similar to: combined random effects

Hello R-users, I am currently trying to learn how to use the function gls of the nlme library. I fitted the following model: Generalized least squares fit by REML Model: response ~ array + dye + genes + variety + variety * genes + array * genes + dye * genes Data: data I have 11 arrays, 2 dyes, 2 varieties, 3200 genes, and 2 replications for each. Therefore I should have the corresponding

Hi all, when i run this script: >read.table("Angelika.txt",header=T,sep="\t") >mytable=read.table("Angelika.txt",header=T,sep="\t") >for ( dye in c("A","B","C","F","G","K","L","M")) + { + for (cond in 1:8) + { + measurement =

Dear all, I have a performance problem in terms of computing time. I estimate mixed models on a fairly large number of subgroups (10000) using lme(.) within the by(.) function and it takes hours to do the calculation on a fast notebook under Windows. I suspect by(.) to be a poor implementation for doing individual analysis on subgroups. Is there an alternative and more efficient way for doing

Hi all, I am having memory allocation problem with my R 2.2.1 for Mac OS. The following is the error message that I get. I do not get this message if I break down the large dataset in to sub datasets. I think breaking up the dataset is not a sustainable solution in the long run. The data that I am analysing is essentially big, and it would be reasonable to do the analyis on the whole dataset

Given two numeric vectors of possibly unequal length, I'd like to pair each element of the shorter vector with an element of the longer vector such that the sum of squared differences between the pairs is minimized. Can someone point me to an R function or an algorithm for accomplishing this? All the best, Tom Thomas S. Dye, Ph.D. T. S. Dye & Colleagues, Archaeologists, Inc.

Aloha all, I love using ggplot. It took a while to get used to the grammar of graphics, but it is starting to get easy now that I am thinking in a more structured way. A question. I'm making a wind rose that I'd like to be oriented with due north straight up. I've discovered that the orientation is sensitive to how north is represented. When north is represented as 0,

Aloha all, I have a data frame with 4 columns. The first three are factors (f1, f2, f3) and the fourth is numeric. I'd like to explore these data using median polish. To do that I plan to use medpolish() on the matrix[f1,f2xf3], then medpolish on the resulting matrix[f2,f3]. This approach is described by Cook on page 141 of Exploring Data Tables, Trends, and Shapes. split()

Hi, I have a linear mixed-effects model object and want to extract the 95% confidence intervals for the fixed and random effects, respectively. I found the function intervals() for confidence intervals for the fixed effects but no corresponding function for the random effects. Does it exist or do I have to calculate the confidence intervals for the random effects myself? Greetings, joerg

Aloha all, I have an adjacency matrix for an acyclic digraph that contains transitive relations, e.g. (u,v), (v,w), (u,w). I want a DAG with only intransitive relations. Can someone point me to an R function that will take my adjacency matrix and give me back one with only intransitive relations? In the example, I'd like to get rid of (u,w) and keep (u,v) and (v,w). All the best, Tom --

I am a novice R user, and have been in trouble to get the right mixed model syntax in microarray analyses. There are three factors: Dye(2 levels), Temperature(3 levels) and Array(3 for each Temperature with a total of 9 arrays). I want to treat array as random, and to regard array variation different between Temperatures. So the model I want to seek is: Y = Dye + Temp + Dye + Temp*Dye +

Hello all, I have data: Genes time rep vart dye y trt 130911 sa1-d07 030min 1 col g 9.636244 o 145771 sa1-d07 030min 1 col r 8.107577 c 93335 sa1-d07 030min 1 ler g 7.409566 o 94821 sa1-d07 030min 1 ler r 5.107160 c 10119101 sa1-d07 030min 2 col g 8.336862 o 11605101 sa1-d07 030min 2 col r 7.824530 c 725313 sa1-d07

Hi, does anybody know of a nice test to detect trend turning points in time series? Possibly with reference? Thanks, joerg

Hi, concerning my earlier mail, maybe someone has noted from the variable names that I try to analyse mircoarrary experiments. Does anybody know of a R-implementation of the two-step mixed-model normalization procedure proposed by Wolfinger et al. (2001) J. Comput. Biol. 8:625-637? That would be great, best, joerg

Hi, I have two non-normal distributions and use interquartile ranges as a dispersion measure. Now I am looking for a test, which tests whether the interquartile ranges from the two distributions are significantly different. Any idea? Thanks, joerg [[alternative HTML version deleted]]

Dear R helpers, I am working with R 2.4.1 GUI 1.18 (4038) for MacOSX. I have a matrix of 10 000 genes and try to run the following commands: > model.mix<-makeModel (data=data, formula=~Dye+Array+Sample+Time, random=~Array+Sample) > anova.mix<-fitmaanova (data, model.mix) > test.mix<-matest (data, model=model.mix, term="Time", n.perm=100, test.method=c(1,0,1,1))

Guys can you tell me if a samba 4 DC implements any kerberos polices. am wanting to extend our kerberos ticket expiry values to 24hours is this possible ? -- Damien Dye IT Manager *Sondrel Ltd* Sondrel House, Theale Lakes Business Park Moulden Way, Sulhamstead, Berkshire, RG7 4GB, UK Tel: +44(0)118 9838 550 www.sondrel.com [image: Sondrel] <http://www.sondrel.com/> This e-mail and

Guys is it possible on samba 4 to raise the expiry time on tgt tickets to be more than 10 hours ? I have users running scripts that need a longer expiry time on the token on another side note is it the Samba 4 DC that decides the renewal date of the tokens also ? Regards -- Damien Dye IT Manager *Sondrel Ltd* Sondrel House, Theale Lakes Business Park Moulden Way, Sulhamstead, Berkshire,

Does anyone have nice quality controlls for affy arrays,.... Can't find any tools as are being used for 2 dye arrays..... cheers, marinus This e-mail and its contents are subject to the DISCLAIMER at http://www.tno.nl/disclaimer/email.html [[alternative HTML version deleted]]

Hello, I use lme4 package. library(lme4) fit=lmer(y ~ time+dye+trt+trt:time + (1|rep), data=dataset, na.action='na.omit') anova(fit) The anova gives sequential F-tests and sequential SS. My question is: how I can get partial F-tests and partial SS? For lm (not lmer) anova(lm(y~x+z)) we can use anova(fit, ssType=3) but it is not work for lmer. Natalia.

Hi, I'm trying to use the i-best software. Does anyone have experience from this, I can't get it to work with my data. Here is the code: T1 <- read.delim(file="S://SEDIM//Yvonne//2_5//T1.txt",col.names= c("Dye/Sample_Peak", "Sample_File_Name", "Size", "Height", "Area_in_Point", "Area_in_BP",