similar to: internal validation_logistic regression results

Hello, I would like to plot specific SNPs with their exact locations on a chromosome. Based on my genotyping results I would like to separate these SNPs in three different categories: 1, 2 and 3 and use different colours to represent these categories. The script below generates the sample data. I can plot these with the image function using the following: val <- 1:3 samp <- sample(val,

I'm pretty new to R, and I've been given a script by a user who wants some help with it. I know enough about the way R works to know that this is a very inefficient way to do what the user wants (the LSB_JOBINDEX stuff is added by me so that this can work on many hundreds of input data files as LSF jobs - it's the nested loops I'm really interested in):

Hi, Does anybody know how to obtain the imputed SNP genotype probabilities from the snpStats package? I am interested in using an imputation method implemented in R to be further used in a simulation study context. I have found the snpStats package that seems to contain suitable functions to do so. As far as I could find out from the package vignette examples and its help, it gives the

Dear all, I generated a dataset with 500 unrelated individuals and 10 biallelic SNPs. From this dataset,I would like to create data with 5% missingness on genotype information at random and also data with 5% genotyping error. Can someone help me with how I can do it. [[alternative HTML version deleted]]

Hi All, I have a problem with weighted logistic regression. I have a number of SNPs and a case/control scenario, but not all genotypes are as "guaranteed" as others, so I am using weights to downsample the importance of individuals whose genotype has been heavily "inferred". My data is quite big, but with a dummy example: > status <- c(1,1,1,0,0) > SNPs <-

Dear R developers: I am a PHD candidate student in the school of public health of Peking University and my major is genetic epidemiology. I am learning the FAM-MDR algorithm, which is used to detect the gene-gene and gene-environment interactions in the data of pedigree. The codes were written by Tom Cattaert of the University of Liege. The algorithms and the sample datasets are available at

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

This will probably seem very simple to experienced R programmers: I am doing a snp association analysis and am at the model-fitting stage. I am using the Stats package's "drop1" with the following code: ##geno is the dataset ## the dependent variable (casectrln) is dichotomous and coded 0,1 ## rs743572_2 is one of the snps (which is coded 0,1,2 for the 3 genotypes)

Hello, I did a pca with over 200000 snps for 340 observations (ids). If I plot the eigenvectors (called rotation in prcomp) 2,3 and 4 (e.g. plot (rotation[,2]) I see a strange "column" in my data (see attachment). I suggest it is an artefact (but of what?). Suggestion: I used prcomp this way: prcomp (mat), where mat is a matrix with the column means already substracted followed by a

Does anyone use the R library GenABEL? I am using it to calculate SNP interactions. I have a list of 100 SNPs, I need to look at the interaction between each of two SNPs among the list. my question is how to perform this in GenABEL. I want to use the "lm" function, but don't know how to use the SNP information. for example: result <- (lm(y~SNP1+SNP2+SNP1*SNP2)) the problem here

Hi R-help folks, I have been doing some single SNP association work using snpMatrix. This works well, but produces a lot of false positives, because of population structure in my data. I would like to correct the p-values (which snpMatrix gives me) for population structure, possibly using principle component analysis (PCA). My data is complicated, so here's a simple example of what

I use R on my Ubuntu 9.04 laptop, which was installed by "aptitude install" way Now i'm learning a book of R which needs "MASS" and "DAAG" installed. So i followed the instructions: >install.packages("MASS") >library("MASS") MASS works fine. But DAAG doesn't. Anyone who could help would be appreciated a lot ! Below are my error

I use R on my Ubuntu 9.04 laptop, which was installed by "aptitude install" way Now i'm learning a book of R which needs "MASS" and "DAAG" installed. So i followed the instructions: >install.packages("MASS") >library("MASS") MASS works fine. But DAAG doesn't. Anyone who could help would be appreciated a lot ! Below are my error

Bottom Line Up Front: How does one reshape genetic data from long to wide? I currently have a lot of data. About 180 individuals (some probands/patients, some parents, rare siblings) and SNP data from 6000 loci on each. The standard formats seem to be something along the lines of Famid, pid, fatid, motid, affected, sex, locus1Allele1, locus1Allele2, locus2Allele1, locus2Allele2, etc In other

Dear R users, I'm a newbie for R and want to ask some basic questions. So, after I open the R software, I typed library(DAAG). Then, I get massive warning messages as shown below. Why does it happen? Also, here are few specific questions regarding each message. 1) Loading required package: MASS -> Does this mean that the MASS package is not included in DAAG? 2) Attaching package:

Hi all, I am running Stepwise logistic regression and i have : 1- Multiple covatiates included in each model (No missing data) 2- Genotype data (SNPs) about 500,000 . I partitioned the data to multiple files (there are missing data) I run the step by including all the covariates and one SNP at each model. but i got this message : number of rows in use has changed: remove missing values? In

Dear All, I am trying to calculate the Hardy-Weinberg Equilibrium p-value for 42 SNPs. I am using the function HWE.exact from the package "genetics". In order not to do a lot of coding "by hand", I have a for loop that goes through each column (each column is one SNP) and gives me the p.value for HWE.exact. Unfortunately some SNP have reached fixation and HWE.exact requires a

Hi, The program below work very well. (snps = c('rs621782_G', 'rs8087639_G', 'rs8094221_T', 'rs7227515_A', 'rs537202_C')) Selec = todos[ , colnames(todos) %in% snps] head(Selec) But, I have a data set with 1.000 columns and I need extract 70 to use (like snps in command above). This 70 snps are in a file. So I create a file to extract them with

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I have a solution (actually a few) to this problem, but none are computationally efficient enough to be useful. I'm hoping someone can enlighten me to a better solution. I have data frame of chromosome/position pairs (along with other data for the location). For each pair I need to determine if it is with in a given data frame of ranges. I need to keep only the pairs that are within any of