac2)), data=data) Then, with coef(y.aov) I get the coefficients

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 all, I have simple question regarding how to fit a model (i.e. linear) to the data. Say I have 10 subjects with different phenotypes (dependent var Y, identical for a particular subject) and one predictor variable measured 3 times for each subject (X). By other words: Y Subj X 1 1 1.2 1 1 1.3 1 1 0.7 3 2 2.1 3 2 2.5 3 2 4 5 3 3 5 3 4 5 3 4 ... 20 10 12 20 10 13 20 10 12.5 Subj is a

Dear Colleagues, I have what Roger Kirk (Experimental Design: Procedures for the Behavioral Sciences, 1968) refers to as a randomized block factorial design. The anova table would look like this: df A 3 Subj/A 103 (error term for A) B 23 A*B 69 B*Subj/A 2369 (error term for B and A*B) Subjects are nested

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

Dear R community, I am trying to resolve a discrepancy between the way SPSS and R handle 2-way, repeated measures ANOVA. An experiment was performed in which samples were drawn before and after treatment of four groups of subjects (control and disease states 1, 2 and 3). Each group contained five subjects. An experimental measurement was performed on each sample to yield a

Hello list, I' d very much appreciate some help with a two sample repeated measures ANOVA. I did the analysis yielding sign. main effects (between subj.=site, within subj.=cover) and a sign. interaction: Univariate Type II Repeated-Measures ANOVA Assuming Sphericity SS num Df Error SS den Df F Pr(>F) site 18.7620 1 18.831 10 9.9631 0.010220 *

Hello I have a dataframe that looks like this: Date Min Subj VAR1 VAR2 VAR3 1 8/30/2011 5min 1 34.41042 126.08490 55.3548387 2 8/30/2011 10min 1 34.53030 133.81343 61.6000000 3 8/30/2011 15min 1 34.66297 118.38193 11.8000000 4 8/30/2011 20min 1 34.82770 110.77767 6.6000000 5 8/30/2011 5min 2 36.36994 116.24861 41.2258065 6 8/30/2011 10min 2

Can anyone explain how to properly use index.cond? I cannot include it in the xyplot command and get what I expect. For example, plot<-xyplot(y ~ x | z, data) # let's say z is a factor with six levels gives a nice plot but I am unhappy with the order of panels. I can get the order I want by plot$index.plot[[1]]<-c(1,6,4,3,2,5) I cannot seem to get it with either

I predicted that y would increase as x increased. However, I only made the prediction on the ranks of the scores. The ranks don't correlate with predicted. And, I don't think a regression on the ranks is warranted. However, the actual scores do yield a significant slope for b, and a significant R^2 using a linear regression (y is the value and x is the predicted rank). What

Hello all, I am trying to use xyplot to give a six panel plot and to highlight only points (in any panel) that meet a certain criterion. With the plot command I would do something like: plot.default(filein$Site,filein$circ.conc) points(filein$Site,filein$circ.conc,type="p", pch=ifelse(filein$p.value<5e-02,19,21)) I had thought I could just stick in the pch line from above into

Dear R-Users, I'm trying to set up a repeated measures anova with two within subjects factors. I tried it by 3 different anova functions: aov, Anova (from car package) and lme (from nlme package). I managed to get the same results with aov and Anova, but the results that I get from lme are slightly different and I don't figure out why. I guess I did not set up the error structure