similar to: the numimum number of fixed factors lme package can deal with

Dear All, I run R on a windows 7 machine and it has been worked very well. I installed Graphvis 2.20.3 and Rgraphviz. recently, however, I cannot load the Rgraphviz package and error message popped up The message shown on the pop up window with the title: R Consol: Rgui.exe - Sysytem error The program can't start because MSVCR80.dll is missing from your computer. Try reinstalling the

Dear all this is a question of model specification in lme which I'd for which I'd greatly appreciate some guidance. Suppose I have data in long format gene treatment rep Y 1 1 1 4.32 1 1 2 4.67 1 1 3 5.09 . . . . . . . . . . . . 1 4 1 3.67 1 4 2 4.64 1 4 3 4.87 .

Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me. Ruposh --- r-help-request at stat.math.ethz.ch wrote:

Hi I am trying to get to grips with lme and perhaps the euro-cent is not dropping. I want to define a model with some terms random and some fixed so that: gd<-groupedData(y~x|c,df) defining by the group c in data frame df, whereupon mod<-lme(y~t+f,gd,~t) where I want the t as a random effect and the f as the fixed part. Without the f it works OK but when I add the f, I get an error

Hi there, I have a question for using "lme". Say, I have 6 data points and they belong to six groups (one group factor). So there is no replicates for each group and I cannot separate the with-in group variation from the between group variation. But when I try to use "lme" to deal with it, it gave the answers for both with-in group variation and the between group

Dear R-users, I have longitudinal data and would like to fit a model where both the variance-covariance matrix of the random effects and the residual variance are conditional on a (binary) grouping variable. I guess the model would have the following form (in hierarchical notation) Yi|bi,k ~ N(XiB+Zibi, sigmak*Ident) bi|k ~ N(0, Dk) K~Bernoulli(p) I can obtain different sigmas (sigma0 and

Dear R users, I want to conduct a small simulation study and I have to use the lme function in a loop to save the restricted log likelihood. However, for one simulated data set the lme function gives this error Error en lme.formula(yboot ~ X[, -1], data = data.fr, random = Z.block) : nlminb problem, convergence error code = 1 message = singular convergence (7) and then, the

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 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

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

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 ~ >

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

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

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

Hello, I have a question regarding the syntax of the lme function in the nlme package. What I'm trying to do is to calculate an estimate of R^2 based on the likelihood ratio test. For this calculation, I need to determine the maximum log-likelihood of the intercept-only model and the model of interest (with the desired factors and interactions). My model has four independent factors (i.e. A,

Hello, I'm using aov() to analyse changes in brain volume between males and females. For every subject (there are 331 in total) I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). The data looks like this: Subject Sex Lobe Tissue Volume subect1 1 F g 262374 subect1 1 F w 173758 subect1 1 O g 67155 subect1 1 O w 30067 subect1 1 P g 117981

Hi, all, I have a question about random effects model. I am dealing with a three-factor experiment dataset. The response variable y is modeled against three factors: Samples, Operators, and Runs. The experimental design is as follow: 4 samples were randomly chosen from a large pool of test samples. Each of the 4 samples was analyzed by 4 operators, randomly selected from a group of

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

Hi, I have a data frame with column names "1", "2", "3", ... and I'd like to extract a subset based on the values in the first column. None of the methods I tried worked (below). x <- subset(dframe, 1 = = "My Text") x <- subset(dframe, "1" = = "My Text") x <- subset(dframe, names(dframe)[1] = = "My Text") Q

Hello all, I've run into a problem where I can't run predict.lme on an object simplified via a stepAIC. A similar post has been recorded on this list: https://stat.ethz.ch/pipermail/r-help/2008-May/162047.html but in my case, I'm going to great lengths to not repeat that poster's error and still coming up short. Any advice would be much appreciated. It would seem that, after