similar to: question related to multiple regression

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

Hello R Forum users, I was hoping someone could help me with the following problem. Consider the following "toy" dataset: Accession SNP_CRY2 SNP_FLC Phenotype 1 NA A 0.783143079 2 BQ A 0.881714811 3 BQ A 0.886619488 4 AQ B 0.416893034 5 AQ B 0.621392903 6 AS B 0.031719125 7 AS NA 0.652375037 "Accession"

Dear R-experts, My problem is how to handle a 10GB data file containing genotype data. The file is in a particular format (Illumina final report) and needs to be altered and merged with phenotype data for further analysis. PERL seems to be an frequently used solution for this type of work, however I am inclined to think it should be doable with R. How do I open a text-file, line by line,

Hi, My query is regarding permutation test and reshuffling of genotype/phenotype data I have been using the haplo.stats package of R. for haplotype analysis and I would like to perform an analysis which I'm requesting your advice. I have a data set of individuals genotyped for 12 SNP and a dichotomous phenotype. At first, I have tested each of those SNP independently in order to bypass

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 data in a dataframe with 139104 rows which is multiple of 96x1449. i have a phenotype file which contains the phenotype information for the 96 samples. the snp name is repeated 1449X96 samples. I haveto merge the two dataframes based on sid and sen. this is how my two dataframes look like dat<-data.frame(snpname=rep(letters[1:12],12),sid=rep(1:12,each=12),

Hi All, this could be a simple question but I am looking into modifying a data frame using a "condition" without the need to loop over that data, would that be possible? I have tried the following > x<-c(4,5,6,6,8) > y<-c("a","b","b","b","c") > data<-data.frame(x,y) > data x y 1 4 a 2 5 b 3 6 b 4 6 b 5 8 c if

Hi, I am new to R. and even though I've made my code to run and do what it needs to . It is taking forever and I can't use it like this. I was wondering if you could help me find ways to fix the code to run faster. Here are my codes.. the data set is a bunch of 0s and 1s in a data.frame. What I am doing is this. I pick a column and make up a new column Y with values associated with that

Hi, I am trying to run PCA on a set of data with dimension 115*300,000. The columns represnt the snps and the row represent the individuals. so this is what i did. #load the data code<-read.table("code.txt", sep='\t', header=F, nrows=300000) # do PCA # pr<-prcomp(code, retx=T, center=T) I am getting the following error message "Error: cannot allocate vector of

Hi, I need to rbind two data frames. Each one has a header . after the rbind I would like to keep the header for each and have the two data frames separated by a line. Is this possible to do in R? For example weight_mean weight_sd.dev > F 14.33333 4.932883 > M 34.66667 10.692677 > > hight_mean hight_sd.dev > F 35.00000 7.071068 > M 34.66667 10.692677 --

#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, #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

I am involved in a study where, as in most of life, men demonstrate themselves to be recalcitrant. So while we have many probands and most of their mothers we only have about 50% of the trios being complete. I have been running tdt and trio.types. It appears as if it is ignoring the duos. Sometimes a duo can be informative. For instance Father ..missing Mother 1/2 Proband 1/1 This duo shows that

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

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

Dear R users, I have a problem with something called "NaNs" in a nested mixed model. The background is that I have studied the number of insect nymphs emerging from replicated Willow genotypes in the field. I have 15 replicates each of 4 Willow genotypes belonging two 2 Willow species. Now I want to elucidate the effect of Willow genotype on the number of emerging nymphs. Previously I

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, 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

-----Original Message----- From: Ghosh, Sandeep Sent: Thursday, June 30, 2005 5:43 PM To: 'Berton Gunter' Subject: plot legend outside the grid Thanks for the pointers... I managed to get everything to look and feel the way I want except for the legend to plot outside the grid... Thanks for the note on the par, but I'm not able to it to plot outside the plot grid.. dataFrame <-