#' Extract estimates of total run abundance by age from Jags output
#' @param samples (data.frame) output of clean_chains.
#' @param alpha (numeric) desired significance level for credible intervals.
#' @param file_path (character) file path to store output xlsx file.
#' @export
extract_abundance_by_age <- function(samples, alpha, file_path, final_year=2020){
aba_samples <- samples[,substr(names(samples), 1, 3)=="N_1" &
substr(names(samples), 4, 4)!="_"]
quants <- apply(aba_samples, MARGIN=2, quantile, probs = c(0.5, alpha/2, 1-alpha/2) )
med_array <- lb_array <- ub_array <- array(NA, dim=c(length(1994:final_year), 2, 6))
l <- 1
for(k in 1:6){
for(j in 1:2){
for(i in 1:length(1994:final_year)){
med_array[i, j, k] <- round(quants[1,l], 0)
lb_array[i, j, k] <- round(quants[2,l], 0)
ub_array[i, j, k] <- round(quants[3,l], 0)
l <- l + 1
}
}
}
chena_tab <- salcha_tab <- data.frame(
year=1994:final_year,
age_3=rep(NA, length(1994:final_year)),
age_4=rep(NA, length(1994:final_year)),
age_5=rep(NA, length(1994:final_year)),
age_6=rep(NA, length(1994:final_year)),
age_7=rep(NA, length(1994:final_year)),
age_8=rep(NA, length(1994:final_year))
)
for (i in 1:length(1994:final_year)){
for (k in 1:6){
chena_tab[i, k+1] <- paste(med_array[i, 1, k], " (", lb_array[i, 1, k], ", ", ub_array[i, 1, k], ")", sep="")
salcha_tab[i, k+1] <- paste(med_array[i, 2, k], " (", lb_array[i, 2, k], ", ", ub_array[i, 2, k], ")", sep="")
}
}
writexl::write_xlsx(list(chena=chena_tab, salcha=salcha_tab), path=file_path)
}
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