similar to: lmer and estimable

Hi, Infinite recursion in S3 methods seem to crash R on Windows 2000 (R terminating with the ("Rgui.exe has generated errors...") message, rather than throwing an error. This happens with both Rgui and Rterm. The following toy example triggers this: myf <- function(x, ...) UseMethod("myf") myf.default <- function(x, ...) myf(x) myf(1) ...R crashes... Which I

I have produced a series of graphs with the png command, however when I have finally printed these out the black text appears to have a colour shadow with blue or red on either side of the letter. I tried increasing my res to 1200 and this improved it somewhat, but the text is still not sharp and occassionally these "shadows" will still print. I initially thought this was an issue with

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

Hi, I am having trouble using the lme function to perform a nested ANOVA with a random nested factor. My design is as follows: Location (n=6) (Random) Site nested within each Location (n=12) (2 Sites nested within each Location) (Random) Dependent variable: sp (species abundance) By using the aov function I can generate a nested ANOVA, however this assumes that my nested

Dear R-helpers, When I try > anova(txtE2.lme, txtE2.lme1) Model df AIC BIC logLik Test L.Ratio p-value txtE2.lme 1 10 8590 8638 -4285 txtE2.lme1 2 7 8591 8624 -4288 1 vs 2 6.79 0.0789 > latex(anova(txtE2.lme, txtE2.lme1)) Error: object "n.group" not found I don't even see n.group as one of the arguments of latex() I checked to see >

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Dear Thomas Lumley, and R-help list members, I have read your article "Network meta-analysis for indirect treatment comparisons" (Statist Med, 2002) with great interest. I found it very helpful that you included the R code to replicate your analysis; however, I have had a problem replicating your example and wondered if you are able to give me a hint. When I use the code from the

R2.2 for WinXP, Splus 6.2.1 for HP 9000 Series, HP-UX 11.0. I am trying to get a handle on why the same lme( ) code gives such different answers. My output makes me wonder if the fact that the UNIX box is 64 bits is the reason. The estimated random effects are identical, but the fixed effects are very different. Here is my R code and output, with some columns and rows deleted for space

Hi, I have searched the archives and can't quite confirm the answer to this. I appreciate your time... I have 4 treatments (fixed) and I would like to know if there is a significant difference in metal volume (metal) between the treatments. The experiment has 5 blocks (random) in each treatment and no block is repeated across treatments. Within each plot there are varying numbers of

Dear R-users, Sorry for reposting. I put it in another way : I want to test random effects in this random effect model : Rendement ~ Pollinisateur (random) + Lignee (random) + Pollinisateur:Lignee (random) Of course : summary(aov(Rendement ~ Pollinisateur * Lignee, data = mca2)) gives wrong tests for random effects. But : summary(aov1 <- aov(Rendement ~ Error(Pollinisateur * Lignee), data =

Dear all, Under version 2.0.0, I get the error below when calling summary() on a lme-object, whereas it works under version 1.9.1 (well, it did last week, before I upgraded). Any help on this? Thx in advance S??ren > library(nlme) > mf <- formula(Weight~Cu*(Time+I(Time^2)+I(Time^3))) > lme1 <- lme(mf, data = dietox, random=~1|Pig) > summary(lme1) Linear mixed-effects model fit

Hi, I am trying to get bootstrap confidence intervals on variance components and related statistics. To calculate the variance components I use the package lme4. > off.fun <- function(data, i){ d <- data[i,] lme1<- lmer(y ~ trt + (trt-1|group), d) VarCorr(lme1)@reSumry$group[2,1] #just as an example } > off.boot <- boot(data=data.sim, statistic=off.fun, R=100) If

Hi! I am quiet new with R and I have some problems to perform a posthoc test with an lme model. My model is the following: >lme1<-lme(eexp~meal+time, random=~1|id,na.action=na.omit) and then i try to get a post hoc test: >summary(glht(lme1,linfct=mcp(meal="Tukey))) but I get a warning message: Erreur dans as.vector(x, mode) : argument 'mode' incorrect Thank you for your

I assigned a class the first problem in Pinheiro & Bates, which uses the data set PBIB from the SASmixed package. I have recently downloaded 2.0.1 and its associated packages. On trying library(SASmixed) data(PBIB) library(nlme) plot(PBIB) I get a warning message Warning message: replacing previous import: coef in: namespaceImportFrom(self, asNamespace(ns)) after library(nlme) and a

Dear friends of lme, After so many year with lme, I feel ashamed that I cannot get this to work. Maybe it's a syntax problem, but possibly a lack of understanding. We have growth curves of new dental bone that can well be modeled by a linear growth curve, for two different treatments and several subjects as random parameter. By definition, newbone is zero at t=0, so I tried to force the

Hello together, I am an absolute noob in R and therefore I need help urgently. I have received a script from my tutor with plot functions in it. However, I can' manage to adapt these plots. The hole script is as follows: setwd("E:/") ##### (1) Read data ### dat <- read.table("Komfort_Tatsaechliche_ID_Versuchsreihe_1.txt", header=TRUE, sep="\t",

I'm starting to understand fixed and random effects, but I'm puzzled a bit. Here is an example from Hays's textbook (which is great at explaining fixed vs. random effects, at least to dummies like me), from the section on mixed models. You need library(nlme) in order to run it. ------ task <- gl(3,2,36) # Three tasks, a fixed effect. subj <- gl(6,6,36) # Six subjects, a random

Hi Everyone, I posted a similar question about a week ago, but haven't gotten any replies -- I'm afraid that's because my previous question was too vague. Let me try again with a more specific question, and I hope someone can help. NOTE, I know I should be using the newer lme4 package, I just haven't had a chance to update my version of R yet, so the question below relates