similar to: Compare two repeated measures mixed models

Hi Group, Create some example data. set.seed(1) wide_data <- data.frame( id=c(1:10), predictor1 = sample(c("a","b"),10,replace=TRUE), predictor2 = sample(c("a","b"),10,replace=TRUE), predictor3 = sample(c("a","b"),10,replace=TRUE), measurement1=rnorm(10), measurement2=rnorm(10)) head(wide_data) id

Dear all, I am using RDA to study plant communities in various land uses (variable LU with values M, U, etc.). For each land use, I sample 3 to 5 fields (M1, M2, U1, U2, etc). I make 5 measurements for both plant communities and environmental variables in each field. I repeat the process every 6 months to study the effect of time and season (D16 for dry season 2017, R16 for rainy season 2016,

Dear Members, I have two estimated transition matrices and I want to compare them. In fact I want to check the hypothesis if they come from the same process. I tried to look for some test but all I found was independence test of contingency tables. The following code shows that the usual chi-squared test statistic does not follow chisq distribution. MCRepl <- 5000 khi12 <- rep(0,MCRepl)

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

Goal: use GEE or GLMM to analyze repeated measures data in R GEE problem: can?t find a way to do GEE with negative binomial family in R GLMM problem: not sure if I?m specifying random effect correctly Study question: Does the interaction of director and recipient group affect rates of a behavior? Data: Animals (n = 38) in one of 3 groups (life stages): B or C. Some individuals (~5)

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 have a serious problem in applying Tukey post hoc after a repeated measure ANOVA. I followed the suggestions that I found in this help -list especially this one: //[R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA You want to use lme() in package nlme, then glht() in the multcomp package. This will give you multiplicity adjusted p-values and confidence

Dear R-Gurus I am a PhD student from South Africa working on chimpanzee behaviour. I am looking at patterns of shade utilization and am using generalized linear mixed models to examine the effects of various factors on whether chimpanzees choose to spend time in the sun or shade. I realise that the lme4 package and the outputs of the lmer functions have been discussed ad nauseum but I have been

Hi R users, I google the website and I found that there are three ways to perform repeated measure ANOVA: aov, lme and lmer. http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg58502.html But the questions are which one is good to use and how to do post-hoc test? I use the example that is provided in the above link and I try > tt<-aov(p.pa~group*time+Error(subject/time),data=P.PA)

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Dear all, I have some questions regarding the validity an implementation of statistical tests based on linear models and loess. I've searched the R-help arhives and found several informative threads that related to my questions, but there are still a few issues I'm not clear about. I'd be grateful for guidance. Background and data set: I wish to compare the growth and metabolism

Hi, I would like to run an ANOVA with repeated measures with both a between subjects variable and within subjects (repeated measures) variables. I know how to do this when I have equal numbers of subjects in both groups (i.e., balanced design). That has been very well documented by Baron, for example, see: http://cran.r-project.org/doc/contrib/Baron-rpsych.pdf On page 34 of this document,

Dear all, I'm struggling to find the best (set of?) function(s) to do repeated measures logistic regression on some data from a psychology experiment. An artificial version of the data I've got is as follows. Firstly, each participant filled in a questionnaire, the result of which is a score. > questionnaire ID Score 1 1 6 2 2 5 3 3 6 4 4 2 ...

dear List I have a dataset with blood measurements at 5 points in TIME (0,30,60,90,120) taken on 3 VISITS (same subjects). the interest is to compare these measurements between Visits, overall and at the different time points. I have problems setting up repeated measures ANOVA with 2 repeated measures (VISIT and TIME) (and then doing post hoc testing) or doing it with a linear mixed model ( both

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

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

nobody? please ----- Mario Garrido Escudero PhD student Dpto. de Biolog?a Animal, Ecolog?a, Parasitolog?a, Edafolog?a y Qca. Agr?cola Universidad de Salamanca -- View this message in context: http://r.789695.n4.nabble.com/interpreting-repeated-measures-mixed-model-tp4647652p4647689.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I don't know if anyone has any thoughts on this. I have been trying to move from SAS Proc Mixed to R nlme and have an unusual result. I have several subjects measured at four timepoints. I want to model the within-subject correlation using an autoregressive structure. I've attached the R and SAS code I'm using along with the results from SAS. With R lme I get an estimate of

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

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