similar to: genotypes simulation

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 receiving the above error ( full r session output below) the script runs OK in windows. and "genotypes" and "ploidy" are both correct arguments any suggestions would be most welcome Nevil Amos MERG/ACB Monash University School of Biological Sciences > library(Geneland) Loading required package: RandomFields Loading required package: fields Loading required

Dear All, Does anybody have experience with R package pbatR (http://cran.r-project.org/web/packages/pbatR/index.html)? I am trying to use it to analyze the family-based case-control data, but the package totally doesn?t work on my computer. I contacted the authors of the package, but I haven?t heard anything from them. Following the package manual, I tried the simple example as below:

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.

Background: OS: Linux Mandrake 10.1 release: R 2.0.0 editor: GNU Emacs 21.3.2 front-end: ESS 5.2.3 --------------------------------- Colleagues Is there a function in R that is an equivalent of zoom in matlab? This is very useful for being able to magnify details in a plot. I have searched the help for "zoom", "interactive zooming", and "magnify". The R search

Dear mailing list, I'm still quite a newbie in the statistical analysis of genotype/allele data, resp. more generally in the analysis of categorical variables. Moreover, I'm currently totally confused by the many R packages available to do such analysis. Here is my case: I've got a list of genes, and a number of case-control population pairs, and for each population and gene, the

HI R users, I would appreciate information/examples/suggestions on building GUIs for R applications. I am currently working on a project that would require the following functionalities : 1) Display a window to the user. Provide a function to scan local drive and choose dataset file. 2) Display the column names for the user to choose the dependent variable and the independent variables. 3) Fit

We have some new Solaris boxes (both Sparc and amd64), and as they are not yet in production use I borrowed some time on them to run tests over CRAN packages, using the Solaris make and Sun Studio compilers. The results were quite depressing. Sun Studio 12 compilers are also available for Linux, and there the problems are worse (for C++ code). Line endings ============ We checked in R CMD

Hi, I have Agilent and Illumina SNP data. That's the only thing I know about the files - there seem to be no version specification in any of them. Is there a generic genotype calling algorithm that I could try to use on these datasets? thanks, N. -- View this message in context: http://www.nabble.com/generic-genotype-calling-algorithm--tp23141124p23141124.html Sent from the R help

I use dChip and Affymetrix gtype to apply some genotyping functions to some microarrays data. Do you know if similar R functions exist? Thank you! Francesco ----------------------------------------------------------- Francesco Falciano, Ph.D. CINECA (High Performance Systems) via Magnanelli, 6/3 40033 Casalecchio di Reno (BO)-ITALY tel: +39-051-6171724 fax: +39-051-6132198 e-mail:

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

Hi, I am trying to test for pairwise associations between genotypes ( Rows=individuals, Columns =genes, data are up to 4 genotypes per gene, some with 2,3 or 4) where each chisquare comparison is different depending on the genes tested. The test is the observed multilocus (across columns for each individual) genotypes vs the expectation, which is the product of the individual frequency for each

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 <-

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

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

Hi all, Here is my problem: I performed bioassays using a unique insecticide on 9 different genotypes and got their mortality depending on the dose of insecticide used. Now, I want to see wether some genotypes are different or not in their responses to insecticide. My problem is that I have up to four replicates for some genotypes, but only one for other... Due to this unbalanced design, I

I would like to store and manipulate large sets of marker genotypes compactly using "raw" data arrays. This works fine for vectors or matrices, but I run into the error shown in the example below as soon as I try to use 3 dimensional arrays (eg. animal x marker x allele). > a <- array(as.raw(1:6),c(2,3)) > a [,1] [,2] [,3] [1,] 01 03 05 [2,] 02 04 06 >

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 all; I need to put 12 different plot2 into the same matrix. So my array for the matrix will be par(mfrow=c(3,4)). I am running ggplot2 to produce my 12 plots. For some reason, par(mfrow=c(3,4)) did not turn out 3*4 matrix. my basic R codes for each plot is par(mfrow=c(3,4)) library(ggplot2) p <- ggplot(a, aes(x=Genotypes, y=Plant_hight, size=Plant_hight, color=Showing_rate)) + . . Best

Hi, I have a list p with different size dataframes and length of over 8000. I'm trying to calculate correlations between the rows of dataframes of this list and columns of another dataset (type data.frame also) so that first column is correlated with all the rows in the list dataframe. Some information from another dataset is also included to the final output (all.corrs). This worked a