Computing the fully conditional P-value for a given lower-triangular array of genotype counts

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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

1
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 <deep.mukherji@utexas.edu>

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