Description Usage Arguments Value Examples
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 |
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:
p-value of a two-sided hypothesis test for a true coefficient of zero
skewness
kurtosis of the coefficient estimate under assumed model
type of trimming/skip which took place (zero means no trimming)
total number of observations
number of sums in the main computation for each regression case
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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
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