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genomic control
gcontrol(
data,
zeta = 1000,
kappa = 4,
tau2 = 1,
epsilon = 0.01,
ngib = 500,
burn = 50,
idum = 2348
)
The returned value is a list containing:
deltot the probability of being an outlier.
x2 the
A the A vector.
the data matrix.
program constant with default value 1000.
multiplier in prior for mean with default value 4.
multiplier in prior for variance with default value 1.
prior probability of marker association with default value 0.01.
number of Gibbs steps, with default value 500.
number of burn-ins with default value 50.
seed for pseudorandom number sequence.
Bobby Jones, Jing Hua Zhao
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 |
burn | number of cycles for the Gibbs sampler to 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.
devlin99gap
if (FALSE) {
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)
}
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