similar to: Nested and repeated effects together?

I have a large dataset where each Subject answered seven similar Items, which are binary yes/no questions. So I've always used Subject and Item random effects in my models, fit with lmer(), e.g.: model<-lmer(Response~Race+Gender+...+(1|Subject_ID)+(1| Item_ID),data,binomial) But I recently realized something. Most of the variables that I've tested as fixed effects are properties

Dear R-list members, I've been struggling with the proper setup for analysing my data. I've performed a route choice experiment, in which participants had to make a choice at each junction for the next road. During the experiment they received traffic information, but also encountered two different accidents. They also made trips without accidents. What I'm interested in is to

I'm running lmer repeatedly on artificial data with two fixed factors (called 'gender' and 'stress') and one random factor ('speaker'). Gender is a between-speaker variable, stress is a within-speaker variable, if that matters. Each dataset has 100 rows from each of 20 speakers, 2000 rows in all. About 5% of the time I get a strange result, where the lmer() model with

I'm working with a nested model (mixed). I have four factors: Patients, Tissue, sex, and tissue_stage. Totally I have 10 patients, for each patient, there are 2 tissues (Cancer vs. Normal). I think Tissue and sex are fixed. Patient is nested in sex,Tissue is nested in patient, and tissue_stage is nested in Tissue. I tried aov and lme as the following, > aov(gene ~ tissue + gender +

Dear list, I would like to run an ANCOVA using car::Anova with repeated measures factors, but I can't figure out how to do it. My (between-subjects) covariate always interacts with my within-subject factors. As far as I understand ANCOVA, covariates usually do not interact with the effects of interest but are simply additive (or am I wrong here?). More specifically, I can add a covariate as

Hello dear R members. I have been learning the Anova syntax in order to perform an SS type III Anova with repeated measures designs (thank you Prof. John Fox!) And another question came up: where/what are the (between/within) residuals for my model? ############ Play code: phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),

Hola! I am encountering the following problem, in a multilevel analysis, using glmmPQL from MASS. This occurs with bothj rw1062 and r-devel, respectively with nlme versions 3.1-38 and 3.1-39 (windows XP). > S817.mod1 <- glmmPQL( S817 ~ MIEMBROScat+S901+S902A+S923+URBRUR+REGION+ + S102+S103+S106A+S108+S110A+S109A+S202+S401+S557A+S557B+ + YHOGFcat,

Hi everyone, I'm having some difficulty getting "simple effects" for the ols() function in the rms package. The example below illustrates my difficulty -- I'll be grateful for any help. #make up some data exD <- structure(list(Gender = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("F", "M"), class = "factor"),

. HI there, I'd like to show demonstrate how the chi-squared distribution works, so I've come up with a sample data frame of two categorical variables y<-data.frame(gender=sample(c('Male', 'Female'), size=100000, replace=TRUE, c(0.5, 0.5)), tea=sample(c('Yes', 'No'), size=100000, replace=TRUE, c(0.5, 0.5))) And I'd like to create a list of 100

Hi, yet another anova.mlm problem - it doesn't seem to end. This time, I have a setup with a few within-subject factors and a between-subject factor (SGROUP). Consider the most simple case with only one within-factor (apo): > mlmfit0 <- lm(data.n ~ 0 + SGROUP) > mlmfit1 <- lm(data.n ~ 1 + SGROUP) > anova(mlmfit1,mlmfit0,test="Spherical",M=~hemi,X=~1) Analysis of

Hi everyone, I'm very new to R and I like to learn a lot... actually I have a little problem concerning errorbars with xyplot. My data look like run target hemi x 1 1 Nichts Links 0.0007743240 2 2 Nichts Links -0.0008153365 3 1 Target Links -0.0015825950 4 2 Target Links 0.0088743785 5 1 Nichts Rechts 0.0015898995 6 2 Nichts Rechts -0.0011465190 7 1

This is an expanded version of the question I tried to ask last night - I thought I had it this morning, but it's still not working and I just do not understand what is going wrong. What I am trying to do is write a wrapper for lattice xyplot() that passes a whole bunch of its secondary arguments, so that I can produce similarly formatted graphs for several different data sets. This is what

Dear all, > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 5.0 year 2002 month 04 day 29 language R OS: Win98 I have a strange problem with a particular data.frame built with

Ok, so here's the deal... I've been having a lot of trouble sleeping, so I've done some research and discovered this program called "Hemi-Sync". I decided to torrent it from the Pirate Bay to try it out before spending my money. What I torrented worked incredibly, so bought it, then torrented another Hemi-Sync program: Lucid Dreaming. It's a DVD-Audio program. What I

Hi all, I have collected response time data from 178 participants ('sub') for each combination of 4 within-Ss factors ('con','int','tone','cue'). Additionally, I have recorded the gender of each participant, so this forms a between-Ss factor ('gender'). Normally this would be analyzed using aov:

Hi, I'm a PhD student and I want to use the function 'daisy' from the package 'cluster' to compute dissimilarities. My variables are of mixed types so I use the argument 'stand' in daisy to define the type of my variables. I have the following error message : Warning message: binary variable(s) 13, 16, 17, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

Dear R-user, I have a data like this below, age <- c("young","mid","old") married <- c("no","yes") income <- c("low","high","medium") gender <- c("female","male") I want to make some of combination data like these, age.income.dat <- expand.grid(age,

dear all, I'm building new dataframes from bigger one's using e.g. columns F76, F83, F90: JJ<-data.frame( c( as.character(rep( gender,3))) , c( F76,6- F83, F90) ) Looking into JJ one has: c.as.character.rep.gender..8... c.6...F73..F78..F79..F82..6...F84..F94..F106..F109 1 w 2 2 w

Hi there, > Value=c(709,679,699,657,594,677,592,538,476,508,505,539) > Lard=rep(c("Fresh","Rancid"),each=6) > Gender=rep(c("Male","Male","Male","Female","Female","Female"),2) > Food=data.frame(Value,Lard,Gender) > Food Value Lard Gender 1 709 Fresh Male 2 679 Fresh Male 3 699 Fresh