similar to: repeated measures with random effects

Hi all, I'm trying to test a repeated measures model with random effects using the nlme library. Suppose I have two within subjects factors A, B both with two levels. Using aov I can do: aov.1 <- aov(y ~ A*B + Error(S/(A+B)) following Pinheiro and Bates I can acheive the analagous mixed-effects model with: lme.1 <- lme(y~A*B, random=pdBlocked(list(pdIdent(~1),pdIdent(~A-1),

Hallo! I have data of the following design: NSubj were measured at Baseline (visit 1) and at 3 following time points (visit 2, visit 3, visit 4). There is or is not a treatment. Most interesting is the question if there is a difference in treatment between the results of visit 4 and baseline. (The other time points are also of interest.) The level of significance is alpha=0.0179 (because of an

Hello, I am trying to fit a repeated measures linear mixed model (using lme) but I don't want to include any random effects. I'm having trouble (even after consulting Pinheiro & Bates 2000) figuring out how to specify the repeated measure without including it in the specification of a random effect. My data consist of repeated "counts" in "plots" that I wish

I have three columns of data, Xc, Trt and fish. This was a repeated measures design with 6 measurements taken from each of 5 fish. Xc is the actual measurement, Trt is the treatment, and fish is the fish number. Data can be seen below (hopefully it is in the column format). I would like to look for differences between treatments in a repeated measures format. I used the following code

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 I have been asked by a colleague to perform a statistical analysis which uses random effects - but I am struggling to get this to work with nlme in R. Help would be very much appreciated! Essentially, the data consists of: 10 patients. Each patient has been given three different treatments (on three separate days). 15 measurements (continuous variable) have been taken from each patient

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

Dear, I want to analyze an outcome in an RCT using lme but I am not sure that I have chosen the right way for the model. We measured the outcome three times repeatedly in the same patient. One time before intervention and two times after intervention. I wanted to adjust for the correlated data in the repeated measurement and baseline differences in the variable in order to get the treatment

Hi, I am fairly new to R and still trying to figure out how it all works, and I have run into a few issues. I apologize in advance if my questions are a bit basic, I'm also no statistics wizard, so part of my problem my be a more fundamental lack of knowledge in the field. I have a dataset that looks something like this: Week Subj Group Readout 0 1 A 352.2 1 1 A

Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

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

Dear all, I'm interested in analysing a reapeated measure desing where plant height (H) was measured 3 times (Time). The experimental design include 2 fixed factor (say A and B) in which A is nested in B, and a random factor (C, the plot), using the aov(). So my first idea would be something like: aov(H ~ B * A %in% B * Time + Error(id) ) where id is the factor coded for the repeated

Hello I am trying to fit a REML to some soil mineral data which has been collected over the time period 1999 - 2004. I want to know if the 19 different treatments imposed, differ in terms of their soil mineral content. A tree model of the data has shown differences between the treatments can be attributed to the Magnesium, Potassium and organic matter content of the soil, with Magnesium being the

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

Hi. I would like to run a mixed effects model, but there's one aspect of the model that I don't know how to code. My goal is to analyze data from an experiment in which I tested the swimming performance (i.e., Umax) of two species of fish from 4 different lakes (a sample of the data is below). Each fish was tested when it was rested and when it was tired, and each test was

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 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 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

Hi, I have a experiment with block, plots, sub-plots, and sub-sub-plots with repeated measures and 3 factors (factorial design) when we have been observed diameter (mm), high (cm) and leaves number (count). However, we don't have one treatment in one factor, so, my design is unbalanced. On a previous message here, a friend tell me that "It appears to me that your design is a split-split

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