similar to: scuba 1.2-2 posted

scuba 1.2-1 ** now with added Helium ** 'scuba' is a contributed R package that performs theoretical calculations about scuba diving --- dive profiles, decompression models, gas toxicity and so on. New features in version 1.2-1: . Breathing gases may now contain Helium as well as Oxygen and Nitrogen. . Decompression models now handle breathing gases containing

scuba 1.2-1 ** now with added Helium ** 'scuba' is a contributed R package that performs theoretical calculations about scuba diving --- dive profiles, decompression models, gas toxicity and so on. New features in version 1.2-1: . Breathing gases may now contain Helium as well as Oxygen and Nitrogen. . Decompression models now handle breathing gases containing

Version 1.1-8 of package 'scuba' has been uploaded to CRAN. 'scuba' is a package for scuba diving calculations and decompression models. It supports dive profiles (tables, plotting etc), analysis of dive profiles using decompression models, gas toxicity calculations, and gas usage calculations. New features in version 1.1-8: . support for dive profiles uploaded from a dive

Okay okay - forgive me for being a whiney itchbay. But the fix was (when discussing *nix systems) quite counter intuitive ... I noticed that, even after using chmod #uid file, that the system was not returning the string name for the appropriate numerical uid. So, since I was headed out to lunch, I went ahead and rebooted the server. Lo and behold it all appears to work now. Correctly even.

Here is patch 1 of 2. Roger diff -rup nut-2.7.4.orig/clients/upsmon.c nut-2.7.4.dev/clients/upsmon.c --- nut-2.7.4.orig/clients/upsmon.c 2015-12-29 13:08:34.000000000 +0100 +++ nut-2.7.4.dev/clients/upsmon.c 2016-07-01 09:46:21.567766415 +0200 @@ -525,6 +525,18 @@ static int get_var(utype_t *ups, const c numq = 2; } + /* Subcommands for polling SIGUSR1, SIGUSR2 RP */ + if (strcmp(var,

Hi all, I have used the sciptaculous Ajax autocompleter a few times now. A major problem I have at the moment is with a text field that has focus when a page loads. The text field has an autocompleter associated with it, but when the user starts typing, the firefox saved form data autocompleter appears first, and the scriptaculous one is partially hidden behind it. Has anyone else encountered

I have a RedHat Linux 9 server that I would like to allow users in my Windows 2000 domain to be able to map shares from without actually having an account on the system. Compiled samba, configured with "./configure --with-pam". Got the server into the domain, and regular "security = domain" seems to be working appropriately - providing there's a local account with the

Hi, I am doing an analysis to see if these is tissue specific effects on the gene expression data . Our data were collected from 6 different labs (batch effects). lab 1 has tissue type 1 and tissue type 2, lab 2 has tissue 3, 4,5,6. The other labs has one tissue type each. The 'sample' data is as below:

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

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 +

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

My small brain is having trouble getting to grips with lme() I wonder if anyone can help me correctly set the random = argument to lme() for this kind of setup with (I think) 9 variance/covariance components ... Study.1 Study.2 ... Study.10 Treatment.A: subject: 1 2 3 4 5 6 etc. 28 29 30 Treatment.B: subject: 31

Hello, I will start out with the caveat that I'm not a statistician by training, but have a fairly decent understanding of probability and likelihood. Nevertheless, I'm trying to fit a nonlinear model to a dataset which has two main factors using nlme. Within the dataset there are two Type categories and four Tissue categories, thus giving me 8 datasets in total. The dataset is in

Dear R users, I have a data frame in the form below, on which I would like to make normality tests on the values in the ExpressionLevel column. > head(df) ID Plant Tissue Gene ExpressionLevel 1 1 p1 t1 g1 366.53 2 2 p1 t1 g2 0.57 3 3 p1 t1 g3 11.81 4 4 p1 t2 g1 498.43 5 5 p1 t2 g2 2.14 6 6 p1 t2 g3 7.85 I

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

Hi Dear all, I have some gene expression data samples from different tissue types ----------------------------------------------- - 120 samples from blood (B) - 20 samples from Liver (L) - 15 samples from Kidney (K) - 6 samples from heart (H) ----------------------------------------------- All the samples are from different individuals, so there are in total 161 individuals from which the DNA was

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

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,

Esteemed R-forum subscribers, I'm having a tough time configuring contrasts for my 3-way ANOVA. In short: I don't know how to configure (all) my contrasts correctly in order to specify (all) my comparisons of interest. I succeeded getting my contrasts of interest set up for a simpler 2-way ANOVA based on the fairly intuitive logic of the design col.names. But i'm not able to

Dear all I apologize for cross-posting, but first it is accepted custom to thank the repliers and give a summary, and second I have still the feeling that this problem might be a general statistical problem and not necessarily related to microarrays only, but I might be wrong. First, I want to thank Robert Gentleman, Mark Kimpel and Mark Reiners for their kind replies. Robert Gentleman kindly