gap (version 1.1-16)

hwe.hardy: Hardy-Weinberg equilibrium test using MCMC

Description

Hardy-Weinberg equilibrium test by MCMC

Usage

hwe.hardy(a, alleles = 3, seed = 3000, sample = c(1000, 1000, 5000))

Arguments

a

an array containing the genotype counts, as integer.

alleles

number of allele at the locus, greater than or equal to 3, as integer

seed

pseudo-random number seed, as integer.

sample

optional, parameters for MCMC containing number of chunks, size of a chunk and burn-in steps, as integer.

Value

The returned value is a list containing:

method

Hardy-Weinberg equilibrium test using MCMC

data.name

name of used data if x is given

p.value

Monte Carlo p value

p.value.se

standard error of Monte Carlo p value

switches

percentage of switches (partial, full and altogether)

Source

http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml,

References

Guo, S.-W. and E. A. Thompson (1992) Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics. 48:361--372.

See Also

hwe, HWE.test, genotype

Examples

  ## Not run: 
#     # example 2 from hwe.doc:
#     a<-c(
#     3,
#     4, 2,
#     2, 2, 2,
#     3, 3, 2, 1,
#     0, 1, 0, 0, 0,
#     0, 0, 0, 0, 0, 1,
#     0, 0, 1, 0, 0, 0, 0,
#     0, 0, 0, 2, 1, 0, 0, 0)
#     ex2 <- hwe.hardy(a=a,alleles=8)
# 
#     # example using HLA
#     data(hla)
#     x <- hla[,3:4]
#     y <- pgc(x,handle.miss=0,with.id=1)
#     n.alleles <- max(x,na.rm=TRUE)
#     z <- vector("numeric",n.alleles*(n.alleles+1)/2)
#     z[y$idsave] <- y$wt
#     hwe.hardy(a=z,alleles=n.alleles)
# 
#     # with use of class 'genotype'
#     # this is to be fixed
#     library(genetics)
#     hlagen <- genotype(a1=x$DQR.a1, a2=x$DQR.a2, 
#                        alleles=sort(unique(c(x$DQR.a1, x$DQR.a2))))
#     hwe.hardy(hlagen)
# 
#     # comparison with hwe
#     hwe(z,data.type="count")
# 
#     # to create input file for HARDY
#     print.tri<-function (xx,n) {
#         cat(n,"\n")
#         for(i in 1:n) {
#             for(j in 1:i) {
#                 cat(xx[i,j]," ")
#             }
#         cat("\n")
#         }
#         cat("100 170 1000\n")
#     }
#     xx<-matrix(0,n.alleles,n.alleles)
#     xxx<-lower.tri(xx,diag=TRUE)
#     xx[xxx]<-z
#     sink("z.dat")
#     print.tri(xx,n.alleles)
#     sink()
#     # now call as: hwe z.dat z.out
#   ## End(Not run)