gap (version 1.1-16)

gcontrol: genomic control

Description

The Bayesian genomic control statistics with the following parameters,

n

number of loci under consideration

lambdahat

median(of the n trend statistics)/0.46

Prior for noncentrality parameter Ai is

Normal(sqrt(lambdahat)kappa,lambdahat*tau2)

kappa

multiplier in prior above, set at 1.6 * sqrt(log(n))

tau2

multiplier in prior above

epsilon

prior probability a marker is associated, set at 10/n

ngib

number of cycles for the Gibbs sampler after burn in

Armitage's trend test along with the posterior probability that each marker is associated with the disorder is given. The latter is not a p-value but any value greater than 0.5 (pout) suggests association.

Usage

gcontrol(data,zeta,kappa,tau2,epsilon,ngib,burn,idum)

Arguments

data

the data matrix

zeta

program constant with default value 1000

kappa

multiplier in prior for mean with default value 4

tau2

multiplier in prior for variance with default value 1

epsilon

prior probability of marker association with default value 0.01

ngib

number of Gibbs steps, with default value 500

burn

number of burn-ins with default value 50

idum

seed for pseudorandom number sequence

Value

The returned value is a list containing:

deltot

the probability of being an outlier

x2

the $chi-squared$ statistic

A

the A vector

Source

http://www.stat.cmu.edu

References

Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997-1004

Examples

## Not run: 
# test<-c(1,2,3,4,5,6,  1,2,1,23,1,2, 100,1,2,12,1,1, 
#         1,2,3,4,5,61, 1,2,11,23,1,2, 10,11,2,12,1,11)
# test<-matrix(test,nrow=6,byrow=T)
# gcontrol(test)
# ## End(Not run)