#
# Example 1
#
data(ACEdata)
# create the haplotype object
x <- haplodata(ACEdata)
# simulates a first sample of 100 haplotypes using all markers
y1 <- haplosim(100, x)
# compares allele frequencies in real and simulated samples
plot(x$freqs, y1$freqs, title=paste("MSE:",y1$mse.freqs)); abline(a=0, b=1)
# compares LD coefficients in real and simulated samples
ldplot(mergemats(x$cor, y1$cor), ld.type='r')
# simulates a second sample of 1000 haplotypes using the first 20 markers only
y2 <- haplosim(1000, which.snp=seq(20), x)
#
# Example 2
#
# simulate a sample of 500 haplotypes based on the ACE data set
set.seed(100)
data(ACEdata)
n <- 500
x <- haplodata(ACEdata)
y <- haplosim(n, x)
# compute the haplotype frequencies
# an haplotype starts at markers 17 and ends at marker 22
freq1 <- haplofreqs(ACEdata, 17, 22)
freq2 <- haplofreqs(y$data, 17, 22)
# extract the set of haplotypic configurations that are shared
# by real and simulated data and their frequencies
commonhapls <- intersect(names(freq1),names(freq2))
cfreq1 <- freq1[commonhapls]
cfreq2 <- freq2[commonhapls]
# compare real vs simulated haplotype frequencies
par(mar=c(10.1, 4.1, 4.1, 2.1), xpd=TRUE)
legend.text <- names(cfreq1)
bp <- barplot(cbind(cfreq1,cfreq2), main="Haplotype Frequencies",
names.arg=c("Real","Simulated"), col=heat.colors(length(legend.text)))
legend(mean(range(bp)), -0.3, legend.text, xjust = 0.5,
fill=heat.colors(length(legend.text)), horiz = TRUE)
chisq.test(x=n*cfreq2, p=cfreq1, simulate.p.value = TRUE, rescale.p = TRUE)
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