Part of Genetic Value Analysis
calcGUSE(alleles, threshold = 1L, byID = FALSE, pop = NULL)Dataframe rows: id, col: guSE
A single-column table of genome-uniqueness standard errors as percentages. Rownames are set to 'id' values that are part of the population.
dataframe containing an AlleleTable (the same input
calcGU takes): one integer column per gene-drop iteration,
followed by an id column and a parent column. Produced by
geneDrop().
an integer indicating the maximum number of copies of an allele that can be present in the population for it to be considered rare. Default is 1.
logical variable of length 1 that is passed through to
eventually be used by alleleFreq(), which calculates the count of each
allele in the provided vector. If byID is TRUE and ids are provided,
the function will only count the unique alleles for an individual
(homozygous alleles will be counted as 1).
character vector with animal IDs to consider as the population of interest, otherwise all animals will be considered. The default is NULL.
Genome uniqueness (calcGU) is a Monte Carlo estimate: it is the
average, over the gene-drop iterations, of the proportion of an animal's two
allele copies that are population-rare. Because it is an average over
independent simulated iterations, it carries sampling error that shrinks as
the number of iterations grows.
For animal i let m_ik = rare[i, k] / 2 be the per-iteration
value in iteration k (so the mean of m_ik over the K
iterations equals gu_i / 100). This function returns the exact
Monte Carlo standard error of that mean, on the same percentage scale as
calcGU:
$$guSE_i = 100 \times \sqrt{\frac{var(m_{i\cdot})}{K}}$$
The standard error is computed from the same per-iteration rare-allele matrix
(calcA) that calcGU averages, so it is correct
for any threshold / byID without a closed-form approximation.
An animal whose rare-allele count does not vary across iterations has a
standard error of 0.
calcGU, calcA, reportGV
Other genetic value analysis:
calcA(),
calcFE(),
calcFEFG(),
calcFG(),
calcFGSE(),
calcGU(),
calcGeneDiversity(),
calcNeSexRatio(),
calcNeVariance(),
calcRetention()
library(nprcgenekeepr)
ped1Alleles <- nprcgenekeepr::ped1Alleles
guSE <- calcGUSE(ped1Alleles, threshold = 3, byID = FALSE, pop = NULL)
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