View source: R/overdispersion.R
TestOverdispersion | R Documentation |
This function is intended to help the user select a value to pass to the
overdispersion
argument of AddGenotypeLikelihood
,
generally via pipeline functions such as IterateHWE
or
PipelineMapping2Parents
.
TestOverdispersion(object, ...) ## S3 method for class 'RADdata' TestOverdispersion(object, to_test = seq(6, 20, by = 2), ...)
object |
A |
to_test |
A vector containing values to test. These are values that will potentially
be used for the |
... |
Additional arguments (none implemented). |
If no genotype calling has been performed, a single iteration under HWE using
default parameters will be done. object$ploidyChiSq
is then examined
to determine the most common/most likely inheritance mode for the whole
dataset. The alleles that are examined are only those where this
inheritance mode has the lowest chi-squared value.
Within this inheritance mode and allele set, genotypes are selected where the
posterior probability of having a single copy of the allele is at least 0.95.
Read depth for these genotypes is then analyzed. For each genotype, a
two-tailed probability is calculated for the read depth ratio to deviate from
the expected ratio by at least that much under the beta-binomial distribution.
This test is performed for each overdispersion value provided in
to_test
.
A list of the same length as to_test
plus one. The names of the list are
to_test
converted to a character vector. Each item in the list is a
vector of p-values, one per examined genotype, of the read depth ratio for
that genotype to deviate that much from the expected ratio. The last item,
named "optimal", is a single number indicating the optimal value for the
overdispersion parameter based on the p-value distributions. If the optimal
value was the minimum or maximum tested, NA
is returned in the
"optimal"
slot to encourage the user to test other values.
Lindsay V. Clark
# dataset with overdispersion data(Msi01genes) # test several values for the overdispersion parameter myP <- TestOverdispersion(Msi01genes, to_test = 8:10) # view results as quantiles sapply(myP[names(myP) != "optimal"], quantile, probs = c(0.01, 0.25, 0.5, 0.75, 0.99))
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