gcontrol | R Documentation |
genomic control
gcontrol(
data,
zeta = 1000,
kappa = 4,
tau2 = 1,
epsilon = 0.01,
ngib = 500,
burn = 50,
idum = 2348
)
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. |
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.
The returned value is a list containing:
deltot the probability of being an outlier.
x2 the \chi^2
statistic.
A the A vector.
Adapted from gcontrol by Bobby Jones and Kathryn Roeder, use -Dexecutable for standalone program, function getnum in the original code needs \
Bobby Jones, Jing Hua Zhao
https://www.cmu.edu/dietrich/statistics-datascience/index.html
devlin99gap
## 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)
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