SimulationStudy {FEST} | R Documentation |
Assume a given set of true and alternative family relationships. Use simulations and exact likelihood computations to compute likelihood and posterior values. These computations are done for different number of markers.
SimulationStudy(models, chr=c(1:22), nmarker = c(22 * c(1, 10, 100, 1000), 5e+05), nsim = c(1000, 1000, 1000, 1000, 400), maf = numeric(), frequencyData = NULL, freqThreshold = c(rep(0.1, 4), 0), saveMerlinFiles=FALSE, verbose=TRUE)
models |
Object of type Model that specifies true and alternative family
relations. |
chr |
Either a vector or a list. If chr is a vector it contains the chromosomes included in the simulation study. If it is list it should have same length as the nmarker vector. Each element of the list is a vector containing chromosomes included in the simulation study. |
nmarker |
Vector of number of markers. A simulation study is done for each number of markers. |
nsim |
Number of simulations. A vector with same length as the nmarker vector. |
maf |
Minor allele frequency. Same for all SNPs, must be
specified if frequencyData is not specified. |
frequencyData |
A list with frequency information for each chromosome. See
affy for a description of the format. |
freqThreshold |
Selects a sub set of the SNPs in
frequencyData : only SNPs with minor allele frequency >
freqThreshold are retained. Vector with same length as the
nmarker vector. |
saveMerlinFiles |
If TRUE the files used as input to the likelihood computations in merlin are saved. Default value is FALSE. |
verbose |
If TRUE, information about simulations are output to screen. Default TRUE. |
An object of type SimStudyObject-class
.
Øivind Skare oivind.skare@medisin.uio.no
http://folk.uio.no/thoree/FEST
Øivind Skare, Nuala Sheehan, and Thore Egeland Identification of distant family relationships Bioinformatics Advance Access published on July 6, 2009.
set.seed(17) models <- SetModels(trueModels=paste("HS-", 1:6, sep=""), altModels=c("true", "unrelated")) nsim <- rep(10, 2) nmarker <- 22*c(1, 10) chr <- c(1:22) simObj1 <- SimulationStudy(models, chr=chr, nmarker=nmarker, nsim=nsim, maf=0.5) stat1 <- ComputeSummaryStatistics(simObj1) ## Average posterior results for no of markers=22: print(round(stat1$posterior[1,,],4)) # rows: true models, columns: alternative models ### HS-1 HS-2 HS-3 HS-4 HS-5 HS-6 unrelated ### HS-1 0.5284 NA NA NA NA NA 0.4716 ### HS-2 NA 0.4821 NA NA NA NA 0.5179 ### HS-3 NA NA 0.4945 NA NA NA 0.5055 ### HS-4 NA NA NA 0.5004 NA NA 0.4996 ### HS-5 NA NA NA NA 0.5001 NA 0.4999 ### HS-6 NA NA NA NA NA 0.5 0.5000 ## No of markers=220: print(round(stat1$posterior[2,,],4)) ## Simulation study using Affymetrix frequency data ## The complete 500K data file may be downloaded from ## 'http://folk.uio.no/thoree/FEST/affy.RData' ## load("affy.RData") ## A small subset of the Affymetrix frequency data. 100 markers on ## each chromsome data(affy.subset) simObj2 <- SimulationStudy(models, chr=chr, nmarker=nmarker, nsim=nsim, frequencyData=affy.subset, freqThreshold=c(0.1,0.1)) stat2 <- ComputeSummaryStatistics(simObj2) ## Average posterior results (Affymetrix): ## No of markers=22: print(round(stat1$posterior[1,,],4)) # rows: true models, columns: alternative models ## No of markers=220: print(round(stat1$posterior[2,,],4))