similar to: genetics package not working

Dear R-devel members, We are building a new package (GeneticsBase) for analysis of genetic data . While doing "R CMD check with R-2.1.1, I am getting the following error: ** building package indices ... Error in "colnames<-"(`*tmp*`, value = c("family", "pid", "father", "mother", : length of 'dimnames' [2] not equal to

Hi, I have a genotype data for both case and controls and would like to calculate the HW p-value. However, since the number of one genotype is 0, I got wired result. Would someone help me to figure it out? Or confirm it's right? Thanks a lot. ============ > library( "genetics" ) NOTE: THIS PACKAGE IS NOW OBSOLETE. The R-Genetics project has developed an set of enhanced

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 R-help community, I have another question about filtering datasets. Please consider the following "toy" data matrix example, called "x" for simplicity. There are 20 different individuals ("ID"), with information about the alleles (A,T, G, C) at six different loci ("Locus1" - "Locus6") for each of these 20 individuals. At any single locus

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)

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

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

Hi all, [this is a bit hard to describe, so if my initial description is confusing, please try running my code below] #WHAT I'M TRYING TO DO I'd appreciate any help in trying to speed up some code. I've written a script that converts a matrix of integers (usually between 1-10,000 - these represent allele names) into two new matrices of normally distributed values (representing

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 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 user: I am studying the allele data of two populations. the following is the data: a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12 a13 a14 a15 a16 a17 pop1 0.0217 0.0000 0.0109 0.0435 0.0435 0.0000 0.0109 0.0543 0.1739 0.0761 0.1413 0.1522 0.1087 0.0870 0.0435 0.0217 0.0109 pop2 0.0213 0.0213 0.0000 0.0000 0.0000 0.0426 0.1702 0.2128 0.1596 0.1809 0.0957 0.0745 0.0106

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,

I am about one step away from heaven on earth. I think only one step! I am using dgc.genetics to run a TDT test on thousands of genetic loci. I have learnt (through the help of others on this mailing list) to send the complex output to useful data frames which in turn allow me to look at the big picture and screen the thousands of loci. Resultdt<-lapply(PGWide[,240:290], tdt) the above

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

I have a vector of 2,1,0 I want to change to 0,1,2 respectively (the data is allele dosages) I have tried multiple nested if/else statements and looked at the ?if help and cannot work out what is wrong, other people have posted code which is identical and they state works. Any help would be greatly appreciated. > A[1:20] [1] 1 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 1 1 0 0 > B <-

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 <-

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

Hi all, We are using two methods to identify SNPs. One is based on resequencing the genome and aligning the reads to the sequenced genome to identify SNPs (data available for 44 individuals). Another is based on SNP array with selected loci (30000 loci, 870 individuals). I want to compare the results from the resequencing based minor allele frequency and Array based minor allele frequency.

I'm sorry everyone for the inconvenience of spamming the R-help... Here's the complete post: Hi everyone, > > Since it's quite a while that I used the reshape package, I now feel kind > of rusty. > > I have a data.frame like this: > > > > id Sample.Name Marker Allele.1 > Allele.2 sample_id species