similar to: predict.nlme

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

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

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 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

Dear all, I have the following dataset with results from an experiment with individual bats that performed two tasks related to prey capture under different conditions: X variables: indiv - 5 individual bats used in the experiment; all of which performed both tasks task - 2 tasks that each individual bat had to perform dist - 5 repeated measures of individual bats at 5 different distances from

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

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,

Dear R-help members, I'm trying to run LME model on some behavioral data and need confirmations about what I'm doing... Here's the story... I have some behavioral reaction time (RT) data (participants have to detect dome kind of auditory stimuli). the dependant variable is RT measured in milliseconds. 61 participants were tested separated in 4 age groups (unblanced groups,

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

Hi everyone, I'm using lmer and estimable (from packages lme4 and gmodels respectively) and have the disconcerting happening that when I run exactly the same code, I get different results! In checking this out by running the code 50x, it seems to be that answers may be randomly deviating around those which I get from another stats package (GenStat, using the linear mixed models functionality

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 r-helpers, why do I get an output in the first iteration of the for-loop which contains the string values of the input vector, and how can I avoid that? Here's the output (only line 1 is wrong) latentVariable Indiv Group 1 rPlanning rIterat rTDD 2 rPlanning 0.79 0.84 3 rIterat 0.79 0.83 4 rTDD 0.9 0.96 5 rStandup 0.83 0.82 6

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

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

Dear R Friends, I need help with an error associated with corCompSymm in an lme fit. I am using a mixed effects model to analyze a split-plot with repeated measures and would like to fit with the compound symmetry correlation structure. This problem doesn't occur when using corAR1 or any of the other structures. I would greatly appreciate help on how to solve this issue. Here's my

Hello world, I'm trying to do an anova on data in data.set, dependent variable is a column named "dep.var", grouping variable is in a column called "indep.var", and is.factor(indep.var) is TRUE... why can't I just do aov(dep.var ~ indep.var, data = data.set)? What have I done to deserve this?! What gives? Am I missing something totlly obvious? R-base-1.0.0-1,

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

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

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