similar to: Use of lme() function

Dear R-users, We expect to develop statistic procedures and environnement for the computational analysis of our experimental datas. To provide a proof of concept, we plan to implement a test for a given experiment. Its design split data into 10 groups (including a control one) with 2 mesures for each (ref at t0 and response at t1). We aim to compare each group response with control response

Hi, Is anyone has solved MR Xin Meng problem (see below) ? We have the same analysis configuration : 10 groups (including control one) with 2 mesures for each (ref at t0 and response at t1). We expect to compare each group response with control response (group 1) using a multiple comparison procedure (Dunnett test). In order to perform this test, we have to normalize our data (as you) to

Dear R-users, We expect to create N homogenic groups of n features from an experimentation including N*n mesures. The aim of this is to prevent from group effects. How to do that with R functionalities. Does anyone know any methodes enabling this ? Best regards. Alexandre MENICACCI Bioinformatics - FOURNIER PHARMA 50, rue de Dijon - 21121 Daix - FRANCE a.menicacci at fr.fournierpharma.com t??l

Dear R-users, We expect to form N homogeneous groups of n features from an experimentation including N*n data. The aim is to prevent group effects. How to do that with R functionalitites ? Does anyone know any way enabling this ? Example : 100 patients are observed. 3 biochemical parameters are mesured for each one (Red and white globules ans glycemia). Patient RG RW

Dear R-users, I would like to build N "tkentry" compounds in the same window, with default text for each. As N is variable I need to construct them in an iterative way : library(tcltk) main<-tktoplevel() tktitle(main)<-"My Tool" filenames<-c("toto","tata","titi") N<-length(filenames) for (i in 1: N) {

Hi, all: Two examples are shown below. I want to use the multiple comparison of Dunnett. It succeeded in upper case "example 1". However, the lower case "example 2" went wrong. In "example 2", the function pmvt return NaN, so I cannot show this simtest result. Is there any solution? (I changed the variable "maxpts" to a large number in front of the

Dear R-helpers, We marked 6000 leaves from 5 SPECIES - 10 individuals/species - in two different TREATMENTs: a control and a dry-plot from which 50% of incoming precipitation was excluded. We followed those leaves for 42 months and noted the presence and absence at each visit. I then carried out a Cox Harzard model to see differences in leaf mortality between parcels and among species over time:

Hi, I've got a problem building a contrast matrix for the Dunnet contrast in package multcopm. The following works fine: > summary(simtest(adiff ~ trial)) Simultaneous tests: Dunnett contrasts Data: adiff by trial Contrast matrix: trial1 trial2 trial3 trial4 trial5 trial2-trial1 -1 1 0 0 0 trial3-trial1 -1 0 1 0 0

Hi! I use simtest fonction of multcomp package to compile a Dunnett's test. I have 10 treatments and one control group, so i create a matrix with: m<-matrix(0,10,11) m[1,1]<--1 m[1,2]<-1 m[2,1]<--1 m[2,3]<-1 m[3,1]<--1 m[3,4]<-1 m[4,1]<--1 m[4,5]<-1 m[5,1]<--1 m[5,6]<-1 m[6,1]<--1 m[6,7]<-1 m[7,1]<--1 m[7,8]<-1 m[8,1]<--1 m[8,9]<-1

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

# R for Windows will not send your bug report automatically. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs at r-project.org # ###################################################### This report is joint from Richard Heiberger <rmh at temple.edu> and Burt Holland <bholland at temple.edu>. Burt Holland is the coauthor

Hello, happy new year to all. The new Year gave me a new computer with Win98 and promptly I installed R on it. I created a directory R, with subdirectories gcc, perl,bin, helpwrk and rw1040. I got rw1040 from BDR place, the rest were gotter from the internet in the last week of the year, i.e. they are the newest versions. I modified autoexec.bat to put gcc\bin, perl\bin, etc in the path. I put the

Hi,. We have 2 values (first formant F1, second formant F2) for a given phoneme for six languages. We want to see whether the languages are significantly different one from another for this given phoneme. We have done a manova on our data and it works well, but we doesn't allow us to see which pair of languages are different. If we have only one formant for the phoneme, we would use

Hi, I've got a data set with a control group and a number of experimental groups, that have unequal sample sizes, and am measuring the number of people in each that respond yes or no. I'd like to use a dunnett test in R, where the syntax is supposed to be like: library(multcomp) test.dunnett=glht(anova_results,linfct=mcp(method="Dunnett")) confint(test.dunnett)

I have the following script for generating a dataset. It works like a champ except for a couple of things. 1. I need the variables "itbs" and "map" to be negatively correlated with the binomial variable "lunch" (around -0.21 and -0.24, respectively). The binomial variable "lunch" needs to remain unchanged. 2. While my generated variables do come out

Dear R-users I analysed the same data set by two different ways; analysis of covariance by using lm and anova functions and multiple comparison by using simtest function in the multcomp library. The output from the analysis of covariance is; > y<-lm(D~Cond+Q1,data=x) > anova(y) Analysis of Variance Table Response: D Df Sum Sq Mean Sq F value Pr(>F) Cond 2

Hello, I'm trying to reproduce some SAS result wit R (after I got suspicious with the result in R). I struggle with the contrasts in a linear model. I've got three factors > d$dose <- as.factor(d$dose) # 5 levels > d$time <- as.factor(d$time) # 2 levels > d$batch <- as.factor(d$batch) # 3 levels the data frame d contains 82 rows. There are 2 to 4 replicates of

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@r-project.org # ###################################################### The multcomp functions work on "lm" objects as anticipated. They do not work on

Hello all, I would like to do a Dunnett test in glht(). However, the factor level I want to use as the control is not the first. dunn1<-glht(model3, linfct = mcp(Container = "Dunnett"), alternative = "less") The factor container has 8 levels, so it would be nice not to manually enter in all of the contrasts. I originally discovered glht() when working with a glm model