Fixed basic parsing of FASTA files (#25)

NAMESPACE

@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(greedyMix)
+export(handleData)
 export(load_fasta)
 importFrom(R6,R6Class)
 importFrom(Rsamtools,scanBam)

R/greedyMix.R

@@ -54,7 +54,7 @@ greedyMix <- function(
   # Generating partition summary ===============================================
   ekat <- seq(1L, c[["rowsFromInd"]], ninds * c[["rowsFromInd"]]) # ekat = (1:rowsFromInd:ninds*rowsFromInd)';
   c[["rows"]] <- c(ekat, ekat + c[["rowsFromInd"]] - 1L) # c.rows = [ekat ekat+rowsFromInd-1]
-  logml_npops_partitionSummary <- indMixWrapper(c, npops, counts, sumcounts, max_iter, fixedK, verbose);
+  logml_npops_partitionSummary <- indMixWrapper(c, npops, counts, sumcounts, max_iter, fixedK, verbose)
   logml <- logml_npops_partitionSummary[["logml"]]
   npops <- logml_npops_partitionSummary[["npops"]]
   partitionSummary <- logml_npops_partitionSummary[["partitionSummary"]]
@@ -72,8 +72,8 @@ greedyMix <- function(
 
   # Writing mixture info =======================================================
   changesInLogml <- writeMixtureInfo(
-    logml, rowsFromInd, data, adjprior, priorTerm, NULL, inp, partitionSummary,
+    logml, c[["rowsFromInd"]], c[["data"]], c[["adjprior"]], c[["priorTerm"]],
-    popnames, fixedK
+    NULL, inp, partitionSummary, popnames, fixedK
   )
 
   # Updateing results ==========================================================

R/handleData.R

@@ -9,6 +9,7 @@
 #' code to the smallest code that is larger than any code in use. After this,
 #' the function changes the allele codes so that one locus j
 #' codes get values between? 1, ..., noalle(j).
+#' @export
 handleData <- function(raw_data, format = "Genepop") {
   # Alkuper?isen datan viimeinen sarake kertoo, milt?yksil?lt?
   # kyseinen rivi on per?isin. Funktio tutkii ensin, ett?montako

R/indMix.R

@@ -68,7 +68,7 @@ indMix <- function(c, npops, counts = NULL, sumcounts = NULL, max_iter = 100L, d
   nruns <- length(npopsTaulu)
 
   initData <- data
-  data <- data[, 1:(ncol(data) - 1)]
+  data <- data[, seq_along(noalle)]  # Original code always dropped last column.
 
   logmlBest <- -1e50
   partitionSummary <- -1e50 * ones(30, 2) # Tiedot 30 parhaasta partitiosta (npops ja logml)

R/laskeLoggis.R

@@ -1,7 +1,7 @@
 laskeLoggis <- function(counts, sumcounts, adjprior) {
   npops <- size(counts, 3)
-
+  replicated_adjprior <- array(adjprior, c(nrow(adjprior), ncol(adjprior), npops))
-  sum1 <- sum(sum(sum(lgamma(counts + repmat(adjprior, c(1, 1, npops))))))
+  sum1 <- sum(sum(sum(lgamma(counts + replicated_adjprior))))
   sum3 <- sum(sum(lgamma(adjprior))) - sum(sum(lgamma(1 + sumcounts)))
   logml2 <- sum1 - npops * sum3
   loggis <- logml2

R/writeMixtureInfo.R

@@ -31,12 +31,12 @@ writeMixtureInfo <- function(
   }
 
   dispLine()
-  cat("RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:")
+  cat("RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:\n")
-  cat(c("Data file: ", inputFile))
+  cat("Data file: ", inputFile, "\n")
-  cat("Model: independent")
+  cat("Model: independent\n")
-  cat(c("Number of clustered individuals: ", ownNum2Str(ninds)))
+  cat("Number of clustered individuals: ", ownNum2Str(ninds), "\n")
-  cat(c("Number of groups in optimal partition: ", ownNum2Str(npops)))
+  cat("Number of groups in optimal partition: ", ownNum2Str(npops), "\n")
-  cat(c("Log(marginal likelihood) of optimal partition: ", ownNum2Str(logml)))
+  cat("Log(marginal likelihood) of optimal partition: ", ownNum2Str(logml), "\n")
   cat(" ")
   if (fid != -1) {
     append(fid, "RESULTS OF INDIVIDUAL LEVEL MIXTURE ANALYSIS:\n")
@@ -87,10 +87,10 @@ writeMixtureInfo <- function(
         "Cluster ", as.character(m), ": {", as.character(indsInM[1])
       )
       for (k in 2:cluster_size) {
-        text <- c(text, ", ", as.character(indsInM[k]))
+        text <- c(text, ",", as.character(indsInM[k]))
       }
     }
-    text <- c(text, "}")
+    text <- c(text, "}\n")
     while (length(text) > 58) {
       # Take one line and display it.
       new_line <- takeLine(text, 58)
@@ -106,7 +106,7 @@ writeMixtureInfo <- function(
         text <- ""
       }
     }
-    if (text != "") {
+    if (any(text != "")) {
       cat(text)
       if (fid != -1) {
         append(fid, text)
@@ -116,11 +116,11 @@ writeMixtureInfo <- function(
   }
 
   if (npops > 1) {
-    cat(" ")
+    cat("\n")
-    cat(" ")
+    cat("\n")
     cat(
       "Changes in log(marginal likelihood)",
-      " if indvidual i is moved to group j:"
+      " if indvidual i is moved to group j:\n"
     )
     if (fid != -1) {
       append(fid, " ")
@@ -131,7 +131,7 @@ writeMixtureInfo <- function(
         fid,
         c(
           "Changes in log(marginal likelihood)",
-          "if indvidual i is moved to group j:"
+          "if indvidual i is moved to group j:\n"
         )
       )
       append(fid, "\n")
@@ -167,9 +167,9 @@ writeMixtureInfo <- function(
 
       if (names) {
         nimi <- as.character(popnames[ind])
-        rivi <- c(blanks(maxSize - length(nimi)), nimi, ":")
+        rivi <- c(blanks(maxSize - length(nimi)), nimi, ":\n")
       } else {
-        rivi <- c(blanks(4 - floor(log10(ind))), ownNum2Str(ind), ":")
+        rivi <- c("\n", blanks(4 - floor(log10(ind))), ownNum2Str(ind), ":\n")
       }
       for (j in 1:npops) {
         rivi <- c(rivi, "  ", logml2String(omaRound(muutokset[j])))
@@ -181,9 +181,9 @@ writeMixtureInfo <- function(
       }
     }
 
-    cat(" ")
+    cat("\n")
-    cat(" ")
+    cat("\n")
-    cat("KL-divergence matrix in PHYLIP format:")
+    cat("KL-divergence matrix in PHYLIP format:\n")
 
     dist_mat <- zeros(npops, npops)
     if (fid != -1) {
@@ -193,6 +193,7 @@ writeMixtureInfo <- function(
       append(fid, "\n")
     }
 
+    COUNTS <- COUNTS[seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), , drop = FALSE]
     maxnoalle <- size(COUNTS, 1)
     nloci <- size(COUNTS, 2)
     d <- zeros(maxnoalle, nloci, npops)
@@ -204,8 +205,8 @@ writeMixtureInfo <- function(
 
     prior[1, nollia] <- 1
     for (pop1 in 1:npops) {
-      d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) /
+      squeezed_COUNTS_prior <- squeeze(COUNTS[, , pop1]) + prior
-        repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, 1))
+      d[, , pop1] <- squeezed_COUNTS_prior / sum(squeezed_COUNTS_prior)
     }
     ekarivi <- as.character(npops)
     cat(ekarivi)
@@ -215,14 +216,14 @@ writeMixtureInfo <- function(
     }
 
     for (pop1 in 1:npops) {
-      for (pop2 in 1:(pop1 - 1)) {
+      for (pop2 in seq_len(pop1 - 1)) {
         dist1 <- d[, , pop1]
         dist2 <- d[, , pop2]
         div12 <- sum(
-          sum(dist1 * log2((dist1 + 10^-10) / (dist2 + 10^-10)))
+          sum(dist1 * base::log2((dist1 + 10^-10) / (dist2 + 10^-10)))
         ) / nloci
         div21 <- sum(
-          sum(dist2 * log2((dist2 + 10^-10) / (dist1 + 10^-10)))
+          sum(dist2 * base::log2((dist2 + 10^-10) / (dist1 + 10^-10)))
         ) / nloci
         div <- (div12 + div21) / 2
         dist_mat[pop1, pop2] <- div
@@ -232,9 +233,9 @@ writeMixtureInfo <- function(
 
     dist_mat <- dist_mat + t(dist_mat) # make it symmetric
     for (pop1 in 1:npops) {
-      rivi <- c("Cluster_", as.character(pop1), " ")
+      rivi <- c("\nCluster_", as.character(pop1), "\n")
       for (pop2 in 1:npops) {
-        rivi <- c(rivi, kldiv2str(dist_mat[pop1, pop2]), " ")
+        rivi <- c(rivi, kldiv2str(dist_mat[pop1, pop2]))
       }
       cat(rivi)
       if (fid != -1) {
@@ -244,11 +245,11 @@ writeMixtureInfo <- function(
     }
   }
 
-  cat(" ")
+  cat("\n")
-  cat(" ")
+  cat("\n")
   cat(
     "List of sizes of 10 best visited partitions",
-    "and corresponding log(ml) values"
+    "and corresponding log(ml) values\n"
   )
 
   if (fid != -1) {
@@ -278,7 +279,7 @@ writeMixtureInfo <- function(
     line <- c(
       as.character(partitionSummary[part, 1]),
       "    ",
-      as.character(partitionSummary(part, 2))
+      as.character(partitionSummary[part, 2])
     )
     cat(line)
     if (fid != -1) {
@@ -288,9 +289,9 @@ writeMixtureInfo <- function(
   }
 
   if (!fixedK) {
-    cat(" ")
+    cat("\n")
-    cat(" ")
+    cat("\n")
-    cat("Probabilities for number of clusters")
+    cat("Probabilities for number of clusters\n")
 
     if (fid != -1) {
       append(fid, " ")
@@ -322,7 +323,7 @@ writeMixtureInfo <- function(
         line <- c(
           as.character(npopsTaulu[i]), "   ", as.character(probs[i])
         )
-        cat(line)
+        cat(line, "\n")
         if (fid != -1) {
           append(fid, line)
           append(fid, "\n")

man/greedyMix.Rd

@@ -8,9 +8,8 @@ greedyMix(
   data,
   format,
   partitionCompare = NULL,
-  ninds = NULL,
+  ninds = 1L,
   npops = 1L,
-  priorTerm = NULL,
   counts = NULL,
   sumcounts = NULL,
   max_iter = 100L,
@@ -32,8 +31,6 @@ greedyMix(
 
 \item{npops}{number of populations}
 
-\item{priorTerm}{prior terms}
-
 \item{counts}{counts}
 
 \item{sumcounts}{sumcounts}
@@ -55,6 +52,8 @@ greedyMix(
 \item{noalle}{number of alleles}
 
 \item{adjprior}{ajuster prior probabilities}
+
+\item{priorTerm}{prior terms}
 }
 \description{
 Clustering of individuals

matlab/independent/indMix.m

@@ -87,7 +87,7 @@ for run = 1:nruns
         apu = rows(i);
         PARTITION(i) = initialPartition(apu(1));
     end
-    
+
     COUNTS = counts; SUMCOUNTS = sumcounts;
     POP_LOGML = computePopulationLogml(1:npops, adjprior, priorTerm);
     LOGDIFF = repmat(-Inf,ninds,npops);
@@ -98,7 +98,7 @@ for run = 1:nruns
     kokeiltu = zeros(nRoundTypes, 1);
     roundTypes = [1 1];  %Ykkösvaiheen sykli kahteen kertaan.
     ready = 0; vaihe = 1;
-    
+
     if dispText
         disp(' ');
         disp(['Mixture analysis started with initial ' num2str(npops) ' populations.']);
@@ -106,11 +106,11 @@ for run = 1:nruns
 
     while ready ~= 1
         muutoksia = 0;
-        
+
         if dispText
             disp(['Performing steps: ' num2str(roundTypes)]);
         end
-        
+
         for n = 1:length(roundTypes)
 
             round = roundTypes(n);
@@ -465,7 +465,7 @@ for run = 1:nruns
 
     npops = poistaTyhjatPopulaatiot(npops);
     POP_LOGML = computePopulationLogml(1:npops, adjprior, priorTerm);
-    if dispText       
+    if dispText
         disp(['Found partition with ' num2str(npops) ' populations.']);
         disp(['Log(ml) = ' num2str(logml)]);
         disp(' ');
@@ -491,7 +491,7 @@ COUNTS = countsBest;
 SUMCOUNTS = sumCountsBest;
 POP_LOGML = pop_logmlBest;
 LOGDIFF = logdiffbest;
-    
+
 %--------------------------------------------------------------------------
 
 function clearGlobalVars
@@ -509,9 +509,9 @@ function Z = linkage(Y, method)
 [k, n] = size(Y);
 m = (1+sqrt(1+8*n))/2;
 if k ~= 1 | m ~= fix(m)
-  error('The first input has to match the output of the PDIST function in size.');   
+  error('The first input has to match the output of the PDIST function in size.');
 end
-if nargin == 1 % set default switch to be 'co' 
+if nargin == 1 % set default switch to be 'co'
    method = 'co';
 end
 method = lower(method(1:2)); % simplify the switch string.
@@ -519,19 +519,19 @@ monotonic = 1;
 Z = zeros(m-1,3); % allocate the output matrix.
 N = zeros(1,2*m-1);
 N(1:m) = 1;
-n = m; % since m is changing, we need to save m in n. 
+n = m; % since m is changing, we need to save m in n.
 R = 1:n;
 for s = 1:(n-1)
    X = Y;
    [v, k] = min(X);
    i = floor(m+1/2-sqrt(m^2-m+1/4-2*(k-1)));
    j = k - (i-1)*(m-i/2)+i;
-   Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A   
+   Z(s,:) = [R(i) R(j) v]; % update one more row to the output matrix A
    I1 = 1:(i-1); I2 = (i+1):(j-1); I3 = (j+1):m; % these are temp variables.
    U = [I1 I2 I3];
    I = [I1.*(m-(I1+1)/2)-m+i i*(m-(i+1)/2)-m+I2 i*(m-(i+1)/2)-m+I3];
    J = [I1.*(m-(I1+1)/2)-m+j I2.*(m-(I2+1)/2)-m+j j*(m-(j+1)/2)-m+I3];
-   
+
    switch method
    case 'si' %single linkage
       Y(I) = min(Y(I),Y(J));
@@ -548,12 +548,12 @@ for s = 1:(n-1)
    end
    J = [J i*(m-(i+1)/2)-m+j];
    Y(J) = []; % no need for the cluster information about j.
-   
+
    % update m, N, R
-   m = m-1; 
+   m = m-1;
    N(n+s) = N(R(i)) + N(R(j));
    R(i) = n+s;
-   R(j:(n-1))=R((j+1):n); 
+   R(j:(n-1))=R((j+1):n);
 end
 
 
@@ -623,7 +623,7 @@ function [muutokset, diffInCounts] = ...
 %
 % Lisäys 25.9.2007:
 % Otettu käyttöön globaali muuttuja LOGDIFF, johon on tallennettu muutokset
-% logml:ssä siirrettäessä yksilöitä toisiin populaatioihin. 
+% logml:ssä siirrettäessä yksilöitä toisiin populaatioihin.
 
 global COUNTS;      global SUMCOUNTS;
 global PARTITION;   global POP_LOGML;
@@ -647,7 +647,7 @@ COUNTS(:,:,i1) = COUNTS(:,:,i1)+diffInCounts;
 SUMCOUNTS(i1,:) = SUMCOUNTS(i1,:)+diffInSumCounts;
 
 i2 = find(muutokset==-Inf);     % Etsitään populaatiot jotka muuttuneet viime kerran jälkeen.
-i2 = setdiff(i2,i1);            
+i2 = setdiff(i2,i1);
 i2_logml = POP_LOGML(i2);
 
 ni2 = length(i2);
@@ -668,19 +668,19 @@ LOGDIFF(ind,:) = muutokset;
 
 function diffInCounts = computeDiffInCounts(rows, max_noalle, nloci, data)
 % Muodostaa max_noalle*nloci taulukon, jossa on niiden alleelien
-% lukumäärät (vastaavasti kuin COUNTS:issa), jotka ovat data:n 
+% lukumäärät (vastaavasti kuin COUNTS:issa), jotka ovat data:n
 % riveillä rows. rows pitää olla vaakavektori.
 
 diffInCounts = zeros(max_noalle, nloci);
 for i=rows
     row = data(i,:);
     notEmpty = find(row>=0);
-    
+
     if length(notEmpty)>0
         diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) = ...
             diffInCounts(row(notEmpty) + (notEmpty-1)*max_noalle) + 1;
     end
-end    
+end
 
 %------------------------------------------------------------------------
 
@@ -693,8 +693,8 @@ function updateGlobalVariables(ind, i2, diffInCounts, ...
 % Suorittaa globaalien muuttujien muutokset, kun yksilö ind
 % on siirretään koriin i2.
 
-global PARTITION; 
+global PARTITION;
-global COUNTS; 
+global COUNTS;
 global SUMCOUNTS;
 global POP_LOGML;
 global LOGDIFF;
@@ -724,7 +724,7 @@ function [muutokset, diffInCounts] = laskeMuutokset2( ...
     i1, globalRows, data, adjprior, priorTerm);
 % Palauttaa npops*1 taulun, jossa i:s alkio kertoo, mikä olisi
 % muutos logml:ssä, mikäli korin i1 kaikki yksilöt siirretään
-% koriin i. 
+% koriin i.
 
 global COUNTS;      global SUMCOUNTS;
 global PARTITION;   global POP_LOGML;
@@ -839,7 +839,7 @@ for pop2 = 1:npops2
 
         i2 = [1:i1-1 , i1+1:npops];
         i2_logml = POP_LOGML(i2)';
-    
+
         COUNTS(:,:,i2) = COUNTS(:,:,i2)+repmat(diffInCounts, [1 1 npops-1]);
         SUMCOUNTS(i2,:) = SUMCOUNTS(i2,:)+repmat(diffInSumCounts,[npops-1 1]);
         new_i2_logml = computePopulationLogml(i2, adjprior, priorTerm)';
@@ -848,7 +848,7 @@ for pop2 = 1:npops2
 
         muutokset(pop2,i2) = new_i1_logml - i1_logml ...
             + new_i2_logml - i2_logml;
-    end    
+    end
 end
 
 %------------------------------------------------------------------------------------
@@ -858,7 +858,7 @@ function muutokset = laskeMuutokset5(inds, globalRows, data, adjprior, ...
 
 % Palauttaa length(inds)*1 taulun, jossa i:s alkio kertoo, mikä olisi
 % muutos logml:ssä, mikäli yksilö i vaihtaisi koria i1:n ja i2:n välillä.
-    
+
 global COUNTS;      global SUMCOUNTS;
 global PARTITION;   global POP_LOGML;
 
@@ -885,14 +885,14 @@ for i = 1:ninds
     SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)-diffInSumCounts;
     COUNTS(:,:,pop2) = COUNTS(:,:,pop2)+diffInCounts;
     SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)+diffInSumCounts;
-    
+
     new_logmls = computePopulationLogml([i1 i2], adjprior, priorTerm);
     muutokset(i) = sum(new_logmls);
-    
+
     COUNTS(:,:,pop1) = COUNTS(:,:,pop1)+diffInCounts;
     SUMCOUNTS(pop1,:) = SUMCOUNTS(pop1,:)+diffInSumCounts;
     COUNTS(:,:,pop2) = COUNTS(:,:,pop2)-diffInCounts;
-    SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)-diffInSumCounts; 
+    SUMCOUNTS(pop2,:) = SUMCOUNTS(pop2,:)-diffInSumCounts;
 end
 
 muutokset = muutokset - i1_logml - i2_logml;
@@ -952,7 +952,7 @@ dist2 = dist(apu);
 
 
 function npops = poistaTyhjatPopulaatiot(npops)
-% Poistaa tyhjentyneet populaatiot COUNTS:ista ja 
+% Poistaa tyhjentyneet populaatiot COUNTS:ista ja
 % SUMCOUNTS:ista. Päivittää npops:in ja PARTITION:in.
 
 global COUNTS;
@@ -1006,7 +1006,7 @@ if abs(logml)<10000
     end
     if logml<0
         mjono(pointer-1) = '-';
-    end 
+    end
 else
     suurinYks = 4;
     while abs(logml)/(10^(suurinYks+1)) >= 1
@@ -1035,8 +1035,8 @@ end
 
 function digit = palautaYks(num,yks)
 % palauttaa luvun num 10^yks termin kertoimen
-% string:inä 
+% string:inä
-% yks täytyy olla kokonaisluku, joka on 
+% yks täytyy olla kokonaisluku, joka on
 % vähintään -1:n suuruinen. Pienemmillä
 % luvuilla tapahtuu jokin pyöristysvirhe.
 
@@ -1063,7 +1063,7 @@ if abs(div)<100
     if arvo>0
         mjono(1) = num2str(arvo);
     end
-    
+
 else
     suurinYks = floor(log10(div));
     mjono(6) = num2str(suurinYks);
@@ -1125,7 +1125,7 @@ T = zeros(m,1);
          end
       end
    end
-   
+
 function T = clusternum(X, T, k, c)
 m = size(X,1)+1;
 while(~isempty(k))
@@ -1136,7 +1136,7 @@ while(~isempty(k))
    % Assign this node number to leaf children
    t = (children<=m);
    T(children(t)) = c;
-   
+
    % Move to next level
    k = children(~t) - m;
 end