
These functions produce (parametric) bootstrap estimates of the IBD coefficients between two individuals. Both kappa coefficients (for noninbred individuals) and the 9 condensed identity coefficients are supported.
kappaBootstrap(kappa, N, freqList, plot = TRUE)deltaBootstrap(delta, N, freqList)
A probability vector of length 3 (kappa) or 9 (delta): The coefficients under which simulations are performed.
The number of simulations.
A list of probability vectors: The allele frequencies for each marker.
A logical. If TRUE, the bootstrap kappa estimates are plotted in the IBD triangle.
A data frame with N
rows containing the bootstrap estimates. The
last column (dist
) gives the euclidean distance to the input, viewed as a
point in R^3 (kappa) or R^9 (delta).
In each replication, profiles for two individuals are simulated under the
input coefficients, and their relationship is re-estimated with ibdEstim()
.
# NOT RUN {
# Frequency list of 15 standard STR markers
freqList = NorwegianFrequencies[1:15]
# Number of bootstrap simulations (increase!)
N = 5
# Bootstrap estimates for kappa of full siblings
boot1 = kappaBootstrap(c(0.25, .5, .25), N = N, freqList = freqList)
boot1
# Mean deviation
mean(boot1$dist)
# Same, but with the 9 identity coefficients.
delta = c(0, 0, 0, 0, 0, 0, .25, .5, .25)
boot2 = deltaBootstrap(delta, N = N, freqList = freqList)
# Mean deviation
mean(boot2$dist)
# }
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