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#### Get accuracy with the ancestries matrix that has errors in it.
#
# Inputs:
#
# (1) Error_rate: P(We change a true ancestry value)
#
# (2) anc_spec_freqs:
# [p0, p1, p2] scenarios x 3 ancestry-specific allele frequencies matrix
#
# (3) ancestries_matrix_true:
# 1, ..., 2*(number of individuals) alleles
# x 1, ..., (number of markers) matrix of true ancestries (0,1,or 2)
#
# Outputs:
#
# (1) For each combo of true ancestral allele frequencies [p0, p1, p2],
# give summary stats (mean and SD) of errors over markers
# in estimating each of
# P(Allele 1 | Anc 0), P(Allele 1 | Anc 1), P(Allele 1 | Anc 2).
#
# Output is a matrix with:
# Rows: P(Allele 1 | Anc 0), P(Allele 1 | Anc 1), P(Allele 1 | Anc 2).
# Cols: (p0, p1, p2, mean of errors over markers, sd of errors over markers).
get_results_error <- function(error_rate,
anc_spec_freqs,
ancestries_matrix_true){
# Introduce ancestry errors.
ancestries_error_matrix <- matrix(mapply(FUN = function(anc){
suppressMessages(change_ancestry(anc, error_rate))
}, ancestries_matrix_true),
nrow = nrow(ancestries_matrix_true),
ncol = ncol(ancestries_matrix_true))
out <- matrix(NA, nrow = 3 * nrow(anc_spec_freqs), ncol = 5)
# For each [p0, p1, p2] scenario, get the table of error summary stats.
# 3 rows per scenario:
# P(Allele 1 | Afr), P(Allele 1 | Eur), P(Allele 1 | NA).
# Columns: P(Allele 1 in Ancestral Pop 0), P(Allele 1 in Ancestral Pop 1),
# P(Allele 1 in Ancestral Pop 2), Mean, SD.
for(row in 1:nrow(anc_spec_freqs)){
# 3 x 5 matrix
summary_stats <- get_scenario_errors(row = row,
anc_spec_freqs = anc_spec_freqs,
ancestries_matrix_true = ancestries_matrix_true,
ancestries_matrix_estimated = ancestries_error_matrix)
out[(row - 1) * 3 + 1:3, ] <- summary_stats
}
colnames(out) <- c("p0", "p1", "p2", "Mean", "SD")
row.names(out) <- replicate(n = nrow(anc_spec_freqs),
expr = c("Abs_Error_Afr_Freq",
"Abs_Error_Eur_Freq",
"Abs_Error_NA_Freq"))
return(out)
}
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