#' Computation of HBD probabilities
#'
#' This function is used to compute HBD probabilities on individuals in a sample
#'
#' @param atlas A atlas object
#' @param w.id The indices of individals for which HBD probabilities are computed
#' @details This function iterates over the slots submaps_list of the atlas object.
#' @details For each submaps in the slots submaps_list of the object, the slot HBD.prob will be filled with a matrix of dimension : number_individual x number_of_markers
#'
#' @return the atlas object with each HBD.prob slot of each submaps in the slot submaps_list computed
#'
#' @seealso setFLOD
#' @seealso setHFLOD
#'
#' @export
setHBDProb <- function(atlas, w.id)
{
if(class(atlas@submaps_list[[1]])[1] != "snpsMatrix" & class(atlas@submaps_list[[1]])[1] != "HostspotsMatrix")
stop("need either an hotspots.segments list of submaps or a snpsSegments list of submaps.")
if(class(atlas@bedmatrix)[1] != "bed.matrix")
stop("Need a bed.matrix.")
id <- as.vector(atlas@submap_summary$id[w.id])
famid <- as.vector(atlas@submap_summary$famid[w.id])
for(i in seq_along(atlas@submaps_list)) {
HBD_prob <- matrix(NA, nrow = length(w.id), ncol = atlas@submaps_list[[i]]@ncol)#HBD matrix
# dimnames(HBD_prob) <- list(rownames(HBD_prob) <- paste(famid,id, sep = "_"), colnames(HBD_prob) <- c(atlas@submaps_list[[i]]@map$id))
dimnames(HBD_prob) <- list( uniqueIds(famid,id), atlas@submaps_list[[i]]@map$id)
for (j in seq_len(nrow(HBD_prob))) {
j1 <- w.id[j]
if(!is.na(atlas@submaps_list[[i]]@a[j1]) & (atlas@submaps_list[[i]]@a[j1] <= 1) & !is.na(atlas@submaps_list[[i]]@f[j1])) {
HBD_prob[j,seq_len(ncol(HBD_prob))] <-forwardBackward(getLogEmiss(atlas@submaps_list[[i]], j1),
atlas@submaps_list[[i]]@delta.dist,
atlas@submaps_list[[i]]@a[j1],
atlas@submaps_list[[i]]@f[j1] )[2,]
}
}
HBD_prob[!is.finite(HBD_prob)] <- 0
atlas@submaps_list[[i]]@HBD.prob <- HBD_prob
}
atlas
}
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