Translated handleData
This commit is contained in:
Waldir Leoncio
parent
bb7cfe4b8d
commit
e265f738e8
4 changed files with 120 additions and 83 deletions
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@ -11,6 +11,7 @@ export(computePersonalAllFreqs)
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export(computeRows)
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export(etsiParas)
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export(greedyMix)
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export(handleData)
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export(inputdlg)
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export(isfield)
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export(laskeMuutokset4)
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@ -835,89 +835,6 @@ greedyMix <- function(
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# %---------------------------------------------------------------------------------------
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# function [newData, rowsFromInd, alleleCodes, noalle, adjprior, priorTerm] = ...
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# handleData(raw_data)
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# % Alkuper<65>isen datan viimeinen sarake kertoo, milt?yksil<69>lt?
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# % kyseinen rivi on per<65>isin. Funktio tutkii ensin, ett?montako
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# % rivi?maksimissaan on per<65>isin yhdelt?yksil<69>lt? jolloin saadaan
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# % tiet<65><74> onko kyseess?haploidi, diploidi jne... T<>m<EFBFBD>n j<>lkeen funktio
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# % lis<69><73> tyhji?rivej?niille yksil<69>ille, joilta on per<65>isin v<>hemm<6D>n
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# % rivej?kuin maksimim<69><6D>r?
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# % Mik<69>li jonkin alleelin koodi on =0, funktio muuttaa t<>m<EFBFBD>n alleelin
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# % koodi pienimm<6D>ksi koodiksi, joka isompi kuin mik<69><6B>n k<>yt<79>ss?oleva koodi.
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# % T<>m<EFBFBD>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 = 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 = find(dataApu==0);
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# if ~isempty(nollat)
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# isoinAlleeli = max(max(dataApu));
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# dataApu(nollat) = isoinAlleeli+1;
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# data(:,1:nloci) = dataApu;
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# end
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# dataApu = []; nollat = []; isoinAlleeli = [];
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# noalle=zeros(1,nloci);
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# alleelitLokuksessa = cell(nloci,1);
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# for i=1:nloci
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# alleelitLokuksessaI = unique(data(:,i));
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# alleelitLokuksessa{i,1} = alleelitLokuksessaI(find(alleelitLokuksessaI>=0));
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# noalle(i) = length(alleelitLokuksessa{i,1});
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# end
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# alleleCodes = zeros(max(noalle),nloci);
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# for i=1:nloci
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# alleelitLokuksessaI = alleelitLokuksessa{i,1};
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# puuttuvia = max(noalle)-length(alleelitLokuksessaI);
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# alleleCodes(:,i) = [alleelitLokuksessaI; zeros(puuttuvia,1)];
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# end
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# for loc = 1:nloci
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# for all = 1:noalle(loc)
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# data(find(data(:,loc)==alleleCodes(all,loc)), loc)=all;
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# end;
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# end;
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# nind = max(data(:,end));
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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=1:nind
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# rowsFromInd(i) = length(find(data(:,end)==i));
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# end
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# maxRowsFromInd = max(rowsFromInd);
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# a = -999;
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# emptyRow = repmat(a, 1, ncols);
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# lessThanMax = find(rowsFromInd < maxRowsFromInd);
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# missingRows = maxRowsFromInd*nind - nrows;
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# data = [data; zeros(missingRows, ncols)];
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# pointer = 1;
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# for ind=lessThanMax' %K<>y l<>pi ne yksil<69>t, joilta puuttuu rivej?
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# miss = maxRowsFromInd-rowsFromInd(ind); % T<>lt?yksil<69>lt?puuttuvien lkm.
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# for j=1:miss
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# rowToBeAdded = emptyRow;
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# rowToBeAdded(end) = ind;
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# data(nrows+pointer, :) = rowToBeAdded;
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# pointer = pointer+1;
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# end
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# end
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# data = sortrows(data, ncols); % Sorttaa yksil<69>iden mukaisesti
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# newData = data;
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# rowsFromInd = maxRowsFromInd;
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# adjprior = zeros(max(noalle),nloci);
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# priorTerm = 0;
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# for j=1:nloci
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# adjprior(:,j) = [repmat(1/noalle(j), [noalle(j),1]) ; ones(max(noalle)-noalle(j),1)];
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# priorTerm = priorTerm + noalle(j)*gammaln(1/noalle(j));
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# end
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# %----------------------------------------------------------------------------------------
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# function [Z, dist] = newGetDistances(data, rowsFromInd)
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# ninds = max(data(:,end));
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95
R/handleData.R
Normal file
95
R/handleData.R
Normal file
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@ -0,0 +1,95 @@
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#' @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 <- 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 <- find(dataApu==0)
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if (!isempty(nollat)) {
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isoinAlleeli <- 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)
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for (i in 1:nloci) {
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alleelitLokuksessaI <- unique(data[, i])
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alleelitLokuksessa[i, 1] <- alleelitLokuksessaI[
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find(alleelitLokuksessaI >= 0)
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]
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noalle[i] <- length(alleelitLokuksessa[i, 1])
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}
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alleleCodes <- zeros(max(noalle), nloci)
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for (i in 1:nloci) {
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alleelitLokuksessaI <- alleelitLokuksessa[i, 1]
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puuttuvia <- 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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nind <- max(data[, end])
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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(find(data[, end] == i))
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}
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maxRowsFromInd <- max(rowsFromInd)
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a <- -999
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emptyRow <- repmat(a, c(1, ncols))
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lessThanMax <- find(rowsFromInd < maxRowsFromInd)
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missingRows <- maxRowsFromInd * nind - nrows
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data <- as.matrix(c(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(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(max(noalle) - noalle[j], 1)
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))
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priorTerm <- priorTerm + noalle[j] * gammaln(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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24
man/handleData.Rd
Normal file
24
man/handleData.Rd
Normal file
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@ -0,0 +1,24 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/handleData.R
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\name{handleData}
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\alias{handleData}
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\title{Handle Data}
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\usage{
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handleData(raw_data)
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}
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\arguments{
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\item{raw_data}{Raw data}
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}
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\description{
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Handle Data
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}
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\details{
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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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}
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