HWE.Chisq {mixIndependR}

Test the Hardy Weinberg Equilibrium with Chi-square test####

Description

Test the Hardy Weinberg Equilibrium with Chi-square test####

Usage

HWE.Chisq(G,G0,rescale.p=FALSE,simulate.p.value=TRUE,B=2000)

Arguments

G

a dataframe of observed genotype frequencies. Each row denotes each genotype; each column denotes each marker. The order of markers follows x; the genotypes are ordered by: from 1:l-th column, the genotypes are homozygous in order as : p1p1, p2p2,p3p3,...,plpl;from ll-th to u-th column, the genotypes are heterozygous in order as:choose(l,2) like: p1p2,p1p3,...,p1pl,p2p3,p2p4,...p2pl,...p(l-1)pl

G0

a dataframe of expected genotype probabilities;each row denotes each genotype; each column denotes each loci. The order of markers follows x; the genotypes are ordered by: from 1:l-th column, the genotypes are homozygous in order as : p1p1, p2p2,p3p3,...,plpl;from ll-th to u-th column, the genotypes are heterozygous in order as:choose(l,2) like: p1p2,p1p3,...,p1pl,p2p3,p2p4,...p2pl,...p(l-1)pl

rescale.p

a logical scalar; if TRUE then p is rescaled (if necessary) to sum to 1. If rescale.p is FALSE, and p does not sum to 1, an error is given.

simulate.p.value

a logical indicating whether to compute p-values by Monte Carlo simulation.

B

an integer specifying the number of replicates used in the Monte Carlo test.

Details

This function check the Hardy Weinberg Equilibrium from observed and expected distribution with Chi-square test#####

Value

a vector of result of p-values for chi-square test; the orders of markers follows x.

Examples

require(mixIndependR)
x <- data.frame(STR1=c("11|12","12|13","11|13","13|15"),
               STR2=c("12|12","13|14","13|13","14|15"),
               SNP1=c("A|T","A|A","T|A","A|T"),
               SNP2=c("A|A","T|T","A|T","T|A"))
G <- GenotypeFreq(x,expect = FALSE)
G0 <- GenotypeFreq(x,expect = TRUE)
HWE.Chisq(G,G0,rescale.p=FALSE,simulate.p.value=TRUE,B=2000)