102 lines
3.6 KiB
R
102 lines
3.6 KiB
R
#' @title Handle Data
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#' @param raw_data Raw data
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#' @details The last column of the original data tells you from which
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#' individual that line is from. The function first examines how many line
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#' maximum is from one individual giving know if it is haploid, diploid, etc.
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#' After this function. Add blank lines for individuals with fewer rows as
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#' maximum. If the code of an allele is = 0, the function changes that allele
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#' code to the smallest code that is larger than any code in use. After this,
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#' the function changes the allele codes so that one locus j
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#' codes get values between? 1, ..., Noah (j).
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#' @export
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handleData <- function(raw_data) {
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# Alkuper?isen datan viimeinen sarake kertoo, milt?yksil?lt?
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# kyseinen rivi on per?isin. Funktio tutkii ensin, ett?montako
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# rivi?maksimissaan on per?isin yhdelt?yksil?lt? jolloin saadaan
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# tiet?? onko kyseess?haploidi, diploidi jne... T?m?n j?lkeen funktio
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# lis?? tyhji?rivej?niille yksil?ille, joilta on per?isin v?hemm?n
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# rivej?kuin maksimim??r?
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# Mik?li jonkin alleelin koodi on =0, funktio muuttaa t?m?n alleelin
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# koodi pienimm?ksi koodiksi, joka isompi kuin mik??n k?yt?ss?oleva koodi.
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# T?m?n j?lkeen funktio muuttaa alleelikoodit siten, ett?yhden lokuksen j
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# koodit saavat arvoja v?lill?1,...,noalle(j).
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data <- as.matrix(raw_data)
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nloci <- size(raw_data, 2) - 1
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dataApu <- data[, 1:nloci]
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nollat <- matlab2r::find(dataApu == 0)
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if (!isempty(nollat)) {
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isoinAlleeli <- base::max(max(dataApu))
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dataApu[nollat] <- isoinAlleeli + 1
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data[, 1:nloci] <- dataApu
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}
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# dataApu <- []
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# nollat <- []
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# isoinAlleeli <- []
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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 <- unique(data[, i])
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alleelitLokuksessa[[i]] <- sort(alleelitLokuksessaI[
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matlab2r::find(
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alleelitLokuksessaI >= 0
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)
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])
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noalle[i] <- length(alleelitLokuksessa[[i]])
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}
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alleleCodes <- zeros(base::max(noalle), nloci)
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for (i in 1:nloci) {
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alleelitLokuksessaI <- alleelitLokuksessa[[i]]
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puuttuvia <- base::max(noalle) - length(alleelitLokuksessaI)
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alleleCodes[, i] <- as.matrix(
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c(alleelitLokuksessaI, zeros(puuttuvia, 1))
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)
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}
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for (loc in seq_len(nloci)) {
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for (all in seq_len(noalle[loc])) {
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data[matlab2r::find(data[, loc] == alleleCodes[all, loc]), loc] <- all
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}
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}
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nind <- base::max(data[, ncol(data)])
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nrows <- size(data, 1)
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ncols <- size(data, 2)
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rowsFromInd <- zeros(nind, 1)
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for (i in 1:nind) {
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rowsFromInd[i] <- length(matlab2r::find(data[, ncol(data)] == i))
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}
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maxRowsFromInd <- base::max(rowsFromInd)
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a <- -999
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emptyRow <- repmat(a, c(1, ncols))
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lessThanMax <- matlab2r::find(rowsFromInd < maxRowsFromInd)
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missingRows <- maxRowsFromInd * nind - nrows
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data <- rbind(data, zeros(missingRows, ncols))
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pointer <- 1
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for (ind in t(lessThanMax)) { # K?y l?pi ne yksil?t, joilta puuttuu rivej?
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miss <- maxRowsFromInd - rowsFromInd(ind) # T?lt?yksil?lt?puuttuvien lkm.
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}
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data <- sortrows(data, ncols) # Sorttaa yksil?iden mukaisesti
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newData <- data
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rowsFromInd <- maxRowsFromInd
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adjprior <- zeros(base::max(noalle), nloci)
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priorTerm <- 0
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for (j in 1:nloci) {
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adjprior[, j] <- as.matrix(c(
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repmat(1 / noalle[j], c(noalle[j], 1)),
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ones(base::max(noalle) - noalle[j], 1)
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))
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priorTerm <- priorTerm + noalle[j] * lgamma(1 / noalle[j])
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}
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out <- list(
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newData = newData,
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rowsFromInd = rowsFromInd,
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alleleCodes = alleleCodes,
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noalle = noalle,
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adjprior = adjprior,
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priorTerm = priorTerm
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)
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return(out)
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}
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