similar to: NaNs in Nested Mixed Model

HI, I'm trying to exclude some files & directories from a filesystem which I would like to copy to a different site. I did: # rsync -avz --exclude-file=/fs21/tmp/perl_scripts/exclude.txt /fs22/a/circuit_design mickey.willow.com:/cpu/store/design where file /fs21/tmp/perl_scripts/exclude.txt contains the following directories and files to be excluded from /fs22/a/circuit_design :

#Hello, #I have a question about obtaining results from a loop I have written. #Below is a sample of individual genotypes from a genetic question I am working on called "P.genotype.sample ". P.genotype.sample<-matrix(10,10,10) P.genotype.sample[,1]<-c(2,2,1,5,1,1,5,6,1,3) P.genotype.sample[,2]<-c(6,3,3,6,8,1,6,7,2,3) P.genotype.sample[,3]<-c(2,2,2,3,3,2,2,2,3,3)

Hello, My R skills are somewhere between novice and intermediary, and I am hoping that some of you very helpful forum members, whom I've seen work your magic on other peoples' problems/questions, can help me here. I have a matrix with the following format: (i) individual plants comprising many different genotype groups (i.e., a plant is genotype 1 or genotype 2 or genotype 3, etc). The

Hi, I am conducting an association analysis of genotype and a phenotype such as cholesterol level as an outcome and the genotype as a regressor using multiple linear regression. There are 3 possibilities for the genotype AA, AG, GG. There are 5 people with the AA genotype, 100 with the AG genotype and 900 with the GG genotype. I coded GG genotype as 1, AG as 2 and AA as 3 and the p-value for the

I want to specify a two-factor model in lme, which should be easy? Here's what I have: factor 1 - treatment FIXED (two levels) factor 2 - genotype RANDOM (160 genotypes in total) I need a model that tells me whether the treatment, genotype and interaction terms are significant. I have been reading 'Mixed effects models in S' but in all examples the random factor is not in the main

Hello and Thanks in Advance, I've been at this for three days now and can't think of anything else to try. Been through the docs and #samba on openprojects and even begged for direction in #samba-technical who suggested writing here. On the ancient RedHat 5.2 server (SAMBA 1.9?) both boxes work OK and continue to work OK with both servers now up (new one is from Mandrake 8.2 - SAMBA

Dear all, Lattice provides automatic coloring for subgroups on each panel by the simple use of a groups statement. For an application I want to change these colors to a predifined set. This works well using a panel function in stead of the default as long as there are only points in the graphs. When I set type="b" things get messed up. Any idea why? I include sample code for

Dear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps but struggling. Any help is deeply appreciated.   1) Experimental design: I have 40 plants each of which has 4 clones. Each clone planted to one of 4 blocks. Phenotypes were collected from each clone for 3 consecutive years. I have genotypes of plants. I

I'm new to R (previously used SAS primarily) and I have a genetics data frame consisting of genotypes for each of 300+ subjects (ID1, ID2, ID3, ...) at 3000+ genetic locations (SNP1, SNP2, SNP3...). A small subset of the data is shown below: SNP_ID SNP1 SNP2 SNP3 SNP4 Maj_Allele C G C A Min_Allele T A T G ID1 CC GG CT AA ID2 CC GG CC AA ID3 CC GG nc AA

Dear R list, I think my question maybe easy for you but I really spent entire day to resolve it. Say I have a matrix, rows are 6000 genes, columns(1-6) are 3 genotypes (a,b,c) with 2 repeat. I have to use two groups each time for t-test, a vs. c or b vs. c, but I dont know how to write correct codes. Below is my codes, the last two lines are needed to be corrected....

Dear all, I am trying to repeat an example from Sokal and Rohlfs "Biometry" -- Box 11.4, example of a randomized-complete-blocks experiment. The data is fairly simple: series genotype weight 1 pp 0.958 1 pb 0.985 1 bb 0.925 2 pp 0.971 2 pb 1.051 2 bb 0.952 3 pp 0.927 3 pb 0.891 3 bb 0.892 4

I have been merrily using the genetics package and more specifically have been using the makeGenotypes and genotypes function. I check my accomplishments by going > class(g2) [1] "genotype" "factor" and likewise > class(g1) [1] "genotype" "factor" Yet when I execute a command such as allele count I get this > allele.count(g1) D I [1,]

Hi, I'm using R for a QTL analysis of SNP data. I was wondering if anyone had any advice on fitting a dominance effect into the following function; > myfun4 function (x) { x <- scan(con, nmax=169) y <- unique(x[which(!is.na(x))]) if(length(y)>1) { summary(lme(Ad ~ x, random= ~1|sire, na.action="na.omit")) } else {print("no.infomation")} } Con is the

Hello, Willow > C:\Downloads\llvm-gcc4.2-2.3-x86-mingw32\llvm-gcc4.2\bin>llvm-gcc a.c -o a > a.c:1: error: bad value (generic) for -mtune= switch Something is really broken at your side. Are you running Vista? -- WBR, Anton Korobeynikov

Sequenom has an odd format of calling a SNP genotype gg [1] "C" "GA" "A" "C" "C" "AG" "C" "C" "T" "G" homozygous A is called A and heterozygous is called AT The genetics package cannot handle the fact that some genotypes are declared with 2 letter while other are declared with only 1.

#Hello, #I have would like to paste a single column of a matrix # in pair wise fashion with other columns based upon # even and odd column numbers. # I can do it in a very clunky fashion and I know there # must be a better way. below is a sample matrix and my extremely # clunky code that gets the job done for a small matrix, but i plan to # do this on a much grander scale. any help would be very

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

Hi, Could someone tell me if this is the correct model syntax for the following dataset: lme(height~treatment+genotype+treatment*genotype,drought,random=~genotyp e) The dataset has two factors: one fixed - treatment, and one random - genotype. I need to test the effect of both factors to identify their significance. There are multiple (but not equal) replicates at each level of genotype (the

Yes I am running Vista. I tried running the program as an Administrator but I get the same error. What is a valid -mtune argument I can try? Thanks, Willow Anton Korobeynikov wrote: > Hello, Willow > > >> C:\Downloads\llvm-gcc4.2-2.3-x86-mingw32\llvm-gcc4.2\bin>llvm-gcc a.c -o a >> a.c:1: error: bad value (generic) for -mtune= switch >> > Something is

On Sun, Jul 27, 2008 at 10:15 PM, Willow Schlanger <wschlanger at gmail.com> wrote: > Yes I am running Vista. I tried running the program as an Administrator > but I get the same error. What is a valid -mtune argument I can try? generic *is* a valid value for mtune; however, it's was added to gcc relatively recently. This is only a rough guess, but the most likely issue is that