similar to: lme error terms

How does one go about doing a repeated measure regression? The documentation I have on it (Lorch & Myers 1990) says to use linear / (subj x linear) to get your F. However, if I put subject into glm or lm I can't get back a straight error term because it assumes (rightly) that subject is a nominal predictor of some sort. In looking at LME it seems like it just does the right thing

Hi, what is the appropriate syntax to get the random error correct when performing repeated measures anova with 'lme'. let's say i have 3 independent variables, with 'aov', i would write something like: aov(dep_var~(indep_var1*indep_var2*indep_var3) + Error(subject/(indep_var1*indep_var2*indep_var3)). With 'lme' however, i can't find the right formula. i tried

Hi I try to implement a simple 2-factorial repeated-measure anova in the lme framework and would be grateful for a short feedback -my dependent var is a reaction-time (rt), -as dependent var I have -the age-group (0/1) the subject belongs to (so this is a between-subject factor), and -two WITHIN experimental conditions, one (angle) having 5, the other 3 (hands) factor-levels;

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

Dear all, I'm not very sure on the use of repeated measures in R, so some advice would be very appreciate. Here is a simple example similar to my real problem (R 2.6.0 for windows): Lets supose I have annual tree production measured in 9 trees during 3 years; the 9 trees are located in 3 different mountains (sites), and each tree receive different annual rainfall (different locations). I would

Hi, I have what I believe is a repeated-measures dataset that I'm trying to analyze using lme(). This is *not* homework, but an exercise in my trying to self-teach myself repeated-measure ANOVA for other *real* datasets that I have and that are extremely similar to the following design. I'm fairly sure the dataset described below would work with lme() -- but it'd be great if anybody

I am trying to understand better an analysis mean RT in various conditions in a within subjects design with the overall mean RT / subject as one of the factors. LME seems to be the right way to do this. using something like m<- lme(rt~ a *b *subjectRT, random= ~1|subject) and then anova(m,type = "marginal"). My understanding is that lme is an easy interface for dummy coding

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

I'm trying to learn how to do a repeated measures ANOVA in R using lme(). A data set that comes from the book Design and Analysis has the following structure: Measurements (DV) were taken on 8 subjects (SUB) with two experimental levels (GROUP) at four times (TRIAL). In SAS, I use the code: PROC MIXED DATA=[data set below]; CLASS sub group trial; MODEL dv = group trial group*trial;

Hi everyone, I'm having a little trouble with working out what formula is better to use for a repeated measures two way anova. I have two factors, L (five levels) and T (two levels). L and T are both crossed factors (all participants do all combinations). So, I do: summary(aov(dat~L*T+Error(participant/(L*T)),data=dat4)) But get different results with:

I am analyzing data from an experiment with two factors: Carbon (+/-) and O3 (+/-), with 4 replicates of each treatment, and 4 harvests over a year. The treatments are assigned in a block design to individual Rings. I have approaches this as a repeated measures design. Fixed factors are Carbon, O3 and Harvest, with Ring assigned as a random variable. I have performed repeated measures analysis

In Baron and Li's "Notes on the use of R for psychology experiments and questionnaires" http://cran.r-project.org/doc/contrib/rpsych.htm they describe a balanced data set for a drug experiment: "... a test of drug treatment effect by one between-subject factor: group (two groups of 8 subjects each) and two within-subject factors: drug (2 levels) and dose (3 levels). "

Hi Everyone, I’m trying to figure out how to get R to analyze this experiment properly. I have a series of subjects each with two legs. Within each leg there are two bones that I am interested in. There are also two treatments that I am interested in. That results in four different combinations of treatments. Obviously, since the subjects only have two legs, they can’t receive each

Hi As I can't find an example of my data structure I'd like some advice on which is the most appropriate test for significant effects. If I should be using either lme or anova, is the relevant example below the best/correct way to do the test? The Data... 2 groups of patients (5 in GroupA, 7 in GroupB) 3 short acting drugs, (I'm not concerned with residual effects from the previous

I am trying to do Tukey HSD comparisons on a repeated measures expt. I found the following example on r-help and quoted approvingly elsewhere. It is broken. Can anyone please tell me how to get it to work? I am using R 2.4.1. > require(MASS) ## for oats data set > require(nlme) ## for lme() > require(multcomp) ## for multiple comparison stuff > Aov.mod <- aov(Y ~ N + V +

Hi, Im using lme to calculate a mixed factors ANOVA according to: px_anova = anova(lme(dep~music*time*group, random = ~1|id, data = px_data)) where dep is a threshold, time is a repeated measures variable (2 levels) group is a between subjects variable (2 levels) id is a random factor (subject id) music is a between subjects variable (2 levels) indicating if a person has a musical experience,

Hi, I have a fairly simple repeated measures-type data set I've been attempting to analyze using the lme function in the nlme package. Repeated searches here and other places lead me to believe I have specified my model correctly. However, I am having trouble with post-hoc tests. From what I gather, other people are successfully using the glht function from the multcomp package to

http://www.nabble.com/file/p24211204/repeated.csv repeated.csv Dear all, I'm currently trying to replicate some Proc Mixed results using lme() and have a curious result I can't explain. The dataset is a repeated measures example where patients (each on one of several treatments) are measured over a number of days. Putting aside issues of the error covariance structure I'm using

hello list, the topic is covered extensively but from none of the postings i could conclude the correct statement for my design: a 2-level within and a 2-level between subjects factor, both fixed, subjects as random factor. i want to test wheter there is a within effect and if it is different for levels of the between-factor. i want to do it with lme and multicomp for post-hocs. i tried am2

Dear List, I downloaded R for the first time yesterday, in the hopes that I might deal more effectively with a complex repeated measures experimental design involving inbred strains of laboratory mice. The design below, somewhat simplified, cannot be computed with standard ANOVA, because something called the X'X matrix is too large. The design has the following factors: Between-subject