HW.cond: Computing the fully conditional P-value for a given...

In HW.pval: Testing Hardy-Weinberg Equilibrium for Multiallelic Genes

Description

Computes the fully conditional P-Value associated to the provided lower-triangular array of genotype counts to be consistent with the Hardy-Weinberg equilibrium model

Usage

HW.cond(obs_dist, model_dist, rms, chisq, gsq, T, n)

Arguments

obs_dist	Observed genotype count matrix
model_dist	Hardy-Weinberg equilibrium model distribution for the observed genotype count matrix determined in HW.pval(). Calculated via the function create.model()
rms	Root-Mean-Square test statistic determined in HW.pval()
chisq	Chi-Square test statistic determined in HW.pval()
gsq	Log Likelihood-Ratio test statistic determined in HW.pval()
T	Number of Monte-Carlo simulations desired
n	Total number of observed genotypes

Details

Determines the fully-conditional P-value via Monte-Carlo simulation as described in Algorithm 5.2 of the referenced paper

Returns fully conditional P-values associated to the root-mean-square, chi-square, and log likelihood-ratio statistics.

Author(s)

Shuhodeep Mukherji <[e-mail]>

References

"Testing Hardy-Weinberg equilibrium with a simple root-mean-square statistic" by Rachel Ward.

See Also

HW.pval, create.model, test.rms, test.chisq, and test.gsq

HW.pval documentation built on May 2, 2019, 1:44 p.m.