Fixed several linting issues
This commit is contained in:
Waldir Leoncio
parent
66d0f0c730
commit
835ac7e6b9
28 changed files with 180 additions and 175 deletions
39
R/admix1.R
39
R/admix1.R
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@ -21,8 +21,6 @@ admix1 <- function(tietue) {
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cat("---------------------------------------------------\n")
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message("Reading mixture result from: ", pathname_filename, "...")
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}
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Sys.sleep(0.0001) # TODO: remove
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# ASK: what is this supposed to do? What do graphic obj have to do here?
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# h0 = findobj('Tag','filename1_text');
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# set(h0,'String',filename); clear h0;
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@ -42,12 +40,10 @@ admix1 <- function(tietue) {
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if (isfield(c, "gene_lengths") &
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strcmp(c$mixtureType, "linear_mix") |
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strcmp(c$mixtureType, "codon_mix")) { # if the mixture is from a linkage model
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# Redirect the call to the linkage admixture function.
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# call function noindex to remove the index column
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strcmp(c$mixtureType, "codon_mix")) {
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# if the mixture is from a linkage model Redirect the call to the linkage
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# admixture function. call function noindex to remove the index column
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c$data <- noIndex(c$data, c$noalle)
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# linkage_admix(c) # TODO: obsolete. remove.
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# return
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stop("linkage_admix not implemented")
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}
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PARTITION <- c$PARTITION
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@ -148,7 +144,8 @@ admix1 <- function(tietue) {
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}
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}
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# Analyze further only individuals who have log-likelihood ratio larger than 3:
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# Analyze further only individuals who have log-likelihood ratio larger than
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# 3
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to_investigate <- t(matlab2r::find(likelihood > 3))
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cat("Possibly admixed individuals:\n")
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for (i in 1:length(to_investigate)) {
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@ -229,9 +226,15 @@ admix1 <- function(tietue) {
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# Initialize the data structures, which are required in taking the missing
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# data into account:
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n_missing_levels <- zeros(npops, 1) # number of different levels of "missingness" in each pop (max 3).
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missing_levels <- zeros(npops, 3) # the mean values for different levels.
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missing_level_partition <- zeros(ninds, 1) # level of each individual (one of the levels of its population).
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# number of different levels of "missingness" in each pop (max 3).
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n_missing_levels <- zeros(npops, 1)
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# the mean values for different levels.
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missing_levels <- zeros(npops, 3)
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# level of each individual (one of the levels of its population).
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missing_level_partition <- zeros(ninds, 1)
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for (i in 1:npops) {
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inds <- matlab2r::find(PARTITION == i)
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# Proportions of non-missing data for the individuals:
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@ -239,7 +242,9 @@ admix1 <- function(tietue) {
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for (j in 1:length(inds)) {
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ind <- inds[j]
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non_missing_data[j] <- length(
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matlab2r::find(data[(ind - 1) * rowsFromInd + 1:ind * rowsFromInd, ] > 0)
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matlab2r::find(
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data[(ind - 1) * rowsFromInd + 1:ind * rowsFromInd, ] > 0
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)
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) / (rowsFromInd * nloci)
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}
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if (all(non_missing_data > 0.9)) {
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@ -247,8 +252,6 @@ admix1 <- function(tietue) {
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missing_levels[i, 1] <- mean(non_missing_data)
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missing_level_partition[inds] <- 1
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} else {
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# TODO: fix syntax
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# [ordered, ordering] = sort(non_missing_data);
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ordered <- ordering <- sort(non_missing_data)
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# part = learn_simple_partition(ordered, 0.05);
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part <- learn_partition_modified(ordered)
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@ -258,7 +261,9 @@ admix1 <- function(tietue) {
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n_levels <- length(unique(part))
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n_missing_levels[i] <- n_levels
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for (j in 1:n_levels) {
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missing_levels[i, j] <- mean(non_missing_data[matlab2r::find(part == j)])
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missing_levels[i, j] <- mean(
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non_missing_data[matlab2r::find(part == j)]
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)
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}
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}
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}
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@ -369,8 +374,8 @@ admix1 <- function(tietue) {
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}
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}
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tulostaAdmixtureTiedot(proportionsIt, uskottavuus, alaRaja, iterationCount) # TODO: textual outputs. probably not necessary. translate nonetheless
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viewPartition(proportionsIt, popnames) # TODO: adapt
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tulostaAdmixtureTiedot(proportionsIt, uskottavuus, alaRaja, iterationCount)
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viewPartition(proportionsIt, popnames)
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talle <- inputdlg("Do you want to save the admixture results? [Y/n]", "y")
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if (talle %in% c("y", "Y", "yes", "Yes")) {
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@ -17,7 +17,8 @@ cluster_own <- function(Z, nclust) {
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if (i <= m) { # original node, no leafs
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T[i] <- clsnum
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clsnum <- clsnum + 1
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} else if (i < (2 * m - maxclust + 1)) { # created before cutoff, search down the tree
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} else if (i < (2 * m - maxclust + 1)) {
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# created before cutoff, search down the tree
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T <- clusternum(Z, T, i - m, clsnum)
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clsnum <- clsnum + 1
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}
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@ -25,7 +26,8 @@ cluster_own <- function(Z, nclust) {
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if (i <= m) { # original node, no leafs
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T[i] <- clsnum
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clsnum <- clsnum + 1
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} else if (i < (2 * m - maxclust + 1)) { # created before cutoff, search down the tree
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} else if (i < (2 * m - maxclust + 1)) {
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# created before cutoff, search down the tree
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T <- clusternum(Z, T, i - m, clsnum)
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clsnum <- clsnum + 1
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}
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@ -4,7 +4,9 @@ computePopulationLogml <- function(pops, adjprior, priorTerm) {
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# ======================================================== #
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# Limiting COUNTS size #
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# ======================================================== #
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COUNTS <- COUNTS[seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops, drop = FALSE]
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COUNTS <- COUNTS[
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seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops, drop = FALSE
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]
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x <- size(COUNTS, 1)
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y <- size(COUNTS, 2)
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@ -14,14 +16,16 @@ computePopulationLogml <- function(pops, adjprior, priorTerm) {
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# Computation #
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# ======================================================== #
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isarray <- length(dim(repmat(adjprior, c(1, 1, length(pops))))) > 2
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# FIXME: 3rd dimension of COUNTS getting dropped
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term1 <- squeeze(
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sum(
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sum(
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reshape(
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lgamma(
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repmat(adjprior, c(1, 1, length(pops))) +
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COUNTS[seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops, drop = !isarray]
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COUNTS[
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seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops,
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drop = !isarray
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]
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),
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c(x, y, z)
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),
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@ -11,7 +11,7 @@ etsiParas <- function(osuus, osuusTaulu, omaFreqs, logml) {
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muutokset <- laskeMuutokset4(osuus, osuusTaulu, omaFreqs, logml)
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# Work around R's base::max() limitation on complex numbers
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if (any(sapply(muutokset, class) == "complex")) {
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if (any(vapply(muutokset, class, vector("character", 1)) == "complex")) {
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maxRe <- base::max(Re(as.vector(muutokset)))
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maxIm <- base::max(Im(as.vector(muutokset)))
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maxMuutos <- complex(real = maxRe, imaginary = maxIm)
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@ -1,7 +1,9 @@
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#' @title Read line from file, removing newline characters
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#' @description Equivalent function to its homonymous Matlab equivalent.
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#' @param file character vector to be read, usually an output of `fopen()`
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#' @return If the file is nonempty, then fgetl returns tline as a character vector. If the file is empty and contains only the end-of-file marker, then fgetl returns tline as a numeric value -1.
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#' @return If the file is nonempty, then fgetl returns tline as a character
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#' vector. If the file is empty and contains only the end-of-file marker, then
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#' fgetl returns tline as a numeric value -1.
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#' @author Waldir Leoncio
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#' @seealso fopen
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#' @export
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@ -13,5 +13,6 @@ fiksaaPartitioYksiloTasolle <- function(rows, rowsFromInd) {
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partitio2[rivi / rowsFromInd] <- PARTITION[ind]
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}
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}
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PARTITION <<- partitio2
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global_env <- as.environment(1L)
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assign("PARTITION", partitio2, envir = global_env)
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}
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@ -1,17 +1,17 @@
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findOutRowsFromInd <- function(popnames, rows, ploidisuus = NULL) {
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if (is.null(ploidisuus)) {
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ploidisuus <- questdlg(
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quest = 'Specify the type of individuals in the data',
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dlgtitle = 'Individual type?',
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btn = c('Haploid', 'Diploid', 'Tetraploid'),
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defbtn = 'Diploid'
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quest = "Specify the type of individuals in the data",
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dlgtitle = "Individual type?",
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btn = c("Haploid", "Diploid", "Tetraploid"),
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defbtn = "Diploid"
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)
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}
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rowsFromInd <- switch(ploidisuus,
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'Haploid' = 1,
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'Diploid' = 2,
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'Tetraploid' = 4
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"Haploid" = 1,
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"Diploid" = 2,
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"Tetraploid" = 4
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)
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popnames2 <- popnames * NA
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@ -22,5 +22,5 @@ findOutRowsFromInd <- function(popnames, rows, ploidisuus = NULL) {
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popnames2[i, 2] <- rivi[rowsFromInd] / rowsFromInd
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}
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}
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return(list(popnames2 = popnames2, rowsFromInd = rowsFromInd))
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return(list(popnames2 = popnames2, rowsFromInd = rowsFromInd))
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}
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@ -1,11 +1,12 @@
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getDistances <- function(data_matrix, nclusters) {
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# %finds initial admixture clustering solution with nclusters clusters, uses simple mean Hamming distance
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# %gives partition in 8 - bit format
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# %allocates all alleles of a single individual into the same basket
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# %data_matrix contains #Loci + 1 columns, last column indicate whose alleles are placed in each row,
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# %i.e. ranges from 1 to #individuals. For diploids there are 2 rows per individual, for haploids only a single row
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# %missing values are indicated by zeros in the partition and by negative integers in the data_matrix.
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# finds initial admixture clustering solution with nclusters clusters, uses
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# simple mean Hamming distance; gives partition in 8 - bit format; allocates
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# all alleles of a single individual into the same basket; data_matrix
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# contains #Loci + 1 columns, last column indicate whose alleles are placed in
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# each row, i.e. ranges from 1 to #individuals. For diploids there are 2 rows
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# per individual, for haploids only a single row; missing values are indicated
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# by zeros in the partition and by negative integers in the data_matrix.
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size_data <- size(data_matrix)
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nloci <- size_data[2] - 1
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@ -10,18 +10,25 @@ getPopDistancesByKL <- function(adjprior) {
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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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# Lokukset, joissa oli havaittu vain yht?alleelia.
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nollia <- find(all(prior == 0))
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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, ncol(prior)))
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d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) / repmat(
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sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, ncol(prior))
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)
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}
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pointer <- 1
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for (pop1 in 1:(npops - 1)) {
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for (pop2 in (pop1 + 1):npops) {
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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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div12 <- sum(sum(dist1 * log2((dist1 + 10^-10) / (dist2 + 10^-10)))) /
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nloci
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div21 <- sum(sum(dist2 * log2((dist2 + 10^-10) / (dist1 + 10^-10)))) /
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nloci
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div <- (div12 + div21) / 2
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distances[pointer] <- div
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pointer <- pointer + 1
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@ -46,5 +46,4 @@ greedyMix <- function(data, format, verbose = TRUE) {
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stop("Format not supported.")
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}
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return(out)
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# TODO: add handleData(out) or some other post-processing of data
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}
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@ -12,7 +12,8 @@
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#' @references Samtools: a suite of programs for interacting
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#' with high-throughput sequencing data. <http://www.htslib.org/>
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#' @export
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greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE) {
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greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE
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) {
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# Replacing original file reading code with greedyMix()
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rawdata <- greedyMix(data, format, verbose)
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@ -26,12 +27,12 @@ greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE)
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priorTerm <- data_greedyMix_handle$priorTerm
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rm(data_greedyMix_handle)
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Z_dist <- getPopDistancesByKL(adjprior)
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Z_dist$Z -> Z
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Z_dist$dist -> dist
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Z <- Z_dist$Z
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dist <- Z_dist$dist
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rm(Z_dist)
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a_data <- data[, 1:(ncol(data) - 1)]
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sumcounts_counts_logml <- initialPopCounts(a_data, npops, rows, noalle, adjprior)
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sumcounts_counts_logml$logml -> logml
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logml <- sumcounts_counts_logml$logml
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rm(sumcounts_counts_logml)
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c <- list()
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c$data <- data
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@ -76,17 +77,17 @@ greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE)
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NULL, NULL, partitionSummary, popnames, fixedK = FALSE
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)
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talle <- questdlg(
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'Do you want to save the mixture populations so that you can use them later in admixture analysis?',
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'Save results?', c('Yes', 'No'), 'Yes'
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"Do you want to save the mixture populations so that you can use them later in admixture analysis?",
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"Save results?", c("Yes", "No"), "Yes"
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)
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if (tolower(talle) == 'yes') {
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if (tolower(talle) == "yes") {
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waitALittle()
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filename_pathname <- uiputfile()
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if (rowsFromInd == 0) {
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# BAPS format was used, rowsFromInd is not known.
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popnames_rowsFromInd <- findOutRowsFromInd(popnames, rows)
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popnames_rowsFromInd$popnames -> popnames
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popnames_rowsFromInd$rows -> rows
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popnames <- popnames_rowsFromInd$popnames
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rows <- popnames_rowsFromInd$rows
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rm(popnames_rowsFromInd)
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}
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groupPartition <- PARTITION
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@ -101,7 +102,7 @@ greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE)
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c$data <- data
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c$npops <- npops
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c$noalle <- noalle
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c$mixtureType = 'popMix'
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c$mixtureType <- "popMix"
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c$groupPartition <- groupPartition
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c$rows <- rows
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c$logml <- logml
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@ -18,54 +18,57 @@ handlePopData <- function(raw_data) {
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dataApu <- data[, 1:nloci]
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nollat <- find(dataApu == 0)
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if (length(nollat) > 0) {
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isoinAlleeli <- max(max(dataApu)$maxs)$maxs
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dataApu[nollat] <- isoinAlleeli + 1
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data[, 1:nloci] <- dataApu
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isoinAlleeli <- max(max(dataApu)$maxs)$maxs
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dataApu[nollat] <- isoinAlleeli + 1
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data[, 1:nloci] <- dataApu
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}
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noalle <- zeros(1, nloci)
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alleelitLokuksessa <- cell(nloci, 1, expandable = TRUE)
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for (i in 1:nloci) {
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alleelitLokuksessaI <- sort(unique(data[, i]))
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alleelitLokuksessa[[i]] <- alleelitLokuksessaI[find(alleelitLokuksessaI >= 0)]
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noalle[i] <- length(alleelitLokuksessa[[i]])
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alleelitLokuksessaI <- sort(unique(data[, i]))
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alleelitLokuksessa[[i]] <- alleelitLokuksessaI[find(alleelitLokuksessaI >= 0)]
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noalle[i] <- length(alleelitLokuksessa[[i]])
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}
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alleleCodes <- zeros(unique(max(noalle)$maxs), nloci)
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for (i in 1:nloci) {
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alleelitLokuksessaI <- alleelitLokuksessa[[i]]
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puuttuvia <- unique(max(noalle)$maxs) - length(alleelitLokuksessaI)
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alleleCodes[, i] = c(alleelitLokuksessaI, zeros(puuttuvia, 1))
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alleelitLokuksessaI <- alleelitLokuksessa[[i]]
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puuttuvia <- unique(max(noalle)$maxs) - length(alleelitLokuksessaI)
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alleleCodes[, i] <- c(alleelitLokuksessaI, zeros(puuttuvia, 1))
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}
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for (loc in 1:nloci) {
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for (all in 1:noalle[loc]) {
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data[find(data[ , loc] == alleleCodes[all, loc]), loc] <- all
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}
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for (all in 1:noalle[loc]) {
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data[find(data[, loc] == alleleCodes[all, loc]), loc] <- all
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}
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}
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nind <- max(data[, ncol(data)])$maxs
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rows <- zeros(nind, 2)
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for (i in 1:nind) {
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rivit <- t(find(data[, ncol(data)] == i))
|
||||
rows[i, 1] <- min(rivit)$mins
|
||||
rows[i, 2] <- max(rivit)$maxs
|
||||
rivit <- t(find(data[, ncol(data)] == i))
|
||||
rows[i, 1] <- min(rivit)$mins
|
||||
rows[i, 2] <- max(rivit)$maxs
|
||||
}
|
||||
newData <- data
|
||||
|
||||
adjprior <- zeros(unique(max(noalle)$maxs), nloci)
|
||||
priorTerm <- 0
|
||||
for (j in 1:nloci) {
|
||||
adjprior[, j] <- c(repmat(1 / noalle[j], c(noalle[j], 1)), ones(unique(max(noalle)$maxs) - noalle[j], 1))
|
||||
priorTerm <- priorTerm + noalle[j] * log(gamma(1 / noalle[j]))
|
||||
}
|
||||
return(
|
||||
list(
|
||||
newData = newData,
|
||||
rows = rows,
|
||||
alleleCodes = alleleCodes,
|
||||
noalle = noalle,
|
||||
adjprior = adjprior,
|
||||
priorTerm = priorTerm
|
||||
)
|
||||
adjprior[, j] <- c(
|
||||
repmat(1 / noalle[j], c(noalle[j], 1)),
|
||||
ones(unique(max(noalle)$maxs) - noalle[j], 1)
|
||||
)
|
||||
priorTerm <- priorTerm + noalle[j] * log(gamma(1 / noalle[j]))
|
||||
}
|
||||
return(
|
||||
list(
|
||||
newData = newData,
|
||||
rows = rows,
|
||||
alleleCodes = alleleCodes,
|
||||
noalle = noalle,
|
||||
adjprior = adjprior,
|
||||
priorTerm = priorTerm
|
||||
)
|
||||
)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -135,7 +135,8 @@ laskeMuutokset2 <- function(i1, globalRows, data, adjprior, priorTerm) {
|
|||
}
|
||||
|
||||
|
||||
laskeMuutokset3 <- function(T2, inds2, globalRows, data, adjprior, priorTerm, i1) {
|
||||
laskeMuutokset3 <- function(T2, inds2, globalRows, data, adjprior, priorTerm, i1
|
||||
) {
|
||||
# Palauttaa length(unique(T2))*npops taulun, jossa (i,j):s alkio
|
||||
# kertoo, mik<69> olisi muutos logml:ss<73>, jos populaation i1 osapopulaatio
|
||||
# inds2(matlab2r::find(T2==i)) siirret<65><74>n koriin j.
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ lueGenePopDataPop <- function(tiedostonNimi) {
|
|||
nimienLkm <- 0
|
||||
ninds <- 0
|
||||
poimiNimi <- 1
|
||||
digitFormat = -1
|
||||
digitFormat <- -1
|
||||
while (lokusRiveja < length(fid) - 2) {
|
||||
lokusRiveja <- lokusRiveja + 1 # Keeps the loop moving along
|
||||
line <- fid[lokusRiveja + 2]
|
||||
|
|
|
|||
|
|
@ -1,9 +1,12 @@
|
|||
#' @title Generates random number from a Gamma distribution
|
||||
#' @description Generates one random number from shape parameter a and rate parameter b
|
||||
#' @description Generates one random number from shape parameter a and rate
|
||||
#' parameter b
|
||||
#' @param a shape
|
||||
#' @param b rate
|
||||
#' @return One realization of Gamma(a, b)
|
||||
#' @details The generated random variable has mean a / b. It will be positively-skewed for small values, but converges to a symmetric distribution for very large numbers of a and b.
|
||||
#' @details The generated random variable has mean a / b. It will be
|
||||
#' positively-skewed for small values, but converges to a symmetric distribution
|
||||
#' for very large numbers of a and b.
|
||||
randga <- function(a, b) {
|
||||
flag <- 0
|
||||
if (a > 1) {
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
#' @title simuloiAlleeli
|
||||
#' @description Simuloi populaation pop lokukseen loc alleelin.
|
||||
#' @note This function is (only?) called by `simulateIndividuals()`. Therefore, exporting it is probably unnecessary.
|
||||
#' @note This function is (only?) called by `simulateIndividuals()`. Therefore,
|
||||
#' exporting it is probably unnecessary.
|
||||
#' @param allfreqs allfreqa
|
||||
#' @param pop pop
|
||||
#' @param loc loc
|
||||
|
|
|
|||
|
|
@ -1,7 +1,9 @@
|
|||
#' @title Tests GenePop data
|
||||
#' @param tiedostonNimi Filename
|
||||
#' @return kunnossa (binary "ok" condition value) == 0 if the data is not valid genePop data. Otherwise, kunnossa == 1.
|
||||
#' @details GenePop data are textfiles that follow the GenePop format. This function checks if such file is properly formatted as GenePop.
|
||||
#' @return kunnossa (binary "ok" condition value) == 0 if the data is not valid
|
||||
#' genePop data. Otherwise, kunnossa == 1.
|
||||
#' @details GenePop data are textfiles that follow the GenePop format. This
|
||||
#' function checks if such file is properly formatted as GenePop.
|
||||
testaaGenePopData <- function(tiedostonNimi) {
|
||||
# kunnossa == 0, jos data ei ole kelvollinen genePop data.
|
||||
# Muussa tapauksessa kunnossa == 1.
|
||||
|
|
@ -36,7 +38,10 @@ testaaGenePopData <- function(tiedostonNimi) {
|
|||
# Tiedet<65><74>n, ett?pys<79>htyy
|
||||
pointer <- pointer + 1
|
||||
}
|
||||
line4 <- substring(line4, pointer + 1) # pilkun j<>lkeinen osa (the part after the comma)
|
||||
|
||||
# pilkun j<>lkeinen osa (the part after the comma)
|
||||
line4 <- substring(line4, pointer + 1)
|
||||
|
||||
nloci2 <- rivinSisaltamienMjonojenLkm(line4)
|
||||
if (nloci2 != nloci) stop("Incorrect file format 1195")
|
||||
} else {
|
||||
|
|
@ -59,7 +64,10 @@ testaaGenePopData <- function(tiedostonNimi) {
|
|||
# Tiedet<65><74>n, ett?pys<79>htyy
|
||||
pointer <- pointer + 1
|
||||
}
|
||||
line4 <- substring(line4, pointer + 1) # pilkun j<>lkeinen osa (the part after the comma)
|
||||
|
||||
# pilkun j<>lkeinen osa (the part after the comma)
|
||||
line4 <- substring(line4, pointer + 1)
|
||||
|
||||
nloci2 <- rivinSisaltamienMjonojenLkm(line4)
|
||||
if (nloci2 != nloci) stop("Incorrect file format 1228")
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,60 +1,3 @@
|
|||
tulostaAdmixtureTiedot <- function(proportions, uskottavuus, alaRaja, niter) {
|
||||
# ASK: what does this function does. Plotting? Get examples?
|
||||
# h0 <- findobj('Tag','filename1_text')
|
||||
# inputf = get(h0,'String');
|
||||
# h0 = findobj('Tag','filename2_text');
|
||||
# outf = get(h0,'String'); clear h0;
|
||||
|
||||
# if length(outf)>0
|
||||
# fid = fopen(outf,'a');
|
||||
# else
|
||||
# fid = -1;
|
||||
# diary('baps4_output.baps'); % save in text anyway.
|
||||
# end
|
||||
|
||||
# ninds = length(uskottavuus);
|
||||
# npops = size(proportions,2);
|
||||
# disp(' ');
|
||||
# dispLine;
|
||||
# disp('RESULTS OF ADMIXTURE ANALYSIS BASED');
|
||||
# disp('ON MIXTURE CLUSTERING OF INDIVIDUALS');
|
||||
# disp(['Data file: ' inputf]);
|
||||
# disp(['Number of individuals: ' num2str(ninds)]);
|
||||
# disp(['Results based on ' num2str(niter) ' simulations from posterior allele frequencies.']);
|
||||
# disp(' ');
|
||||
# if fid ~= -1
|
||||
# fprintf(fid, '\n');
|
||||
# fprintf(fid,'%s \n', ['--------------------------------------------']); fprintf(fid, '\n');
|
||||
# fprintf(fid,'%s \n', ['RESULTS OF ADMIXTURE ANALYSIS BASED']); fprintf(fid, '\n');
|
||||
# fprintf(fid,'%s \n', ['ON MIXTURE CLUSTERING OF INDIVIDUALS']); fprintf(fid, '\n');
|
||||
# fprintf(fid,'%s \n', ['Data file: ' inputf]); fprintf(fid, '\n');
|
||||
# fprintf(fid,'%s \n', ['Number of individuals: ' num2str(ninds)]); fprintf(fid, '\n');
|
||||
# fprintf(fid,'%s \n', ['Results based on ' num2str(niter) ' simulations from posterior allele frequencies.']); fprintf(fid, '\n');
|
||||
# fprintf(fid, '\n');
|
||||
# end
|
||||
|
||||
# ekaRivi = blanks(6);
|
||||
# for pop = 1:npops
|
||||
# ekaRivi = [ekaRivi blanks(3-floor(log10(pop))) num2str(pop) blanks(2)];
|
||||
# end
|
||||
# ekaRivi = [ekaRivi blanks(1) 'p']; % Added on 29.08.06
|
||||
# disp(ekaRivi);
|
||||
# for ind = 1:ninds
|
||||
# rivi = [num2str(ind) ':' blanks(4-floor(log10(ind)))];
|
||||
# if any(proportions(ind,:)>0)
|
||||
# for pop = 1:npops-1
|
||||
# rivi = [rivi proportion2str(proportions(ind,pop)) blanks(2)];
|
||||
# end
|
||||
# rivi = [rivi proportion2str(proportions(ind,npops)) ': '];
|
||||
# rivi = [rivi ownNum2Str(uskottavuus(ind))];
|
||||
# end
|
||||
# disp(rivi);
|
||||
# if fid ~= -1
|
||||
# fprintf(fid,'%s \n',[rivi]); fprintf(fid,'\n');
|
||||
# end
|
||||
# end
|
||||
# if fid ~= -1
|
||||
# fclose(fid);
|
||||
# else
|
||||
# diary off
|
||||
warning("tulostaAdmixtureTiedot() not implemented" )
|
||||
}
|
||||
|
|
|
|||
|
|
@ -11,12 +11,17 @@
|
|||
#' @param popnames popnames
|
||||
#' @param fixedK fixedK
|
||||
#' @export
|
||||
writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outPutFile, inputFile, partitionSummary, popnames, fixedK) {
|
||||
writeMixtureInfo <- function(
|
||||
logml, rowsFromInd, data, adjprior, priorTerm, outPutFile, inputFile,
|
||||
partitionSummary, popnames, fixedK
|
||||
) {
|
||||
changesInLogml <- list()
|
||||
ninds <- size(data, 1) / rowsFromInd
|
||||
npops <- size(COUNTS, 3)
|
||||
# Check that the names refer to individuals
|
||||
names <- (size(popnames, 1) == ninds) # Tarkistetaan ett?nimet viittaavat yksil<69>ihin
|
||||
|
||||
# Tarkistetaan ett?nimet viittaavat yksil<69>ihin
|
||||
names <- (size(popnames, 1) == ninds)
|
||||
|
||||
if (length(outPutFile) > 0) {
|
||||
fid <- load(outPutFile)
|
||||
|
|
@ -194,7 +199,10 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
|
|||
d <- zeros(maxnoalle, nloci, npops)
|
||||
prior <- adjprior
|
||||
prior[matlab2r::find(prior == 1)] <- 0
|
||||
nollia <- matlab2r::find(all(prior == 0)) # Loci in which only one allele was detected.
|
||||
|
||||
# Loci in which only one allele was detected.
|
||||
nollia <- matlab2r::find(all(prior == 0))
|
||||
|
||||
prior[1, nollia] <- 1
|
||||
for (pop1 in 1:npops) {
|
||||
d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) /
|
||||
|
|
|
|||
|
|
@ -135,7 +135,8 @@ writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm,
|
|||
nollia <- find(all(prior == 0)) # Lokukset, joissa oli havaittu vain yht?alleelia.
|
||||
prior[1, nollia] <- 1
|
||||
for (pop1 in 1:npops) {
|
||||
d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) / repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, 1))
|
||||
d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) /
|
||||
repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, 1))
|
||||
}
|
||||
ekarivi <- as.character(npops)
|
||||
cat(ekarivi, "\n")
|
||||
|
|
@ -166,13 +167,23 @@ writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm,
|
|||
}
|
||||
}
|
||||
cat(" \n \n \n")
|
||||
cat("List of sizes of 10 best visited partitions and corresponding log(ml) values\n")
|
||||
cat(
|
||||
"List of sizes of 10 best visited partitions and corresponding",
|
||||
"log(ml) values\n"
|
||||
)
|
||||
if (exists("fid")) {
|
||||
append(fid, " \n\n")
|
||||
append(fid, " \n\n")
|
||||
append(fid, " \n\n")
|
||||
append(fid, " \n\n")
|
||||
append(fid, "List of sizes of 10 best visited partitions and corresponding log(ml) values\n")
|
||||
append(
|
||||
fid,
|
||||
cat(
|
||||
"List of sizes of 10 best visited partitions and corresponding",
|
||||
"log(ml) values\n"
|
||||
)
|
||||
)
|
||||
|
||||
}
|
||||
partitionSummary <- sortrows(partitionSummary, 2)
|
||||
partitionSummary <- partitionSummary[size(partitionSummary, 1):-1, ]
|
||||
|
|
@ -183,7 +194,11 @@ writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm,
|
|||
vikaPartitio <- size(partitionSummary, 1)
|
||||
}
|
||||
for (part in 1:vikaPartitio) {
|
||||
line <- c(as.character(partitionSummary[part, 1]), " ", as.character(partitionSummary[part, 2]))
|
||||
line <- c(
|
||||
as.character(partitionSummary[part, 1]),
|
||||
" ",
|
||||
as.character(partitionSummary[part, 2])
|
||||
)
|
||||
cat(line, "\n")
|
||||
if (exists("fid")) {
|
||||
append(fid, c(line, "\n"))
|
||||
|
|
@ -204,7 +219,9 @@ writeMixtureInfoPop <- function(logml, rows, data, adjprior, priorTerm,
|
|||
partitionSummary[, 2] <- partitionSummary[, 2] - max(partitionSummary[, 2])
|
||||
sumtn <- sum(exp(partitionSummary[, 2]))
|
||||
for (i in 1:len) {
|
||||
npopstn <- sum(exp(partitionSummary(find(partitionSummary[, 1] == npopsTaulu(i)), 2)))
|
||||
npopstn <- sum(
|
||||
exp(partitionSummary(find(partitionSummary[, 1] == npopsTaulu(i)), 2))
|
||||
)
|
||||
probs[i] <- npopstn / sumtn
|
||||
}
|
||||
for (i in 1:len) {
|
||||
|
|
|
|||
|
|
@ -10,7 +10,9 @@ fgetl(file)
|
|||
\item{file}{character vector to be read, usually an output of `fopen()`}
|
||||
}
|
||||
\value{
|
||||
If the file is nonempty, then fgetl returns tline as a character vector. If the file is empty and contains only the end-of-file marker, then fgetl returns tline as a numeric value -1.
|
||||
If the file is nonempty, then fgetl returns tline as a character
|
||||
vector. If the file is empty and contains only the end-of-file marker, then
|
||||
fgetl returns tline as a numeric value -1.
|
||||
}
|
||||
\description{
|
||||
Equivalent function to its homonymous Matlab equivalent.
|
||||
|
|
|
|||
|
|
@ -15,8 +15,11 @@ randga(a, b)
|
|||
One realization of Gamma(a, b)
|
||||
}
|
||||
\description{
|
||||
Generates one random number from shape parameter a and rate parameter b
|
||||
Generates one random number from shape parameter a and rate
|
||||
parameter b
|
||||
}
|
||||
\details{
|
||||
The generated random variable has mean a / b. It will be positively-skewed for small values, but converges to a symmetric distribution for very large numbers of a and b.
|
||||
The generated random variable has mean a / b. It will be
|
||||
positively-skewed for small values, but converges to a symmetric distribution
|
||||
for very large numbers of a and b.
|
||||
}
|
||||
|
|
|
|||
|
|
@ -17,5 +17,6 @@ simuloiAlleeli(allfreqs, pop, loc)
|
|||
Simuloi populaation pop lokukseen loc alleelin.
|
||||
}
|
||||
\note{
|
||||
This function is (only?) called by `simulateIndividuals()`. Therefore, exporting it is probably unnecessary.
|
||||
This function is (only?) called by `simulateIndividuals()`. Therefore,
|
||||
exporting it is probably unnecessary.
|
||||
}
|
||||
|
|
|
|||
|
|
@ -10,11 +10,13 @@ testaaGenePopData(tiedostonNimi)
|
|||
\item{tiedostonNimi}{Filename}
|
||||
}
|
||||
\value{
|
||||
kunnossa (binary "ok" condition value) == 0 if the data is not valid genePop data. Otherwise, kunnossa == 1.
|
||||
kunnossa (binary "ok" condition value) == 0 if the data is not valid
|
||||
genePop data. Otherwise, kunnossa == 1.
|
||||
}
|
||||
\description{
|
||||
Tests GenePop data
|
||||
}
|
||||
\details{
|
||||
GenePop data are textfiles that follow the GenePop format. This function checks if such file is properly formatted as GenePop.
|
||||
GenePop data are textfiles that follow the GenePop format. This
|
||||
function checks if such file is properly formatted as GenePop.
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,4 +1 @@
|
|||
library(testthat)
|
||||
library(rBAPS)
|
||||
|
||||
test_check("rBAPS")
|
||||
testthat::test_check("rBAPS")
|
||||
|
|
|
|||
|
|
@ -46,7 +46,6 @@ test_that("type convertions behave like on Matlab", {
|
|||
expect_equal(proportion2str(0.4), "0.40")
|
||||
expect_equal(proportion2str(0.89), "0.89")
|
||||
expect_equal(proportion2str(-0.4), "0.0-40") # also bugged in original
|
||||
# TODO: fix after release, as long as it doesn't break anything else
|
||||
})
|
||||
|
||||
test_that("computeRows behaves like on Matlab", {
|
||||
|
|
|
|||
|
|
@ -1,11 +1,7 @@
|
|||
context("Auxiliary functions to greedyMix")
|
||||
|
||||
# Defining the relative path to current inst -----------------------------------
|
||||
if (interactive()) {
|
||||
path_inst <- "../../inst/ext"
|
||||
} else {
|
||||
path_inst <- system.file("ext", "", package = "rBAPS")
|
||||
}
|
||||
path_inst <- system.file("ext", "", package = "rBAPS")
|
||||
|
||||
# Reading datasets -------------------------------------------------------------
|
||||
baps_diploid <- read.delim(
|
||||
|
|
@ -50,7 +46,6 @@ df_bam <- greedyMix(
|
|||
data = file.path(path_inst, "bam_example.bam"),
|
||||
format = "BAM",
|
||||
)
|
||||
# TODO #19: add example reading Genpop
|
||||
test_that("Files are imported correctly", {
|
||||
expect_equal(dim(df_fasta), c(5, 99))
|
||||
expect_equal(dim(df_vcf), c(variants = 2, fix_cols = 8, gt_cols = 3))
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ test_that("Auxiliary functions work properly", {
|
|||
expect_equal(
|
||||
getPopDistancesByKL(x2),
|
||||
list(
|
||||
Z = matrix(c(c(1, 101:198), c(2:100), rep(0, 99)), nrow = 99, ncol = 3),
|
||||
Z = matrix(c(c(1, 101:198), 2:100, rep(0, 99)), nrow = 99, ncol = 3),
|
||||
distances = as.matrix(rep(0, 4950))
|
||||
)
|
||||
)
|
||||
|
|
@ -27,7 +27,7 @@ test_that("Auxiliary functions work properly", {
|
|||
rows = matrix(c(1: 3, 1: 3), 3),
|
||||
alleleCodes = matrix(c(1, 4, 9, 2, 5, 8), 3),
|
||||
noalle = matrix(c(3, 3), 1),
|
||||
adjprior = matrix(rep(3/9, 6), 3),
|
||||
adjprior = matrix(rep(3 / 9, 6), 3),
|
||||
priorTerm = 5.9125
|
||||
)
|
||||
expect_equal(handlePopData(x3), y3, tol = 1e-4)
|
||||
|
|
|
|||
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Add table
Reference in a new issue