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Last data update: 2014.03.03

R: Samples in the posterior distribution of the frequencies and...

ASP.Bayesian

R Documentation

Samples in the posterior distribution of the frequencies and OR

Description

Samples using Metropolis-Hasting Algorithm in the posterior distribution of the four haplotype frequencies and OR

Usage

 ASP.Bayesian(N, Tem_Gen, Index_Gen, IBD, snp, thin = 1, sd.freq = 0.05,
                     sd.psi = 0.05, p0 = c(rep(1/4, 4), 1), psi.prior = 0)

Arguments

`N`	Number of Metropolis-Hastings iterations
`Tem_Gen`	Genotypes of controls (denoted by the number of alternative allele)
`Index_Gen`	Genotypes of index cases
`IBD`	IBD states for each affected sib-pair
`snp`	Names or number column of the SNP to consider
`thin`	Thinning parameter (keep only every thin-th draw)
`sd.freq`	Random walk standard deviation of the frequency logarithms
`sd.psi`	Random walk standard deviation of the OR
`p0`	The initial point of random walk
`psi.prior`	Precision of gaussian log(OR) prior (0 = improper flat prior)

Details

Samples using Metropolis-Hasting and likelihood defined by data. More precisely, give the frequency samples of haplotypes for observed SNP and unobserved causal SNP and give the sample of the odds ratio associated to the causal SNP.

Value

List of 5 vectors of length N/thin with components:

`f_ab`	Sample of the haplotype composed by the two alternative alleles
`f_Ab`	Sample of the haplotype composed by the reference allele for the causal (unobserved) locus and the alternative alleles for the observed locus
`f_aB`	Sample of the haplotype composed by the alternative allele for the causal (unobserved) locus and the reference alleles for the observed locus
`f_AB`	Sample of the haplotype composed by the two reference alleles
`OR`	Sample of the OR

Author(s)

Claire Dandine-Roulland

References

Dandine-Roulland, Claire and Perdry, Herve. Where is the causal variant? On the advantage of the family design over the case-control design in genetic association studies. Submitted to Eur J Hum Genet

Examples

data(ASPData)
B <- ASP.Bayesian(1e5, ASPData$Control, ASPData$Index,
                  ASPData$IBD, 15 )

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ASPBay)
Loading required package: hexbin
Loading required package: Rcpp
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ASPBay/ASP.Bayesian.Rd_%03d_medium.png", width=480, height=480)
> ### Name:  ASP.Bayesian 
> ### Title: Samples in the posterior distribution of the frequencies and OR
> ### Aliases: ' ASP.Bayesian '
> ### Keywords: Metropolis-Hasting Sampling
> 
> ### ** Examples
> 
> data(ASPData)
> B <- ASP.Bayesian(1e5, ASPData$Control, ASPData$Index,
+                   ASPData$IBD, 15 )
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>