similar to: Calculating the correlations of nested random effects in lme4

Hi, I'm new to R, and I have searched helpfiles and this forum on my 2 questions. Hope you guys can help me out! :-) Many thanks in advance! Cheers, Lars Q1: I imported a csv file with columnames subject and class. There are about 1000 different classes... It looks like this: subject1, class1 subject1, class2 subject2, class1 subject2, class3 ... subject999, class1 subject999, class2

I have a multiple text files, separated by a single space, that I need to combine into a single data.frame. Below is the track I'm on using list.files to capture the names of the files and then lapply with read.table. But I run into problems making a usable dataframe out of the data. #Creating example data in similar format to data I have sub <- rep(1,10) trial <- seq(1,10,1) size

*Respected Sir, I have a simple question regarding plots of time series in R. I have to plot "conc" against "time" **for each individual and display in the same panel for the in-built dataset "Indometh" in R. * ***I have six time series, say subject1.ts, subject2.ts, ............., subject6.ts. The observations are taken at an interval of 0.25 hr. All of the series

Hello everybody, I need an help because I don´t know if the command for the ANOVA analysis I am performing in R is correct. Indeed using the function aov I get the following error:"In aov (......) Error() model is singular" The structure of my table is the following: subject, stimulus, condition, sex, response Example: subject stimulus condition sex response

Hi I would like to transform a data frame like Subject Item Score Subject 1 Item 1 1 Subject 1 Item 2 0 Subject 1 Item 3 1 Subject 2 Item 1 1 Subject 2 Item 2 1 Subject 2 Item 3 0 .... *to * Subject Item1 Item2 Item3 .....Item N Subject1 1 0 1 Subject2 1 1 0 ........ SubjectP.. Apologize for the simple nature of my query but I am stuck.

I am trying to specify a mixed model for my research, but I can't quite get it to work. I've spent several weeks looking thru various online sources to no avail. I can't find an example of someone trying to do precisely what I'm trying to do. I'm hoping some smart member of this mailing list may be able to help. First off, full disclosure: (1) I'm an engineer by trade, so

I get the following message when using the reshape2 package line > tDat.m<- melt(Dataset) Using Item, Subject as id variables > tDatCast<- acast(tDat.m,Subject~Item) Aggregation function missing: defaulting to length Note Problem Statement- convert dataframe Subject Item Score 1 Subject 1 Item 1 1 2 Subject 1 Item 2 0 3 Subject 1 Item 3 1 4 Subject 2 Item 1 1 5

Dear R community, I am trying to fit an nlme model where I want to estimate the fixed effects of two treatments on the parameters on the following equation Photo~(a*(1-exp(-c*PARi/a)))-b I was able to fit a simple model without covariates following the method described in Mixed-Effects Methods and Classes for S and S-PLUS, version 3.0, but when I add the covariates, I get the error "

Dunno, your email provider doesn't want to talk to me, heh, screw them :) They claim i'm on an rbl, no rbl checks verify this. Even tried to submit a request using their website, but it's broken and doesn't work (http://postmaster.ausics.net/pmg.php) ----- Forwarded message from patrickdk at patrickdk.com ----- Date: Tue, 06 Oct 2009 23:31:37 -0400 From: Patrick

I am looking for help to analyze an unbalanced AB/BA cross-over design by requesting the type III SS ! # Example 3.1 from S. Senn (1993). Cross-over Trials in Clinical Research outcome<-c(310,310,370,410,250,380,330,270,260,300,390,210,350,365,370,310,380,290,260,90,385,400,410,320,340,220) subject<-as.factor(c(1,4,6,7,10,11,14,1,4,6,7,10,11,14,2,3,5,9,12,13,2,3,5,9,12,13))

Dear all, I'm calculating matrix correlations with permutation tests and I got this funny result. All correlation coefficients are the same with mantel.test {ncf} and pcol {simba} but the two functions yield dramatically different p-values (using the same number of permutations). Could anyone please enlighten me what is causing the difference and which result I can trust? (My matrices

Dear all, This may seem obvious for some of you, but it got me thinking. Can one use correlations (or R^2) as data for an ANOVA? The case in hand: I have several models fitting the same data (individual fits per subject). The different models fitting the same guy will produce different R^2, I was wondering if these could be used as dependent variable in a repeated measures design where the

vobolonkin at lic.co.nz wrote: > Full_Name: Vladimir Obolonkin > Version: tested in 2.0 to 2.4.1 > OS: linux, win, mac > Submission from: (NULL) (202.14.96.194) > > {{ Subject shortened manually -- to pass anti-spam filters > > Original-subject: Incorrect matrix of spearman correlations working \ > large (24000 by 425 and 78 by 425 data frames) in 64-bit Linux

Dear R users: Does anyone know if there are any R library functions for computing trimmed correlations, similar to the trim option in Splus's cor function? Also, does anyone know if there are any R library functions for computing a Euclidean distance matrix which can handle missing values, similar to Splus's dist function? Please reply directly to me, as I am not currently a list

Hello, I would like to know if the following problem can be solved using the survival package in R. I would like to perform a linear regression: Z=aX + bY + c a,b,c being constants to determine (with errors) X,Y,Z being measurement vectors. X being eventually censored. This problem has not been consistently addressed in my field (astronomy), that's why I need interdisciplinarity help!

Dear list members, Perhaps more a methodological than a strictly R question (but I have searched for solutions in R Site Search, R FAQ..., and I intend to implement the solutions in R. Answers containing references to existing R code are of course highly appreciated). What test(s) should be used for testing for differences between two partial (Pearson) coefficients, from independent samples,

Jens I'm not sure what you intend by "predefined assumptions". 1. If you merely want to conduct an exploratory rather than confirmatory analysis for the relevant paths, there are ways within SEM to do this. (In this case you could use John Fox's SEM package). 2. If you do not wish to assume multivariate normality, then you may use a variety of alternative (to maximum likelihood)

Dear John, Dear Joseph, Thank you for your quick answers and the pointer to semnet. I try to clarify on my assumptions: - yes, I am willing to assume multivariate normality - no, I don't want to assume a single factor model - I assume there is an unknown number of factors, and I do not know which items belong to which factors but I still want to estimate single item reliabilities Is this

This is a bit late, but: > About two years ago there was a thread about this which suggested that at > that time nobody had these coefficients ready to go. > (a) has anyone in the meanwhile programmed them? http://www.qimr.edu.au/davidD/R/polyr.R > (d) I appreciate this last item is not strictly an R question, but my > intention is to use these as input into the sem package for

hi, i'm looking for a smart way of distributing items to different groups according to their correlation(s). the correlation of items within one group should be minimal, whereas (canonical) correlation between the blocks should be maximal. example: you have 20 items, which are to be split into 4 blocks with 5 items in each block. (of course, the numbers are to be interchangable). so far, 2