similar to: Right formula for a mixed-design anova

Dear R-help list, I am trying to do a mixed ANOVA on a 8960 x 5 dataframe. I have 3 factors for which I want to test all main effects and interactions : f1 (40 levels), f2 (7 levels), and f3 (4 levels). I also have a subject factor, subject, and a dependent variable, dv. Some more information about the factors: f2 is a between-subject factor. That is, for each level of f2 there are 8 nested

Dear R Experts, I am attempting to run a mixed effects model on a within-subjects repeated measures design, but I am unsure if I am doing it properly. I was hoping that someone would be able to offer some guidance. There are 5 independent variables (subject, condition, difficulty, repetition) and 1 dependent measure (value). Condition and difficulty are fixed effects and have 3 levels each

Hi, I would like to run an ANOVA with repeated measures with both a between subjects variable and within subjects (repeated measures) variables. I know how to do this when I have equal numbers of subjects in both groups (i.e., balanced design). That has been very well documented by Baron, for example, see: http://cran.r-project.org/doc/contrib/Baron-rpsych.pdf On page 34 of this document,

Hi, I am not very familiar with complex ANOVA, so, I am not sure about the formula to use with aov() in this particular case. Could someone help me? Here is my data. I have an unbalanced plan with 4 factors: "Site", "Sex", "Age" and "Individual". "Site" is a 5-level fixed factor; "Sex" is of course a 2-levels fixed factor, as is

Hi, I wish to analyze with R the results of a perception experiment in which subjects had to recognize each stimulus among three choices (this was a forced-choice design). The experiment runs under two different conditions and the data is like the following: N1 : count of trials in condition 1 p11, p12, p13: proportions of choices 1, 2, and 3 in condition 1 N2 : count of trials in

Dear all, I am trying to using a mixed-design ANOVA to analyze my data. I have two between subject variables: location (2 different locations) and age group (young and old), and one within-subject variable: valence (positive, neutral, negative). I am looking at an Stevens(one between, two within) in Notes on the use of R for psychology experiments and questionnaires (P34 in this article). In

Dear r-help list, I would like to run a mixed design anova to compare the results from one population sample to another. Here my within subject variable (stiulusID) has 45 levels and my between subject variable (group) has two levels. In addition to my number of levels in the within subject variable being very large, one other 'feature' of my data is that it is not balanced on the between

Dear all, I've got a big problem. I try to analyse my data using R with a mixed model ANOVA without useful results and success. My data are as follows: 3 factors (Treatment, Site, Subsite) with 'Subsite' as random factor and nested into 'Site'. I want to analyse the effects of the three main effects (factorial design to a specified degrees (2)) with the interactions between

First of all, I apologize for asking a question that has appeared recurrently in this mailing list. However, I have googled for it, have looked at the mailing list archives, and also looked at Pinheiro & Bates book (although not very thoroughly, I must confess), to no avail. Here is the question: I am trying to obtain with lme or lmer the same exact numerical results (p-values) that I obtain

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

This issue has been brought at least twice in R-help: https://stat.ethz.ch/pipermail/r-help/2008-September/173828.html https://stat.ethz.ch/pipermail/r-help/2009-May/197360.html Looking at the source in src/unix/sys-std.c, it seems that the definition of rl_readline_name as "RCustomCompletion" is made after the readline library has been initialized and the ~/.inputrc file has been

I am trying to understand how to fit an ARMAX model with the arima function from the stats package. I tried the simple data below, where the time series (vector x) is generated by filtering a step function (vector u, the exogenous signal) through a lowpass filter with AR coefficient equal to 0.8. The input gain is 0.3 and there is a 0.01 normal white noise added to the output: x <- u

Hello dear R help group. I encountered this old thread (http://tinyurl.com/dklgsk) containing the a similar question to the one I have, but left without an answer. I am and hoping one of you might help. A simplified situation: I have a factorial design (with 2^3 experiment combinations), for 167 subjects, each one has answered the same question twice (out of a bunch of "types" of

I have been attempting to use cph models to get better calibration of my models for which I had originally used logistic regression. I tried running with 40 repetitions and got an error. I then tried 500 repetitions (thinking that the NaNs in the output below might be caused by that choice) and then let my computer crunch for several hours and got only the same error message and

Dear All, I am economist and working on poverty / income inequality. I need descriptive statitics like the ratio of education expentitures between different income quintiles where each household has a different weight. After a bit of google search I found 'Hmisc' and 'quantreg' libraries for weighted quantiles. The problem is that these packages give me only weighted quintiles;

(There really doesn't seem to be a dedicated e2fsprogs list, so this was the next-best place I could find. I don't place much faith in the sourceforge forums..) The filesystem in question is a 480 gig ext3 partition, on a hardware RAID5 controller. It's approximately 90% full, with something like 115K files. Kernel 2.4.20-pre4, on a Duron 900. As I type this, e2fsck has used up

Dear R-experts! I having trouble with the correct implementation of a mixed-model ANOVA in R. I my dataset subjects were tested in a cognitive performance test (numerical outcome variable 'score'). This cognitive performance test is devided into five blocks (categorical factor 'block'). All subjects were tested two times (in random order once following placebo treatment and once

Dear R and statistics experts: I have data of a behavioral experiment with the aim to investigate the effect of a memory task on motor learning. Question: I would appreciate help in figuring out a possible formula to determine whether motor learning across sessions differs between 2 groups. Design: - 2 Groups: group A: n=10 subjects, group B: n=10 - 6 motor learning sessions: baseline;

dear list, i am using a multifactorial design with two treatments (factor A: drugs, three levels; factor B: theraphy, two levels) and a time factor (three levels, different timepoint). hypothetically, i measured the same subjects for all treatements and timepoints, so its a repeated measurement design. now i ran an anova in R and also some Tukey post-hoc tests using glht. but what i am actually

Dear all, I need an help because I am really not able to find over internet a good example in R to analyze an unbalanced table with Anova with repeated measures. For unbalanced table I mean that the questions are not answered all by the same number of subjects. For a balanced case I would use the command aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), data=scrd)