225 lines
7.4 KiB
R
225 lines
7.4 KiB
R
#' @title Write Mixture Info Pop
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#' @description Writes information about the pop mixture
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#' @param logml logml
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#' @param rows rows
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#' @param data data
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#' @param adjprior adjprior
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#' @param priorTerm priorTerm
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#' @param outPutFile outPutFile
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#' @param inputFile inputFile
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#' @param partitionSummary partitionSummary
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#' @param popnames popnames
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#' @param fixedK fixedK
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#' @export
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writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm,
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outPutFile, inputFile, partitionSummary,
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popnames, fixedK) {
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ninds <- size(rows, 1)
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npops <- size(COUNTS, 3)
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names <- size(popnames, 1) == ninds # Tarkistetaan ett?nimet viittaavat yksilöihin
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changesInLogml <- vector()
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if (!missing(outPutFile)) {
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fid <- vector()
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}
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cat("RESULTS OF GROUP LEVEL MIXTURE ANALYSIS:\n")
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cat("Data file:", inputFile, "\n")
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cat("Number of clustered groups:", ownNum2Str(ninds), "\n")
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cat("Number of clusters in optimal partition:", ownNum2Str(npops), "\n")
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cat("Log(marginal likelihood) of optimal partition:", ownNum2Str(logml), "\n")
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if (exists("fid")) {
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append(fid, "RESULTS OF GROUP LEVEL MIXTURE ANALYSIS:\n")
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append(fid, c("Data file:", inputFile, "\n"))
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append(fid, c("Number of clustered groups:", ownNum2Str(ninds), "\n"))
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append(fid, c("Number of clusters in optimal partition:", ownNum2Str(npops), "\n"))
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append(fid, c("Log(marginal likelihood) of optimal partition:", ownNum2Str(logml), "\n\n"))
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}
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cluster_count <- length(unique(PARTITION))
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cat("Best Partition:\n")
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if (exists("fid")) {
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append(fid, c("Best partition:\n"))
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}
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for (m in 1:cluster_count) {
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indsInM <- find(PARTITION == m)
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length_of_beginning <- 11 + floor(log10(m))
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cluster_size <- length(indsInM)
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if (names) {
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text <- c("Cluster ", as.character(m), ": {", as.character(popnames[indsInM[1]]))
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for (k in 2:cluster_size) {
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text <- c(text, ", ", as.character(popnames[[indsInM[k]]]))
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}
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} else {
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text <- c("Cluster ", as.character(m), ": {", as.character(indsInM[1]))
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for (k in 2:cluster_size) {
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text <- c(text, ", ", as.character(indsInM[k]))
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}
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}
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text <- c(text, "}")
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while (length(text) > 58) {
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# Take one line and display it.
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new_line <- takeLine(text, 58)
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text <- text[(length(new_line) + 1):length(text)]
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cat(new_line, "\n")
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if (exists("fid")) {
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append(fid, c(new_line, "\n"))
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}
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if (length(text) > 0) {
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text <- c(blanks(length_of_beginning), text)
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} else {
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text <- vector()
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}
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}
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if (!is.null(text)) {
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cat(text, "\n")
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if (exists(fid)) {
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append(fid, c("\n", text, "\n"))
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}
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}
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}
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if (npops > 1) {
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cat("\n\nChanges in log(marginal likelihood) if (group i is moved to cluster j:")
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if (exists("fid")) {
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append(fid, " \n \n", )
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append(fid, "Changes in log(marginal likelihood) if (group i is moved to cluster j:")
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}
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if (names) {
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nameSizes <- zeros(ninds, 1)
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for (i in 1:ninds) {
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nimi <- as.character(popnames[i])
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nameSizes[i] <- length(nimi)
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}
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maxSize <- max(nameSizes)
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maxSize <- max(maxSize, 5)
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erotus <- maxSize - 5
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alku <- blanks(erotus)
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ekarivi <- c(alku, "group", blanks(6 + erotus))
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} else {
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ekarivi <- "group "
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}
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for (i in 1:cluster_count) {
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ekarivi <- c(ekarivi, ownNum2Str(i), blanks(8 - floor(log10(i))))
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}
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cat(ekarivi, "\n")
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if (exists("fid")) {
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append(fid, c(ekarivi, "\n"))
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}
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changesInLogml <- t(LOGDIFF)
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for (ind in 1:ninds) {
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muutokset <- changesInLogml[, ind]
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if (names) {
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nimi <- as.character(popnames[ind])
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rivi <- c(blanks(maxSize - length(nimi)), nimi, ":")
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} else {
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rivi <- c(blanks(4 - floor(log10(ind))), ownNum2Str(ind), ":")
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}
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for (j in 1:npops) {
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rivi <- c(rivi, " ", logml2String(omaRound(muutokset(j))))
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}
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cat(rivi, "\n")
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if (exists("fid")) {
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append(fid, c(rivi, "\n"))
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}
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}
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cat(" ")
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cat("KL-divergence matrix in PHYLIP format:")
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dist_mat <- zeros(npops, npops)
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if (exists("fid")) {
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append(fid, " \n")
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append(fid, " \n")
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append(fid, "KL - divergence matrix in PHYLIP format:\n")
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}
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maxnoalle <- size(COUNTS, 1)
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nloci <- size(COUNTS, 2)
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d <- zeros(maxnoalle, nloci, npops)
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prior <- adjprior
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prior[find[prior == 1]] <- 0
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nollia <- find(all(prior == 0)) # Lokukset, joissa oli havaittu vain yht?alleelia.
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prior[1, nollia] <- 1
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for (pop1 in 1:npops) {
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d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) / repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, 1))
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}
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ekarivi <- as.character(npops)
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cat(ekarivi, "\n")
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if (exists("fid")) {
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append(fid, c(ekarivi, "\n"))
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}
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for (pop1 in 1:npops) {
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rivi <- c(blanks(2 - floor(log10(pop1))), as.character(pop1), " ")
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for (pop2 in 1:(pop1 - 1)) {
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dist1 <- d[, , pop1]
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dist2 <- d[, , pop2]
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div12 <- sum(sum(dist1 * log2((dist1 + 10^-10) / (dist2 + 10^-10)))) / nloci
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div21 <- sum(sum(dist2 * log2((dist2 + 10^-10) / (dist1 + 10^-10)))) / nloci
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div <- (div12 + div21) / 2
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dist_mat[pop1, pop2] <- div
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}
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}
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dist_mat <- dist_mat + t(dist_mat) # make it symmetric
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for (pop1 in 1:npops) {
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rivi <- c("Cluster_", as.character(pop1), " ")
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for (pop2 in 1:npops) {
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rivi <- c(rivi, kldiv2str(dist_mat(pop1, pop2)), " ")
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}
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cat(rivi)
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if (exists("fid")) {
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append(fid, c(rivi, "\n"))
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}
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}
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}
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cat(" \n \n \n")
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cat("List of sizes of 10 best visited partitions and corresponding log(ml) values\n")
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if (exists("fid")) {
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append(fid, " \n\n")
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append(fid, " \n\n")
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append(fid, " \n\n")
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append(fid, " \n\n")
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append(fid, "List of sizes of 10 best visited partitions and corresponding log(ml) values\n")
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}
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partitionSummary <- sortrows(partitionSummary, 2)
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partitionSummary <- partitionSummary[size(partitionSummary, 1):-1, ]
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partitionSummary <- partitionSummary[find(partitionSummary[, 2] > -1e49), ]
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if (size(partitionSummary, 1) > 10) {
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vikaPartitio <- 10
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} else {
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vikaPartitio <- size(partitionSummary, 1)
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}
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for (part in 1:vikaPartitio) {
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line <- c(as.character(partitionSummary[part, 1]), " ", as.character(partitionSummary[part, 2]))
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cat(line, "\n")
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if (exists("fid")) {
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append(fid, c(line, "\n"))
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}
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}
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if (!fixedK) {
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cat(" \n")
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cat(" \n")
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cat("Probabilities for number of clusters\n")
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if (exists("fid")) {
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append(fid, " \n\n")
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append(fid, " \n\n")
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append(fid, "Probabilities for number of clusters\n")
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}
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npopsTaulu <- unique(partitionSummary[, 1])
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len <- length(npopsTaulu)
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probs <- zeros(len, 1)
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partitionSummary[, 2] <- partitionSummary[, 2] - max(partitionSummary[, 2])
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sumtn <- sum(exp(partitionSummary[, 2]))
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for (i in 1:len) {
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npopstn <- sum(exp(partitionSummary(find(partitionSummary[, 1] == npopsTaulu(i)), 2)))
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probs[i] <- npopstn / sumtn
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}
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for (i in 1:len) {
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if (probs(i) > 1e-5) {
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line <- c(as.character(npopsTaulu(i)), " ", as.character(probs(i)))
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cat(line)
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if (exists("fid")) {
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append(fid, line)
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append(fid, "\n")
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}
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}
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}
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}
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if (exists("fid")) {
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save(fid, file = outPutFile)
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}
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return(changesInLogml)
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}
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