Fixed several linting issues

R/admix1.R

@@ -21,8 +21,6 @@ admix1 <- function(tietue) {
       cat("---------------------------------------------------\n")
       message("Reading mixture result from: ", pathname_filename, "...")
     }
-    Sys.sleep(0.0001) # TODO: remove
-
     # ASK: what is this supposed to do? What do graphic obj have to do here?
     # h0 = findobj('Tag','filename1_text');
     # set(h0,'String',filename); clear h0;
@@ -42,12 +40,10 @@ admix1 <- function(tietue) {
 
     if (isfield(c, "gene_lengths") &
       strcmp(c$mixtureType, "linear_mix") |
-      strcmp(c$mixtureType, "codon_mix")) { # if the mixture is from a linkage model
+      strcmp(c$mixtureType, "codon_mix")) {
-      # Redirect the call to the linkage admixture function.
+      # if the mixture is from a linkage model Redirect the call to the linkage
-      # call function noindex to remove the index column
+      # admixture function. call function noindex to remove the index column
       c$data <- noIndex(c$data, c$noalle)
-      # linkage_admix(c) # TODO: obsolete. remove.
-      # return
       stop("linkage_admix not implemented")
     }
     PARTITION <- c$PARTITION
@@ -148,7 +144,8 @@ admix1 <- function(tietue) {
     }
   }
 
-  # Analyze further only individuals who have log-likelihood ratio larger than 3:
+  # Analyze further only individuals who have log-likelihood ratio larger than
+  # 3
   to_investigate <- t(matlab2r::find(likelihood > 3))
   cat("Possibly admixed individuals:\n")
   for (i in 1:length(to_investigate)) {
@@ -229,9 +226,15 @@ admix1 <- function(tietue) {
 
   # Initialize the data structures, which are required in taking the missing
   # data into account:
-  n_missing_levels <- zeros(npops, 1) # number of different levels of "missingness" in each pop (max 3).
+
-  missing_levels <- zeros(npops, 3) # the mean values for different levels.
+  # number of different levels of "missingness" in each pop (max 3).
-  missing_level_partition <- zeros(ninds, 1) # level of each individual (one of the levels of its population).
+  n_missing_levels <- zeros(npops, 1)
+
+  # the mean values for different levels.
+  missing_levels <- zeros(npops, 3)
+
+  # level of each individual (one of the levels of its population).
+  missing_level_partition <- zeros(ninds, 1)
   for (i in 1:npops) {
     inds <- matlab2r::find(PARTITION == i)
     # Proportions of non-missing data for the individuals:
@@ -239,7 +242,9 @@ admix1 <- function(tietue) {
     for (j in 1:length(inds)) {
       ind <- inds[j]
       non_missing_data[j] <- length(
-        matlab2r::find(data[(ind - 1) * rowsFromInd + 1:ind * rowsFromInd, ] > 0)
+        matlab2r::find(
+          data[(ind - 1) * rowsFromInd + 1:ind * rowsFromInd, ] > 0
+        )
       ) / (rowsFromInd * nloci)
     }
     if (all(non_missing_data > 0.9)) {
@@ -247,8 +252,6 @@ admix1 <- function(tietue) {
       missing_levels[i, 1] <- mean(non_missing_data)
       missing_level_partition[inds] <- 1
     } else {
-      # TODO: fix syntax
-      # [ordered, ordering] = sort(non_missing_data);
       ordered <- ordering <- sort(non_missing_data)
       # part = learn_simple_partition(ordered, 0.05);
       part <- learn_partition_modified(ordered)
@@ -258,7 +261,9 @@ admix1 <- function(tietue) {
       n_levels <- length(unique(part))
       n_missing_levels[i] <- n_levels
       for (j in 1:n_levels) {
-        missing_levels[i, j] <- mean(non_missing_data[matlab2r::find(part == j)])
+        missing_levels[i, j] <- mean(
+          non_missing_data[matlab2r::find(part == j)]
+        )
       }
     }
   }
@@ -369,8 +374,8 @@ admix1 <- function(tietue) {
     }
   }
 
-  tulostaAdmixtureTiedot(proportionsIt, uskottavuus, alaRaja, iterationCount) # TODO: textual outputs. probably not necessary. translate nonetheless
+  tulostaAdmixtureTiedot(proportionsIt, uskottavuus, alaRaja, iterationCount)
-  viewPartition(proportionsIt, popnames) # TODO: adapt
+  viewPartition(proportionsIt, popnames)
 
   talle <- inputdlg("Do you want to save the admixture results? [Y/n]", "y")
   if (talle %in% c("y", "Y", "yes", "Yes")) {

R/cluster_own.R

@@ -17,7 +17,8 @@ cluster_own <- function(Z, nclust) {
       if (i <= m) { # original node, no leafs
         T[i] <- clsnum
         clsnum <- clsnum + 1
-      } else if (i < (2 * m - maxclust + 1)) { # created before cutoff, search down the tree
+      } else if (i < (2 * m - maxclust + 1)) {
+        # created before cutoff, search down the tree
         T <- clusternum(Z, T, i - m, clsnum)
         clsnum <- clsnum + 1
       }
@@ -25,7 +26,8 @@ cluster_own <- function(Z, nclust) {
       if (i <= m) { # original node, no leafs
         T[i] <- clsnum
         clsnum <- clsnum + 1
-      } else if (i < (2 * m - maxclust + 1)) { # created before cutoff, search down the tree
+      } else if (i < (2 * m - maxclust + 1)) {
+        # created before cutoff, search down the tree
         T <- clusternum(Z, T, i - m, clsnum)
         clsnum <- clsnum + 1
       }

R/computePopulationLogml.R

@@ -4,7 +4,9 @@ computePopulationLogml <- function(pops, adjprior, priorTerm) {
   # ======================================================== #
   # Limiting COUNTS size                                     #
   # ======================================================== #
-  COUNTS <- COUNTS[seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops, drop = FALSE]
+  COUNTS <- COUNTS[
+    seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops, drop = FALSE
+  ]
 
   x <- size(COUNTS, 1)
   y <- size(COUNTS, 2)
@@ -14,14 +16,16 @@ computePopulationLogml <- function(pops, adjprior, priorTerm) {
   # Computation                                              #
   # ======================================================== #
   isarray <- length(dim(repmat(adjprior, c(1, 1, length(pops))))) > 2
-  # FIXME: 3rd dimension of COUNTS getting dropped
   term1 <- squeeze(
     sum(
       sum(
         reshape(
           lgamma(
             repmat(adjprior, c(1, 1, length(pops))) +
-              COUNTS[seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops, drop = !isarray]
+              COUNTS[
+                seq_len(nrow(adjprior)), seq_len(ncol(adjprior)), pops,
+                drop = !isarray
+              ]
           ),
           c(x, y, z)
         ),

R/etsiParas.R

@@ -11,7 +11,7 @@ etsiParas <- function(osuus, osuusTaulu, omaFreqs, logml) {
     muutokset <- laskeMuutokset4(osuus, osuusTaulu, omaFreqs, logml)
 
     # Work around R's base::max() limitation on complex numbers
-    if (any(sapply(muutokset, class) == "complex")) {
+    if (any(vapply(muutokset, class, vector("character", 1)) == "complex")) {
       maxRe <- base::max(Re(as.vector(muutokset)))
       maxIm <- base::max(Im(as.vector(muutokset)))
       maxMuutos <- complex(real = maxRe, imaginary = maxIm)

R/fgetl-fopen.R

@@ -1,7 +1,9 @@
 #' @title Read line from file, removing newline characters
 #' @description Equivalent function to its homonymous Matlab equivalent.
 #' @param file character vector to be read, usually an output of `fopen()`
-#' @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.
+#' @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.
 #' @author Waldir Leoncio
 #' @seealso fopen
 #' @export

R/fiksaaPartitioYksiloTasolle.R

@@ -13,5 +13,6 @@ fiksaaPartitioYksiloTasolle <- function(rows, rowsFromInd) {
       partitio2[rivi / rowsFromInd] <- PARTITION[ind]
     }
   }
-  PARTITION <<- partitio2
+  global_env <- as.environment(1L)
+  assign("PARTITION", partitio2, envir = global_env)
 }

R/findOutRowsFromInd.R

@@ -1,17 +1,17 @@
 findOutRowsFromInd <- function(popnames, rows, ploidisuus = NULL) {
   if (is.null(ploidisuus)) {
     ploidisuus <- questdlg(
-      quest = 'Specify the type of individuals in the data',
+      quest = "Specify the type of individuals in the data",
-      dlgtitle = 'Individual type?',
+      dlgtitle = "Individual type?",
-      btn = c('Haploid', 'Diploid', 'Tetraploid'),
+      btn = c("Haploid", "Diploid", "Tetraploid"),
-      defbtn = 'Diploid'
+      defbtn = "Diploid"
     )
   }
 
   rowsFromInd <- switch(ploidisuus,
-    'Haploid' = 1,
+    "Haploid" = 1,
-    'Diploid' = 2,
+    "Diploid" = 2,
-    'Tetraploid' = 4
+    "Tetraploid" = 4
   )
 
   popnames2 <- popnames * NA
@@ -22,5 +22,5 @@ findOutRowsFromInd <- function(popnames, rows, ploidisuus = NULL) {
           popnames2[i, 2] <- rivi[rowsFromInd] / rowsFromInd
       }
   }
-	return(list(popnames2 = popnames2, rowsFromInd = rowsFromInd))
+  return(list(popnames2 = popnames2, rowsFromInd = rowsFromInd))
 }

R/getDistances.R

@@ -1,11 +1,12 @@
 getDistances <- function(data_matrix, nclusters) {
 
-  # %finds initial admixture clustering solution with nclusters clusters, uses simple mean Hamming distance
+  # finds initial admixture clustering solution with nclusters clusters, uses
-  # %gives partition in 8 - bit format
+  # simple mean Hamming distance; gives partition in 8 - bit format; allocates
-  # %allocates all alleles of a single individual into the same basket
+  # all alleles of a single individual into the same basket; data_matrix
-  # %data_matrix contains #Loci + 1 columns, last column indicate whose alleles are placed in each row,
+  # contains #Loci + 1 columns, last column indicate whose alleles are placed in
-  # %i.e. ranges from 1 to #individuals. For diploids there are 2 rows per individual, for haploids only a single row
+  # each row, i.e. ranges from 1 to #individuals. For diploids there are 2 rows
-  # %missing values are indicated by zeros in the partition and by negative integers in the data_matrix.
+  # per individual, for haploids only a single row; missing values are indicated
+  # by zeros in the partition and by negative integers in the data_matrix.
 
   size_data <- size(data_matrix)
   nloci <- size_data[2] - 1

R/getPopDistancesByKL.R

@@ -10,18 +10,25 @@ getPopDistancesByKL <- function(adjprior) {
   d <- zeros(maxnoalle, nloci, npops)
   prior <- adjprior
   prior[find(prior == 1)] <- 0
-  nollia <- find(all(prior == 0)) # Lokukset, joissa oli havaittu vain yht?alleelia.
+
+  # Lokukset, joissa oli havaittu vain yht?alleelia.
+  nollia <- find(all(prior == 0))
+
   prior[1, nollia] <- 1
   for (pop1 in 1:npops) {
-    d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) / repmat(sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, ncol(prior)))
+    d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) / repmat(
+      sum(squeeze(COUNTS[, , pop1]) + prior), c(maxnoalle, ncol(prior))
+    )
   }
   pointer <- 1
   for (pop1 in 1:(npops - 1)) {
     for (pop2 in (pop1 + 1):npops) {
       dist1 <- d[, , pop1]
       dist2 <- d[, , pop2]
-      div12 <- sum(sum(dist1 * log2((dist1 + 10^-10) / (dist2 + 10^-10)))) / nloci
+      div12 <- sum(sum(dist1 * log2((dist1 + 10^-10) / (dist2 + 10^-10)))) /
-      div21 <- sum(sum(dist2 * log2((dist2 + 10^-10) / (dist1 + 10^-10)))) / nloci
+        nloci
+      div21 <- sum(sum(dist2 * log2((dist2 + 10^-10) / (dist1 + 10^-10)))) /
+        nloci
       div <- (div12 + div21) / 2
       distances[pointer] <- div
       pointer <- pointer + 1

R/greedyMix.R

@@ -46,5 +46,4 @@ greedyMix <- function(data, format, verbose = TRUE) {
     stop("Format not supported.")
   }
   return(out)
-  # TODO: add handleData(out) or some other post-processing of data
 }

R/greedyPopMix.R

@@ -12,7 +12,8 @@
 #' @references Samtools: a suite of programs for interacting
 #' with high-throughput sequencing data. <http://www.htslib.org/>
 #' @export
-greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE) {
+greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE
+) {
   # Replacing original file reading code with greedyMix()
   rawdata <- greedyMix(data, format, verbose)
 
@@ -26,12 +27,12 @@ greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE)
   priorTerm <- data_greedyMix_handle$priorTerm
   rm(data_greedyMix_handle)
   Z_dist <- getPopDistancesByKL(adjprior)
-  Z_dist$Z -> Z
+  Z <- Z_dist$Z
-  Z_dist$dist -> dist
+  dist <- Z_dist$dist
   rm(Z_dist)
   a_data <- data[, 1:(ncol(data) - 1)]
   sumcounts_counts_logml <- initialPopCounts(a_data, npops, rows, noalle, adjprior)
-  sumcounts_counts_logml$logml -> logml
+  logml <- sumcounts_counts_logml$logml
   rm(sumcounts_counts_logml)
   c <- list()
   c$data <- data
@@ -76,17 +77,17 @@ greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE)
     NULL, NULL, partitionSummary, popnames, fixedK = FALSE
   )
   talle <- questdlg(
-    'Do you want to save the mixture populations so that you can use them later in admixture analysis?',
+    "Do you want to save the mixture populations so that you can use them later in admixture analysis?",
-    'Save results?', c('Yes', 'No'), 'Yes'
+    "Save results?", c("Yes", "No"), "Yes"
   )
-  if (tolower(talle) == 'yes') {
+  if (tolower(talle) == "yes") {
     waitALittle()
     filename_pathname <- uiputfile()
     if (rowsFromInd == 0) {
       # BAPS format was used, rowsFromInd is not known.
       popnames_rowsFromInd <- findOutRowsFromInd(popnames, rows)
-      popnames_rowsFromInd$popnames -> popnames
+      popnames <- popnames_rowsFromInd$popnames
-      popnames_rowsFromInd$rows -> rows
+      rows <- popnames_rowsFromInd$rows
       rm(popnames_rowsFromInd)
     }
     groupPartition <- PARTITION
@@ -101,7 +102,7 @@ greedyPopMix <- function(data, format, partitionCompare = NULL, verbose = TRUE)
     c$data <- data
     c$npops <- npops
     c$noalle <- noalle
-    c$mixtureType = 'popMix'
+    c$mixtureType <- "popMix"
     c$groupPartition <- groupPartition
     c$rows <- rows
     c$logml <- logml

R/handlePopData.R

@@ -18,54 +18,57 @@ handlePopData <- function(raw_data) {
   dataApu <- data[, 1:nloci]
   nollat <- find(dataApu == 0)
   if (length(nollat) > 0) {
-     isoinAlleeli <- max(max(dataApu)$maxs)$maxs
+    isoinAlleeli <- max(max(dataApu)$maxs)$maxs
-     dataApu[nollat] <- isoinAlleeli + 1
+    dataApu[nollat] <- isoinAlleeli + 1
-     data[, 1:nloci] <- dataApu
+    data[, 1:nloci] <- dataApu
   }
 
   noalle <- zeros(1, nloci)
   alleelitLokuksessa <- cell(nloci, 1, expandable = TRUE)
   for (i in 1:nloci) {
-      alleelitLokuksessaI <- sort(unique(data[, i]))
+    alleelitLokuksessaI <- sort(unique(data[, i]))
-      alleelitLokuksessa[[i]]  <- alleelitLokuksessaI[find(alleelitLokuksessaI >= 0)]
+    alleelitLokuksessa[[i]]  <- alleelitLokuksessaI[find(alleelitLokuksessaI >= 0)]
-      noalle[i] <- length(alleelitLokuksessa[[i]])
+    noalle[i] <- length(alleelitLokuksessa[[i]])
   }
   alleleCodes <- zeros(unique(max(noalle)$maxs), nloci)
   for (i in 1:nloci) {
-      alleelitLokuksessaI <- alleelitLokuksessa[[i]]
+    alleelitLokuksessaI <- alleelitLokuksessa[[i]]
-      puuttuvia <- unique(max(noalle)$maxs) - length(alleelitLokuksessaI)
+    puuttuvia <- unique(max(noalle)$maxs) - length(alleelitLokuksessaI)
-      alleleCodes[, i] = c(alleelitLokuksessaI, zeros(puuttuvia, 1))
+    alleleCodes[, i] <- c(alleelitLokuksessaI, zeros(puuttuvia, 1))
   }
 
   for (loc in 1:nloci) {
-      for (all in 1:noalle[loc]) {
+    for (all in 1:noalle[loc]) {
-          data[find(data[ , loc] == alleleCodes[all, loc]), loc] <- all
+      data[find(data[, loc] == alleleCodes[all, loc]), loc] <- all
-      }
+    }
   }
 
   nind <- max(data[, ncol(data)])$maxs
   rows <- zeros(nind, 2)
   for (i in 1:nind) {
-      rivit <- t(find(data[, ncol(data)] == i))
+    rivit <- t(find(data[, ncol(data)] == i))
-      rows[i, 1] <- min(rivit)$mins
+    rows[i, 1] <- min(rivit)$mins
-      rows[i, 2] <- max(rivit)$maxs
+    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))
+    adjprior[, j]  <- c(
-      priorTerm <- priorTerm + noalle[j] * log(gamma(1 / noalle[j]))
+      repmat(1 / noalle[j], c(noalle[j], 1)),
-  }
+      ones(unique(max(noalle)$maxs) - noalle[j], 1)
-	return(
-        list(
-            newData = newData,
-            rows = rows,
-            alleleCodes = alleleCodes,
-            noalle = noalle,
-            adjprior = adjprior,
-            priorTerm = priorTerm
-        )
     )
+    priorTerm <- priorTerm + noalle[j] * log(gamma(1 / noalle[j]))
+  }
+  return(
+    list(
+      newData = newData,
+      rows = rows,
+      alleleCodes = alleleCodes,
+      noalle = noalle,
+      adjprior = adjprior,
+      priorTerm = priorTerm
+    )
+  )
 }

R/laskeMuutokset12345.R

@@ -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.

R/lueGenePopDataPop.R

@@ -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]

R/randga.R

@@ -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) {

R/simuloiAlleeli.R

@@ -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

R/testaaGenePopData.R

@@ -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.
+#' @return kunnossa (binary "ok" condition value) == 0 if the data is not valid
-#' @details GenePop data are textfiles that follow the GenePop format. This function checks if such file is properly formatted as GenePop.
+#' 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")
   }

R/tulostaAdmixtureTiedot.R

@@ -1,60 +1,3 @@
 tulostaAdmixtureTiedot <- function(proportions, uskottavuus, alaRaja, niter) {
-  # ASK: what does this function does. Plotting? Get examples?
+  warning("tulostaAdmixtureTiedot() not implemented" )
-  # 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
 }

R/writeMixtureInfo.R

@@ -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) /

R/writeMixtureInfoPop.R

@@ -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) {

man/fgetl.Rd

@@ -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.

man/randga.Rd

@@ -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.
 }

man/simuloiAlleeli.Rd

@@ -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.
 }

man/testaaGenePopData.Rd

@@ -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.
 }

tests/testthat.R

@@ -1,4 +1 @@
-library(testthat)
+testthat::test_check("rBAPS")
-library(rBAPS)
-
-test_check("rBAPS")

tests/testthat/test-admix1.R

@@ -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", {

tests/testthat/test-greedyMix.R

@@ -1,11 +1,7 @@
 context("Auxiliary functions to greedyMix")
 
 # Defining the relative path to current inst -----------------------------------
-if (interactive()) {
+path_inst <- system.file("ext", "", package = "rBAPS")
-  path_inst <- "../../inst/ext"
-} else {
-  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))

tests/testthat/test-greedyPopMix.R

@@ -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)