similar to: multi-dimension array of raw

I do the these passages: library(Geneland) set.seed(1) data <- simdata(nindiv=200, coord.lim=c(0,1,0,1) , number.nuclei=5 , allele.numbers=rep(10,20), IBD=FALSE, npop=2, give.tess.grid=FALSE) geno <- data$genotypes coord <- t(data$coord.indiv) path.mcmc <-

Hi, Following my earlier posts about having problems performing a PCA, I have worked out what the problem is. The problem lies within the PLINK to gds conversion. It seems as though the SNPs are imported as "samples" and in turn, the samples are recognised as SNPs: >snpsgdsSummary("chr2L") Some values of snp.position are invalid (should be > 0)! Some values of

Hi everyone, I'm using the function 'HWE.exact' of 'genetics' package to compute p-values of the HWE test. My data set consists of ~600 subjects (cases and controls) typed at ~ 10K SNP markers; the test is applied separately to cases and controls. The genotypes are stored in a list of 'genotype' objects, all.geno, and p-values are calculated inside the loop over all

Hello All, Once again thanks for all of the help to date. I am climbing my R learning curve. I've got a few more questions that I hope I can get some guidance on though. I am not sure whether the etiquette is to break up multiple questions or not but I'll keep them together here for now as it may help put the questions in context despite the fact that the post may get a little long.

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, I am having really hard time finding a good article about simulating genotypes of cases and controls at a disease locus using R. if you guys can point me or guide me where i can find more information, it will be helpful. thanks, Claire -- View this message in context: http://www.nabble.com/genotypes-simulation-tp17065607p17065607.html Sent from the R help mailing list archive at

snpMatrix package is quite nice (read.plink())

Dear list I've a generic question about how to tune an SVM I'm trying to classify with caret package some population data from a case-control study . In each column of my matrix there are the SNP genotypes , in each row there are the individuals. I correctly splitted my total dataset in training(132 individuals) and test (50 individuals) (respecting the total observed genotypic

I know that there are quite a few packages out that there for cluster analysis. The problem that I am facing is finding a package that will not incorporate all my samples into clusters but just the samples that fit a threshold (that I have not set yet and may need help finding the right level) for genotyping. It should be able to "no call" samples outside the clusters. It also needs to

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,]

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

Dear R listers, I have a data file looks like the following: Testing marker: s_1 --------------------------------------------- Allele df(0) -LnLk(0) df(T) -LnLk(T) ChiSq p 3 7995 29320.30 7994 29311.85 16.90 4e-05 (2229/8000 probands) Testing marker: s_2 --------------------------------------------- Allele df(0)

The "genetics" package for handling single-locus genetic data is now available on CRAN in both source and Windows binary formats. The purpose of this package is to make it easy to create and manipulate genetic information, and to facility use of this information in statistical models. The library includes classes and methods for creating, representing, and manipulating genotypes

The "genetics" package for handling single-locus genetic data is now available on CRAN in both source and Windows binary formats. The purpose of this package is to make it easy to create and manipulate genetic information, and to facility use of this information in statistical models. The library includes classes and methods for creating, representing, and manipulating genotypes

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,

Has something changed in R that requires an update in the genetics package by Gregory Warnes? I am using R version 2.5.0 This used to work > summary(founders[,59]) to prove that it is a genotype class > class(founders[,59]) [1] "genotype" "factor" Now when I issue the command: > summary(founders[,59]) I get: Error in attr(retval, "which") <- which :

I a using plink on a large SNP dataset with a .map and .ped file. I want to get some sort of file say a list of all the SNPs that plink is saying that I have. ANyideas on how to do this? -- Thanks, Jim. [[alternative HTML version deleted]]

I evaluated the Bayes factor in the k=2 allele case with a "triangular" prior under the null as in the example in the help file: HWETriangBF2(nvec=c(88,10,2)) [1] 0.4580336 When I swap the n11 entry and n22 entry of nvec, I received totally different Bayes factor: > > HWETriangBF2(nvec=c(2,10,88)) [1] 5.710153 > In my understanding, defining the genotype frequency as

Hello, I am working with a matrix of multilocus genotypes for ~180 individual snail samples, with substantial missing data. I am trying to calculate the pairwise genetic distance between individuals using the stats package 'dist' function, using euclidean distance. I took a subset of this dataset (3 samples x 3 loci) to test how euclidean distance is calculated: 3x3 subset used

#If I have two lists as follows a1<- array(1:6, dim=c(2,3)) a2<- array(7:12, dim=c(2,3)) l1<- list(a1,a2) a3<- array(1:4, dim=c(2,2)) a4<- array(5:8, dim=c(2,2)) l2<- list(a3,a4) #how can I create a new list with the mean across all arrays within the list, so all components are included? As an example for [[1]]; cbind((l1[[1]][,1]+l2[[1]][,1])/2,