Test influence of samples on Hardy-Weinberg equilibrium via jackknife.
jackHWE(g, exclude.num = 1, min.hwe.samples = 5, show.progress = TRUE, ...)jackInfluential(jack.result, alpha = 0.05)
# S3 method for jack.influential
plot(x, main = NULL, ...)
.alleleFreqFormat(x, g)
Number of samples to exclude at a time.
minimum samples needed to calculate HWE.
logical. Show progress of jackknife?
other arguments to be passed to hweTest
.
result from run of jackHWE
.
critical value to determine if exclusion is "influential".
a matrix or data.frame where first column is sample id and second colum is locus name.
main title for influential sample plots from
plot.jack.influential
.
jackHWE
returns a list with:
a named vector of HWE p-values for each locus.
a data.frame
of HWE p-values where each row is an
exclusion and columns are loci.
the original gtypes
object.
a data.frame
of influential exclusions.
a data.frame
listing the allele frequencies of
influential exclusions.
a matrix
of odds ratios between exclusions (rows)
and loci (columns).
jackHWE
performs a HWE jackknife where all combinations
of exclude.num
samples are left out and HWE is recalculated
jackInfluential
calculates odds.ratios between jackknife
HWE and observed HWE and identifies "influential" samples. Samples
are "influential" if the observed HWE p-value is < alpha
, but
is > alpha
when the samples are not present
plot.jack.influential
creates a cumulative frequency plot
of all odds-ratios from jack.influential
. A vertical dashed
line marks the smallest influential exclusion
Morin, P.A., R.G. LeDuc, F.I. Archer, K.K. Martien, R. Huebinger, J.W. Bickham, and B.L. Taylor. 2009. Significant deviations from Hardy-Weinberg equilibirum caused by low levels of microsatellite genotyping errors. Molecular Ecology Resources 9:498-504.