similar to: RE: Comparison of correlation coefficients - Details

Dear Ioannis Thank you very much for pointing me to meta-analysis. Although it may not solve my problem with the normalization, it gives me some other options to display the different correlation coefficients. One possibility is the use of Funnel plots, which are even available in library(rmeta). Another possibility is the use of forest-plots, as implemented in rmeta as metaplot. Sorrowly,

Dear R-experts I would like to read all files from a directory, the files have names "myname0001.txt" etc. I paste the directory plus file names and use "read.delim()". My problem is that some file names are missing, so I get an error and my program stops. Is there a way to check for a null pointer analogous to C, so that I can simply skip non-existent filenames? Please do

Dear all Just for fun, I have just downloaded the paper mentioned below and checked it with R-1.6.1. Everything is ok with exception of Table 2b, where I get always 1 instead of 0.5: > pbinom(1e15,2e15,0.5) [1] 1 Which value should be correct? Best regards Christian Stratowa ============================================== Christian Stratowa, PhD Boehringer Ingelheim Austria Dept NCE Lead

dear members, i would like to write a variable in a plot title (main="") but i don't know the right syntax:(...i tried a lot of different ways without success. here my example: y=30 z=33 for (i in 10:length(tissue)) { png(filename = tissues[i], width = 1024, height = 768, pointsize = 12, bg = "white") gene.graph("ENSG00000115252", rma.affy, gps=list(1:3,

Hello, I've used this command to analyse changes in brain volume: mod1<-aov(Volume~Sex*Lobe*Tissue+Error(Subject/(Lobe*Tissue)),data.vslt) I'm comparing males/females. For every subject I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). As aov() provides only type I anovas, I would like to use lmer() with type II, however, I have

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 +

Hi, There are 20 subjects grouped by Gender, each subject has 2 tissues (normal vs. cancer). In fact, it is a 2-way anova (factors: Gender and tissue) with tissue nested in subject. I've tried the following: Model 1: lme(response ~ tissue*Gender, random = ~1|subject) Model 2: response ~ tissue*Gender + subject Model 3: response ~ tissue*Gender It seems like Model 1 is the correct one

Hello, I'm using aov() to analyse changes in brain volume between males and females. For every subject (there are 331 in total) I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). The data looks like this: Subject Sex Lobe Tissue Volume subect1 1 F g 262374 subect1 1 F w 173758 subect1 1 O g 67155 subect1 1 O w 30067 subect1 1 P g 117981

Dear all, I would like to know if it is possible to fit in R a Cox ph model with time-dependent covariates and to account for hierarchical effects at the same time. Additionally, I'd like also to know if it would be possible to perform any feature selection on this model fit. I have a data set that is composed by multiple marker measurements (and hundreds of covariates) at different time

> Dear expeRts, > > Currently, my colors are as follows: > mycol <- > c("blue1","blue2","blue3","blue4","black","yellow4","yellow3","yellow2","y > ellow1") > heatmap(snp, Rowv=NA, Colv=NA, col=mycol) > > However, I would like to have the following colors: > bright blue ->

Hi, I've lost my mind on it... I have to scatterplot two vectors, grouped by a third variable, with two different dimensions according to whether each cell line in the plot is sensitive or resistant to a given drug, and with a different color for each of 9 tissues of origin. Here's what I've done:

Dear Spencer Thank you for this extensive explanation of the problem. I was just curious. Best regards Christian ============================================== Christian Stratowa, PhD Boehringer Ingelheim Austria Dept NCE Lead Discovery - Bioinformatics Dr. Boehringergasse 5-11 A-1121 Vienna, Austria Tel.: ++43-1-80105-2470 Fax: ++43-1-80105-2683 email: christian.stratowa at

Hi, I am looking for a R package which is capable to process and analysis pictures of tissues (stained) in an automatic way. I had a look on biops and EBImage (Bioconductor) but they are not automatic... Did you already use/know a such package ? Thanks, - Martial _________________________________________________________________ Tchattez en direct en en vidéo avec vos amis !

Hello, I'm fitting linear mixed models to gene-expression data from microarrays, in a data set where 4608 genes are studied. For a sample of 5 subjects and for each gene we observe the expression level (Intensity) in four different tissues: N, Tp, Tx and M. I want to test whether the expression level is different accross tissues. Between-subject variability is modeled with a random

Dear all, When running function 'testQAReport()', which uses function 'buildVignettes()' to create a pdf-file I get the following error: > source("testQAReport.R") > testQAReport() Error in .get_package_metadata(pkgdir) : Files 'DESCRIPTION' and 'DESCRIPTION.in' are missing. Since I did not get this error in earlier versions of R, could you

Hello, I am using Mechanize to post a form to a website. When I do this by hand in my browser the response takes about 35s to come back (it''s a long page full of tables and graphics). When I do this with Mechanize, the server starts to respond and then appears to hang. The obvious conclusion is that my code is wrong but I am reasonably sure that I haven''t altered it

Dear R-user group I am currently running R-0.90.1-1 for LinuxPPC on my PowerBook. Few days ago I have seen at CRAN that a new R-package exists, namely R-base-with-hdf5-0.90.1-2. Sorrowly, I could not find any explanation, what the difference to the other package is. I have downloaded the "Introduction to HDF5 Release 1.2" from hdf.ncsa.uiuc.edu/HDF5. It seems that HDF5 is a data

Dear all, First I apologize for cross-posting, but I think that this could be of interest to BioC users, too. For DNA copy-number analysis I have downloaded PLASQ500K from: http://genome.dfci.harvard.edu/~tlaframb/PLASQ/ After creating sub-directories SND and STD containing 3 Sty Mapping arrays each, I tried to compute parent-specific copy number: > library(PLASQ500K) > psCN <-

Hi, Thanks in advance for any advice you can give me, I am very stumped on this problem... I use R every day and consider myself a confident user, but this seems to be an elementary problem.. Outline of problem: I am analysing the results of a study on protein expression in cancer tissues. I have raw intensities from 2 different types of cancer and normal tissue, which can be taken from several

Hi, Maybe this is a simple question: Using S-Plus, and having a matrix m with row- and col-names, I have the following problem: When I import into a function the one-column matrix m[,i], I am no longer able to get the column name. Is there a way to get the column-name? (Although attributes(m) displays row- and col-names, attributes(m[,i]) does no longer show the col-name.) Thank you for your