similar to: Multidimensional scaling and distance matrices

I've finally narrowed down a puzzling problem: here is the short test case. tmt34% R --vanilla R version 2.10.0 (2009-10-26) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 > temp <- matrix(runif(50), ncol=2) > t(temp) %*% temp [,1] [,2] [1,] 7.916016 6.049698 [2,] 6.049698 7.650694 > library(kinship) Loading required package:

I wish to make modifications to the plot.pedigree function in the kinship package. My attempts to contact the maintainer have been unsuccessful, but my question is general, so specifics of the kinship package might not be an issue. My first attempt was to make a new function Plot.pedigree in the .GlobalEnv which mostly achieved what I wanted to. However, I'm sure that's not the tidiest

Hi, I have a problem concerning discrepances between R (which I use) and Statistica (which uses my supervisor). I can't say what is the origin of these differences but unfortunately my supervisor doesn't know that either. Our response variable is number (or presence/absence) of parasites in rodents and explanatory variables are presence/absence of several alleles. The rodents were

Hello, I was hoping someone could answer a question for me that may either be statistical or script related. I don't come from a statistics background, so I am not positive if I am using the correct nomenclature or even the correct procedure. Is it possible to model "random coefficients" in a mixed effects cox-regression using coxme from the Kinship package? For example, using

I am a new R user attempting to convert files from Statistica to R. I can export from Statistica to SPSS .por format, but not to the SPSS .sav format. Is there a procedure for easily accomplishing this, which will allow me to keep variable short and long labels (big surveys LOTS of time to replace all this work). Many thanks for sharing your time and knowledge. steve Dr Steven M Burgess

Dear All, I have problem installing the 'kinship' package. I used the 'install.packages' from R command line and had no problem installing other packages before. Here is what I get: * Installing *source* package 'kinship' ... ** libs gcc -no-cpp-precomp -I/Library/Frameworks/R.framework/Resources/include -I/usr/local/include -fno-common -g -O2 -c agfit6b.c -o agfit6b.o

Hi Folks, Has anyone got a compiled binary of the package 'plm' and/or of the package 'kinship' for Debian Etch? I'm asking because I'd like to install 'plm', but have failed because it depends on 'kinship', and 'kinship' refuses to compile on my Debian Etch. The underlying reason is the Debian refuses to allow me to install the development

Hello, I like to use the package "kinship" (R version 2.2.0). After loading this package the operator %*% doesn't work. Example: R> library(kinship) R> a <- cbind(1:2,rnorm(2)) R> a%*%a Error message: Fehler in a %*% a : keine anwendbare Methode f侟r "%*%" The Message in English: Error in a %*% a: no applicable message for "%*%". What can I do?

I am making the question clear. Please help. > Dear R experts > > I was using kinship package to fit mixed model with kinship matrix. > The package looks like lme4, but I could find a way to extract p-value > out of it. I need to extract is as I need to analyse large number of > variables (> 10000). > > Please help me: > > require(kinship) > > #Generating

Hi Im using the kinship package to draw a pedigree. On my data set this works fine but when i add indivudals to the pedigree i keep getting an error i hope someone can help me! This is the code im using: Data<-read.table("Tree.txt", header=T, sep=",") attach(Data) ped<-pedigree(id, dadid, momid, sex, aff) par(xpd=T) plot.pedigree(ped) This is my data looks like

Hi,I use the 'lmekin' model of the 'kinship' package of R in order to estimate heritability. I want to estimate the confidence interval of the variance coefficient and so I should use a bootstrap simulation. The pedigree file has 1386 subjects so I create a kinship matrix [1386*1386].This is the code of R I use: kfit2 <- lmekin(IT~1+AGE +(1|ID), dati1,

Hello, I'm looking for a method to estimate narrow sense heritability of traits in a RIL population. Papers I've checked either use either SAS or SPSS or do not give any details at all. I've found some reference to using variance components in ANOVA, using the kinship or wgaim packages, but I don't have a clue as to how to do any of this. Is there any way fro a very R illiterate

I've been looking at the kinship package which looks as though it might be appropriate for my purposes. What I can't find is any reference to the data that is used in the example code. A dataframe called d10 with column names, upn, dadid, momid, sex and affect is required. One can get an idea of what sort of values should be in most columns from the description in the pedigree function,

Can we do Cholesky Decompositon in R for any matrix --------------------------------- [[alternative HTML version deleted]]

Hi everybody, I would like to compute the mean for 1 variable between the rows with the same levels. For example, with the dataset below: Factor1 Factor2 Value A X 1 A X 2 A Y 3 A Y 4 B X 5 B X 6 B Y

Hello, I am a new R user and trying to learn how to implement the mahalanobis function to measure the distance between to 2 population centroids. I have used STATISTICA to calculate these differences, but was hoping to learn to do the analysis in R. I have implemented the code as below, but my results are very different from that of STATISTICA, and I believe I may not have interpreted the help

I have library in development with a function that works when called from the top level, but fails under R CMD check. The paricular line of failure is rsum <- rowSums(kmat>0) where kmat is a dsCMatrix object. I'm currently stumped and looking for some ideas. I've created a stripped down library "ktest" that has only 3 functions: pedigree.R to create a pedigree or

Hello, All, GWAF 1.2 R.Version() is below. system(lme.batch.imputed( phenfile = 'phenfile.csv', genfile = 'CARe_imputed_release.0.fhsR.gz', pedfile='pedfile.csv', phen='phen1', covar=c('covar1','covar2'), kinmat='imputed_fhs.kinship.RData', outfile='imputed.FHS.IBC.GWAF.LME.output.0.txt' )) Gives the error messages: Error in

Hi, Sorry to bother you again. I would like to estimate the effect of several categorical factors (two between subjects and one within subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i

I'm currently packaging up some of the kinship matrix routines more formally, these are used in coxme when dealing with family correlation structures. One of my test programs exercises error conditions, i.e., it purposely feeds particular types of invalid pedigree data in to see if the right error message results. So there are comment-action pairs # the next line should generate a