gwrpv: Genome-Wide Regression P-Value (gwrpv) in R
In mfjoneill/gwrpvr: Genome-Wide Regression P-Value (Gwrpv)

Description Usage Arguments Value Examples

View source: R/gwrpv.R

Computes the sample probability value (p-value) for the estimated coefficient from a standard genome-wide univariate regression. It computes the exact finite-sample p-value under the assumption that the measured phenotype (the dependent variable in the regression) has a known Bernoulli-normal mixture distribution.

1 2	gwrpv(beta, n0, n1, n2, mua, siga, mub, sigb, pa, pb, logdelta = -16, lognearnorm = -5, logtopsum = 8)

`beta`	the beta being tested
`n0`	number of major allele homozygotes
`n1`	number of major allele heterozygotes
`n2`	number of minor allele zygotes
`mua`	parameter of the mixture distribution, can be any real number
`siga`	parameter of the mixture distribution, can be any real number
`mub`	parameter of the mixture distribution, can be any real number
`sigb`	parameter of the mixture distribution, can be any real number
`pa`	parameter of the mixture distribution, a real number between zero and one with pa+pb=1
`pb`	parameter of the mixture distribution, a real number between zero and one with pa+pb=1
`logdelta`	must be in log base 10 format, with default value set to -16
`lognearnorm`	must be in log base 10 format, with default value set to -5
`logtopsum`	must be in log base 10 format, with default value set to 8

gwrpv returns a list containing:

$pvalue: p-value of a two-sided hypothesis test for a true coefficient of zero
$skew: skewness
$kurt: kurtosis of the coefficient estimate under assumed model
$skiptype: type of trimming/skip which took place (zero means no trimming)
$totnobs: total number of observations
$loopruns: number of sums in the main computation for each regression case

.

beta <- 6.05879
n0 <- 499
n1 <- 1
n2 <- 0
mua <- 13.87226
siga <- 2.58807
mub <- 4.62829
sigb <- 2.51803
pa <- 0.96544
pb <- 0.03456    # alternatively: pb <- 1.0 - pa
gwrpv(beta,n0,n1,n2,mua,siga,mub,sigb,pa,pb)

# note default values have been used for the trim parameters above
# in the following example we explicitly set the trim parameters
#
g <- gwrpv(beta,n0,n1,n2,mua,siga,mub,sigb,pa,pb,logdelta=-16,lognearnorm=-5,logtopsum=8)
g$pvalue