similar to: Syntax for lme function to model random factors and interactions

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----

Hello everybody, i'm trying to run a lme but am having a problem. My model have many response variables and when i run the anova, the number of interactions (up to six) is great and the p-values of all variables not appear. I wanted to know if i could to control the number of interactions of the model, or if this method is the most advisable for so many variables. Thanks in advance!

Hi All, I'm taking an Experimental Design course this semester, and have spent many long hours trying to coax the professor's SAS examples into something that will work in R (I'd prefer that the things I learn not be tied to a license). It's been a long semester in that regard. One thing that has really frustrated me is that lme has an extremely counterintuitive way for

Hello, I am trying to make the switch from SAS, and I have a fairly elemental problem with syntax using the nlme package for analyzing mixed models. There was a previous question on this topic posted to this list, so I apologize for redundancy, but I didn't understand the advice given to that inquiry. The model I want to run has the following factors: Host (fixed) Sire (random) Dam

In lme, what's the difference between "random = ~ 1 | x / y" and "random = ~ y | x" ? Thanks Julien [[alternative HTML version deleted]]

I'm fitting a lme growth curve model with two predictors and their interaction as predictors. The multilevel model is nested so that level 1 is time within the individual, and level 2 is the individual. I would like to plot the mean group-level trajectories at plus and minus 1 SD from the mean of the main effects composing the interaction term. Thus, the plot should have 4 lines (mean

Hi All, I've been searching the help archives but haven't found a workable solution to this problem. I'm running an lme model with the following call: >lme.fnl <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID) > anova(lme.fnl) numDF denDF F-value p-value (Intercept) 1 168 19255.389 <.0001 S 1 168 5.912 0.0161 Tr

Hi! I'm a bit lost while performing multiple comparisons on a lme model of that type: lmeglu=lme(glucose~Ath*tim,random=~1|Vol,na.action=na.omit,data=data) multc = glht(lmeglu, linfct = mcp(Ath = "Tukey", tim = "Tukey")) This works fine for identifying the effect of each factor. However, when I look for their interactions, l only obtain error messages. For example this

I don't think, this has been answered: > I'm trying to run a 3-way within-subject anova in lme with 3 > fixed factors (Trust, Sex, and Freq), but get stuck with handling > the random effects. As I want to include all the possible random > effects in the model, it would be something more or less > equivalent to using aov > > > fit.aov <- aov(Beta ~ >

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Dear all, I've read numerous posts about the random and nested factors in lme, comparison to proc Mixed in SAS, and so on, but I'm still a bit confused by the notations. More specifically, say we have a model with a fixed effect F, a random effect R and another one N which is nested in R. Say the model is described by Y~F Can anyone clarify the difference between : random = ~1|R:N random

Hello. I am new to R and new to linear mixed effects modeling. I am trying to model some data which has two factors. Each factor has three levels rather than continuous data. Specifically, we measured speech at Test 1, Test 2 and Test 3. We also had three groups of subjects: RepTP, RepNTP and NoRepNTP. I am having a really hard time interpreting this data since all the examples I have seen

On page 165 of Mixed-Effects Models in S and S-Plus by Pinheiro and Bates there is an example of using lme() in the nlme package to fit a model with crossed random factors. The example assumes though that the data is grouped. Is it possible to use lme() to fit crossed random factors when the data is not grouped? E.g., y <- rnorm(12); a=gl(4,1,12); b=gl(3,4,12). Can I fit an additive model

Dear R-users I'm considering a repeated measures experiment where two within-subject factors A (2 levels) and B (3 levels) have been measured for each of 14 subjects, S. I wish to test the effect of factor A. I know that a variance component model with random effects S, S:A, S:B and S:A:B can be fitted using aov: aov( y ~ A*B + Error(S/(A*B)) ) If there is no significant interaction, the

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

Dear all, I am quite new at R and have a question about using lme with crossed random factors. I followed the instructions of Pinheiro & Bates, but that did not work because of the non grouping of my data. Reading prior threads ( http://www.mail-archive.com/r-help@stat.math.ethz.ch/msg10849.html), I found a solution to deal with non grouped data and crossed random factors in lme, by defining

I have some questions about the use of lme(). Below, I constructed a minimal dataset to explain what difficulties I experience: # two participants subj <- factor(c(1, 1, 1, 1, 2, 2, 2, 2)) # within-subjects factor Word Type wtype <- factor(c("nw", "w", "nw", "w", "nw", "w", "nw", "w")) # within-subjects factor

Hello, In my study, mixed effects model is required and the number of fixed effects is very large. When lme package is employed, a model error is displayed once the number of fixed factors in the formula reaches 200. Is this the maximum number of fixed factors can be handled by lme package? If not, what is possible reason of the error message? Thanks Yongxiang

Dear all, I am studying the possibility of using the nlme package in R to analyse field trials of agricultural crops. I have a problem with the syntax for the modelling of variance covariance structures. I can model the within-group covariance structure using the correlation argument and the covariance structure between different random effects of the same grouping level using

Dear R-Community! For example I have a study with 4 treatment groups (10 subjects per group) and 4 visits. Additionally, the gender is taken into account. I think - and hope this is a goog idea (!) - this data can be analysed using lme as below. In a balanced design everything is fine, but in an unbalanced design there are differences depending on fitting y~visit*treat*gender or