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Resolved conflicts involving homonymous functions

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This commit is contained in:
Waldir Leoncio 2022-02-03 10:43:34 +01:00
parent b872760f81
commit 9ad4fa8c76
26 changed files with 92 additions and 90 deletions
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2
NAMESPACE
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@ -36,7 +36,6 @@ export(takeLine)
export(testaaOnkoKunnollinenBapsData)
export(testaaPop)
export(writeMixtureInfo)
import(utils)
importFrom(Rsamtools,scanBam)
importFrom(adegenet,.readExt)
importFrom(adegenet,read.genepop)
@ -45,6 +44,7 @@ importFrom(ape,read.FASTA)
importFrom(matlab2r,blanks)
importFrom(matlab2r,cell)
importFrom(matlab2r,colon)
importFrom(matlab2r,find)
importFrom(matlab2r,inputdlg)
importFrom(matlab2r,isempty)
importFrom(matlab2r,isfield)

2
R/addToSummary.R
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@ -4,7 +4,7 @@ addToSummary <- function(logml, partitionSummary, worstIndex) {
# annettua logml arvoa, niin lis<69>t<EFBFBD><74>n worstIndex:in kohtaan uusi logml ja
# nykyist<73> partitiota vastaava nclusters:in arvo. Muutoin ei tehd<68> mit<69><74>n.
apu <- find(abs(partitionSummary[, 2] - logml) < 1e-5)
apu <- matlab2r::find(abs(partitionSummary[, 2] - logml) < 1e-5)
if (isempty(apu)) {
# Nyt l<>ydetty partitio ei ole viel<65> kirjattuna summaryyn.
npops <- length(unique(PARTITION))

24
R/admix1.R
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@ -130,8 +130,8 @@ admix1 <- function(tietue) {
osuusTaulu[q] <- 1
arvot[q] <- computeIndLogml(omaFreqs, osuusTaulu)
}
iso_arvo <- max(arvot)
isoimman_indeksi <- match(max(arvot), arvot)
iso_arvo <- base::max(arvot)
isoimman_indeksi <- match(base::max(arvot), arvot)
osuusTaulu <- zeros(1, npops)
osuusTaulu[isoimman_indeksi] <- 1
PARTITION[ind] <- isoimman_indeksi
@ -149,7 +149,7 @@ admix1 <- function(tietue) {
}
# Analyze further only individuals who have log-likelihood ratio larger than 3:
to_investigate <- t(find(likelihood > 3))
to_investigate <- t(matlab2r::find(likelihood > 3))
cat("Possibly admixed individuals:\n")
for (i in 1:length(to_investigate)) {
cat(as.character(to_investigate[i]))
@ -200,8 +200,8 @@ admix1 <- function(tietue) {
osuusTaulu[q] <- 1
arvot[q] <- computeIndLogml(omaFreqs, osuusTaulu)
}
iso_arvo <- max(arvot)
isoimman_indeksi <- match(max(arvot), arvot)
iso_arvo <- base::max(arvot)
isoimman_indeksi <- match(base::max(arvot), arvot)
osuusTaulu <- zeros(1, npops)
osuusTaulu[isoimman_indeksi] <- 1
PARTITION[ind] <- isoimman_indeksi
@ -233,13 +233,13 @@ admix1 <- function(tietue) {
missing_levels <- zeros(npops, 3) # the mean values for different levels.
missing_level_partition <- zeros(ninds, 1) # level of each individual (one of the levels of its population).
for (i in 1:npops) {
inds <- find(PARTITION == i)
inds <- matlab2r::find(PARTITION == i)
# Proportions of non-missing data for the individuals:
non_missing_data <- zeros(length(inds), 1)
for (j in 1:length(inds)) {
ind <- inds[j]
non_missing_data[j] <- length(
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)) {
@ -258,7 +258,7 @@ 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[find(part == j)])
missing_levels[i, j] <- mean(non_missing_data[matlab2r::find(part == j)])
}
}
}
@ -269,7 +269,7 @@ admix1 <- function(tietue) {
for (pop in t(admix_populaatiot)) {
for (level in 1:n_missing_levels[pop]) {
potential_inds_in_this_pop_and_level <-
find(
matlab2r::find(
PARTITION == pop & missing_level_partition == level &
likelihood > 3
) # Potential admix individuals here.
@ -338,8 +338,8 @@ admix1 <- function(tietue) {
# In case of a rounding error, the sum is made equal to unity by
# fixing the largest value.
if ((PARTITION[ind] > 0) & (sum(proportionsIt[ind, ]) != 1)) {
isoin <- max(proportionsIt[ind, ])
indeksi <- match(isoin, max(proportionsIt[ind, ]))
isoin <- base::max(proportionsIt[ind, ])
indeksi <- match(isoin, base::max(proportionsIt[ind, ]))
erotus <- sum(proportionsIt[ind, ]) - 1
proportionsIt[ind, indeksi] <- isoin - erotus
}
@ -352,7 +352,7 @@ admix1 <- function(tietue) {
pop <- PARTITION[ind]
if (pop == 0) { # Individual is outlier
uskottavuus[ind] <- 1
} else if (isempty(find(to_investigate == ind))) {
} else if (isempty(matlab2r::find(to_investigate == ind))) {
# Individual had log-likelihood ratio<3
uskottavuus[ind] <- 1
} else {

4
R/admixture_initialization.R
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@ -6,12 +6,12 @@
admixture_initialization <- function(data_matrix, nclusters, Z) {
size_data <- size(data_matrix)
nloci <- size_data[2] - 1
n <- max(data_matrix[, ncol(data_matrix)])
n <- base::max(data_matrix[, ncol(data_matrix)])
T <- cluster_own(Z, nclusters)
initial_partition <- zeros(size_data[1], 1)
for (i in 1:n) {
kori <- T[i]
here <- find(data_matrix[, ncol(data_matrix)] == i)
here <- matlab2r::find(data_matrix[, ncol(data_matrix)] == i)
for (j in 1:length(here)) {
initial_partition[here[j], 1] <- kori
}

2
R/arvoSeuraavaTi.R
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@ -2,7 +2,7 @@ arvoSeuraavaTila <- function(muutokset, logml) {
# Suorittaa yksil<69>n seuraavan tilan arvonnan
y <- logml + muutokset # siirron j<>lkeiset logml:t
y <- y - max(y)
y <- y - base::max(y)
y <- exp(y)
summa <- sum(y)
y <- y / summa

2
R/computeDiffInCounts.R
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@ -6,7 +6,7 @@ computeDiffInCounts <- function(rows, max_noalle, nloci, data) {
diffInCounts <- zeros(max_noalle, nloci)
for (i in seq_len(nrow(data))) {
row <- data[i, ]
notEmpty <- as.matrix(find(row >= 0))
notEmpty <- as.matrix(matlab2r::find(row >= 0))
if (length(notEmpty) > 0) {
diffInCounts[row(notEmpty) + (notEmpty - 1) * max_noalle] <-

4
R/computeLogml.R
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@ -1,10 +1,10 @@
computeLogml <- function(counts, sumcounts, noalle, data, rowsFromInd) {
nloci <- size(counts, 2)
npops <- size(counts, 3)
adjnoalle <- zeros(max(noalle), nloci)
adjnoalle <- zeros(base::max(noalle), nloci)
for (j in 1:nloci) {
adjnoalle[1:noalle[j], j] <- noalle(j)
if ((noalle(j) < max(noalle))) {
if ((noalle(j) < base::max(noalle))) {
adjnoalle[noalle[j] + 1:ncol(adjnoalle), j] <- 1
}
}

8
R/etsiParas.R
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@ -10,13 +10,13 @@ etsiParas <- function(osuus, osuusTaulu, omaFreqs, logml) {
while (ready != 1) {
muutokset <- laskeMuutokset4(osuus, osuusTaulu, omaFreqs, logml)
# Work around R's max() limitation on complex numbers
# Work around R's base::max() limitation on complex numbers
if (any(sapply(muutokset, class) == "complex")) {
maxRe <- max(Re(as.vector(muutokset)))
maxIm <- max(Im(as.vector(muutokset)))
maxRe <- base::max(Re(as.vector(muutokset)))
maxIm <- base::max(Im(as.vector(muutokset)))
maxMuutos <- complex(real = maxRe, imaginary = maxIm)
} else {
maxMuutos <- max(as.vector(muutokset))
maxMuutos <- base::max(as.vector(muutokset))
}
indeksi <- which(muutokset == maxMuutos)
if (Re(maxMuutos) > 0) {

2
R/findEmptyPop.R
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@ -6,7 +6,7 @@ findEmptyPop <- function(npops) {
emptyPop <- -1
} else {
popDiff <- diff(c(0, pops, npops + 1))
emptyPop <- min(find(popDiff > 1))
emptyPop <- base::min(matlab2r::find(popDiff > 1))
}
return(list(emptyPop = emptyPop, pops = pops))
}

12
R/getDistances.R
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@ -9,26 +9,26 @@ getDistances <- function(data_matrix, nclusters) {
size_data <- size(data_matrix)
nloci <- size_data[2] - 1
n <- max(data_matrix[, ncol(data_matrix)])
n <- base::max(data_matrix[, ncol(data_matrix)])
distances <- zeros(choose(n, 2), 1)
pointer <- 1
for (i in 1:n - 1) {
i_data <- data_matrix[
find(data_matrix[, ncol(data_matrix)] == i),
matlab2r::find(data_matrix[, ncol(data_matrix)] == i),
1:nloci
]
for (j in (i + 1):n) {
d_ij <- 0
j_data <- data_matrix[find(data_matrix[, ncol()] == j), 1:nloci]
j_data <- data_matrix[matlab2r::find(data_matrix[, ncol()] == j), 1:nloci]
vertailuja <- 0
for (k in 1:size(i_data, 1)) {
for (l in 1:size(j_data, 1)) {
here_i <- find(i_data[k, ] >= 0)
here_j <- find(j_data[l, ] >= 0)
here_i <- matlab2r::find(i_data[k, ] >= 0)
here_j <- matlab2r::find(j_data[l, ] >= 0)
here_joint <- intersect(here_i, here_j)
vertailuja <- vertailuja + length(here_joint)
d_ij <- d_ij + length(
find(i_data[k, here_joint] != j_data[l, here_joint])
matlab2r::find(i_data[k, here_joint] != j_data[l, here_joint])
)
}
}

2
R/globals.R
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@ -5,8 +5,6 @@ POP_LOGML <- array(1, dim = 100)
LOGDIFF <- array(1, dim = c(100, 100))
# If handling globas break, try other ideas from https://stackoverflow.com/a/65252740/1169233
#' @import utils
utils::globalVariables(
c("PARTITION", "COUNTS", "SUMCOUNTS", "LOGDIFF", "POP_LOGML", "GAMMA_LN")
)

24
R/handleData.R
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@ -24,9 +24,9 @@ handleData <- function(raw_data) {
nloci <- size(raw_data, 2) - 1
dataApu <- data[, 1:nloci]
nollat <- find(dataApu == 0)
nollat <- matlab2r::find(dataApu == 0)
if (!isempty(nollat)) {
isoinAlleeli <- max(max(dataApu))
isoinAlleeli <- base::max(max(dataApu))
dataApu[nollat] <- isoinAlleeli + 1
data[, 1:nloci] <- dataApu
}
@ -39,16 +39,16 @@ handleData <- function(raw_data) {
for (i in 1:nloci) {
alleelitLokuksessaI <- unique(data[, i])
alleelitLokuksessa[[i]] <- sort(alleelitLokuksessaI[
find(
matlab2r::find(
alleelitLokuksessaI >= 0
)
])
noalle[i] <- length(alleelitLokuksessa[[i]])
}
alleleCodes <- zeros(max(noalle), nloci)
alleleCodes <- zeros(base::max(noalle), nloci)
for (i in 1:nloci) {
alleelitLokuksessaI <- alleelitLokuksessa[[i]]
puuttuvia <- max(noalle) - length(alleelitLokuksessaI)
puuttuvia <- base::max(noalle) - length(alleelitLokuksessaI)
alleleCodes[, i] <- as.matrix(
c(alleelitLokuksessaI, zeros(puuttuvia, 1))
)
@ -56,21 +56,21 @@ handleData <- function(raw_data) {
for (loc in seq_len(nloci)) {
for (all in seq_len(noalle[loc])) {
data[find(data[, loc] == alleleCodes[all, loc]), loc] <- all
data[matlab2r::find(data[, loc] == alleleCodes[all, loc]), loc] <- all
}
}
nind <- max(data[, ncol(data)])
nind <- base::max(data[, ncol(data)])
nrows <- size(data, 1)
ncols <- size(data, 2)
rowsFromInd <- zeros(nind, 1)
for (i in 1:nind) {
rowsFromInd[i] <- length(find(data[, ncol(data)] == i))
rowsFromInd[i] <- length(matlab2r::find(data[, ncol(data)] == i))
}
maxRowsFromInd <- max(rowsFromInd)
maxRowsFromInd <- base::max(rowsFromInd)
a <- -999
emptyRow <- repmat(a, c(1, ncols))
lessThanMax <- find(rowsFromInd < maxRowsFromInd)
lessThanMax <- matlab2r::find(rowsFromInd < maxRowsFromInd)
missingRows <- maxRowsFromInd * nind - nrows
data <- rbind(data, zeros(missingRows, ncols))
pointer <- 1
@ -81,12 +81,12 @@ handleData <- function(raw_data) {
newData <- data
rowsFromInd <- maxRowsFromInd
adjprior <- zeros(max(noalle), nloci)
adjprior <- zeros(base::max(noalle), nloci)
priorTerm <- 0
for (j in 1:nloci) {
adjprior[, j] <- as.matrix(c(
repmat(1 / noalle[j], c(noalle[j], 1)),
ones(max(noalle) - noalle[j], 1)
ones(base::max(noalle) - noalle[j], 1)
))
priorTerm <- priorTerm + noalle[j] * lgamma(1 / noalle[j])
}

14
R/indMix.R
View file

@ -48,7 +48,7 @@ indMix <- function(c, npops, dispText = TRUE) {
return()
} else {
npopsTaulu <- as.numeric(npopstext)
ykkoset <- find(npopsTaulu == 1)
ykkoset <- matlab2r::find(npopsTaulu == 1)
npopsTaulu[ykkoset] <- NA # Mik<69>li ykk<6B>si<73> annettu yl<79>rajaksi, ne poistetaan (if ones are given as an upper limit, they are deleted)
if (isempty(npopsTaulu)) {
logml <- 1
@ -233,13 +233,13 @@ indMix <- function(c, npops, dispText = TRUE) {
maxMuutos <- 0
ninds <- size(rows, 1)
for (pop in 1:npops) {
inds2 <- find(PARTITION == pop)
inds2 <- matlab2r::find(PARTITION == pop)
ninds2 <- length(inds2)
if (ninds2 > 2) {
dist2 <- laskeOsaDist(inds2, dist, ninds)
Z2 <- linkage(t(dist2))
if (round == 3) {
npops2 <- max(min(20, floor(ninds2 / 5)), 2)
npops2 <- base::max(base::min(20, floor(ninds2 / 5)), 2)
} else if (round == 4) {
npops2 <- 2 # Moneenko osaan jaetaan
}
@ -253,7 +253,7 @@ indMix <- function(c, npops, dispText = TRUE) {
maxMuutos <- isoin
muuttuvaPop2 <- indeksi %% npops2
if (muuttuvaPop2 == 0) muuttuvaPop2 <- npops2
muuttuvat <- inds2[find(T2 == muuttuvaPop2)]
muuttuvat <- inds2[matlab2r::find(T2 == muuttuvaPop2)]
i2 <- ceiling(indeksi / npops2)
}
}
@ -310,7 +310,7 @@ indMix <- function(c, npops, dispText = TRUE) {
j <- j + 1
pop <- pops[j]
totalMuutos <- 0
inds <- find(PARTITION == pop)
inds <- matlab2r::find(PARTITION == pop)
if (round == 5) {
aputaulu <- c(inds, rand(length(inds), 1))
aputaulu <- sortrows(aputaulu, 2)
@ -398,7 +398,7 @@ indMix <- function(c, npops, dispText = TRUE) {
while (j < npops) {
j <- j + 1
pop <- pops[j]
inds2 <- find(PARTITION == pop)
inds2 <- matlab2r::find(PARTITION == pop)
ninds2 <- length(inds2)
if (ninds2 > 5) {
partition <- PARTITION
@ -410,7 +410,7 @@ indMix <- function(c, npops, dispText = TRUE) {
dist2 <- laskeOsaDist(inds2, dist, ninds)
Z2 <- linkage(t(dist2))
T2 <- cluster_own(Z2, 2)
muuttuvat <- inds2[find(T2 == 1)]
muuttuvat <- inds2[matlab2r::find(T2 == 1)]
muutokset <- laskeMuutokset3(
T2, inds2, rows, data, adjprior, priorTerm, pop

8
R/initialCounts.R
View file

@ -3,18 +3,18 @@ initialCounts <- function(partition, data, npops, rows, noalle, adjprior) {
ninds <- size(rows, 1)
koot <- rows[, 1] - rows[, 2] + 1
maxSize <- max(koot)
maxSize <- base::max(koot)
counts <- zeros(max(noalle), nloci, npops)
counts <- zeros(base::max(noalle), nloci, npops)
sumcounts <- zeros(npops, nloci)
for (i in 1:npops) {
for (j in 1:nloci) {
havainnotLokuksessa <- find(partition == i & data[, j] >= 0)
havainnotLokuksessa <- matlab2r::find(partition == i & data[, j] >= 0)
sumcounts[i, j] <- length(havainnotLokuksessa)
for (k in 1:noalle[j]) {
alleleCode <- k
N_ijk <- length(
find(data[havainnotLokuksessa, j] == alleleCode)
matlab2r::find(data[havainnotLokuksessa, j] == alleleCode)
)
counts[k, j, i] <- N_ijk
}

6
R/initialPopCounts.R
View file

@ -1,16 +1,16 @@
initialPopCounts <- function(data, npops, rows, noalle, adjprior) {
nloci <- size(data, 2)
counts <- zeros(max(noalle), nloci, npops)
counts <- zeros(base::max(noalle), nloci, npops)
sumcounts <- zeros(npops, nloci)
for (i in 1:npops) {
for (j in 1:nloci) {
i_rivit <- rows(i, 1):rows(i, 2)
havainnotLokuksessa <- find(data[i_rivit, j] >= 0)
havainnotLokuksessa <- matlab2r::find(data[i_rivit, j] >= 0)
sumcounts[i, j] <- length(havainnotLokuksessa)
for (k in 1:noalle[j]) {
alleleCode <- k
N_ijk <- length(find(data[i_rivit, j] == alleleCode))
N_ijk <- length(matlab2r::find(data[i_rivit, j] == alleleCode))
counts[k, j, i] <- N_ijk
}
}

8
R/laskeMuutokset12345.R
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@ -68,7 +68,7 @@ laskeMuutokset <- function(ind, globalRows, data, adjprior, priorTerm) {
COUNTS[, , i1] <- COUNTS[, , i1] + diffInCounts
SUMCOUNTS[i1, ] <- SUMCOUNTS[i1, ] + diffInSumCounts
i2 <- find(muutokset == -Inf) # Etsit<69><74>n populaatiot jotka muuttuneet viime kerran j<>lkeen. (Searching for populations that have changed since the last time)
i2 <- matlab2r::find(muutokset == -Inf) # Etsit<69><74>n populaatiot jotka muuttuneet viime kerran j<>lkeen. (Searching for populations that have changed since the last time)
i2 <- setdiff(i2, i1)
i2_logml <- POP_LOGML[i2]
@ -95,7 +95,7 @@ laskeMuutokset2 <- function(i1, globalRows, data, adjprior, priorTerm) {
i1_logml <- POP_LOGML[i1]
inds <- find(PARTITION == i1)
inds <- matlab2r::find(PARTITION == i1)
ninds <- length(inds)
if (ninds == 0) {
@ -138,7 +138,7 @@ laskeMuutokset2 <- function(i1, globalRows, data, adjprior, priorTerm) {
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(find(T2==i)) siirret<65><74>n koriin j.
# inds2(matlab2r::find(T2==i)) siirret<65><74>n koriin j.
npops <- size(COUNTS, 3)
npops2 <- length(unique(T2))
@ -146,7 +146,7 @@ laskeMuutokset3 <- function(T2, inds2, globalRows, data, adjprior, priorTerm, i1
i1_logml <- POP_LOGML[i1]
for (pop2 in 1:npops2) {
inds <- inds2[find(T2 == pop2)]
inds <- inds2[matlab2r::find(T2 == pop2)]
ninds <- length(inds)
if (ninds > 0) {
rows <- list()

6
R/learn_partition_modified.R
View file

@ -11,9 +11,9 @@ learn_partition_modified <- function(ordered) {
part <- learn_simple_partition(ordered, 0.05)
nclust <- length(unique(part))
if (nclust == 3) {
mini_1 <- min(ordered(which(part == 1)))
mini_2 <- min(ordered(which(part == 2)))
mini_3 <- min(ordered(which(part == 3)))
mini_1 <- base::ordered(which(part == 1))
mini_2 <- base::min(ordered(which(part == 2)))
mini_3 <- base::min(ordered(which(part == 3)))
if (mini_1 > 0.9 & mini_2 > 0.9) {
part[part == 2] <- 1
part[part == 3] <- 2

10
R/newGetDistances.R
View file

@ -1,11 +1,11 @@
newGetDistances <- function(data, rowsFromInd) {
ninds <- max(data[, ncol(data)])
ninds <- base::max(data[, ncol(data)])
nloci <- size(data, 2) - 1
riviLkm <- choose(ninds, 2)
empties <- find(data < 0)
empties <- matlab2r::find(data < 0)
data[empties] <- 0
data <- apply(data, 2, as.numeric) # max(noalle) oltava <256
data <- apply(data, 2, as.numeric) # base::max(noalle) oltava <256
pariTaulu <- zeros(riviLkm, 2)
aPointer <- 1
@ -51,10 +51,10 @@ newGetDistances <- function(data, rowsFromInd) {
}
rm(x, y, vertailutNyt)
nollat <- find(vertailuja == 0)
nollat <- matlab2r::find(vertailuja == 0)
dist <- zeros(length(vertailuja), 1)
dist[nollat] <- 1
muut <- find(vertailuja > 0)
muut <- matlab2r::find(vertailuja > 0)
dist[muut] <- summa[muut] / vertailuja[muut]
rm(summa, vertailuja)
Z <- linkage(t(dist))

4
R/poistaTyhjatPopulaatiot.R
View file

@ -1,13 +1,13 @@
poistaTyhjatPopulaatiot <- function(npops) {
# % Poistaa tyhjentyneet populaatiot COUNTS:ista ja
# % SUMCOUNTS:ista. P<>ivitt<74><74> npops:in ja PARTITION:in.
notEmpty <- find(any(SUMCOUNTS, 2))
notEmpty <- matlab2r::find(any(SUMCOUNTS, 2))
COUNTS <- COUNTS[, , notEmpty]
SUMCOUNTS <- SUMCOUNTS[notEmpty, ]
LOGDIFF <- LOGDIFF[, notEmpty]
for (n in 1:length(notEmpty)) {
apu <- find(PARTITION == notEmpty(n))
apu <- matlab2r::find(PARTITION == notEmpty(n))
PARTITION[apu] <- n
}
npops <- length(notEmpty)

2
R/rBAPS-package.R
View file

@ -5,6 +5,6 @@
#' @note Found a bug? Want to suggest a feature? Contribute to the scientific
#' and open source communities by opening an issue on our home page.
#' Check the "BugReports" field on the package description for the URL.
#' @importFrom matlab2r blanks cell colon inputdlg isempty isfield isspace max min ones rand repmat reshape size sortrows squeeze strcmp times zeros
#' @importFrom matlab2r blanks cell colon find inputdlg isempty isfield isspace max min ones rand repmat reshape size sortrows squeeze strcmp times zeros
#' @importFrom stats runif
NULL

2
R/rand_disc.R
View file

@ -1,7 +1,7 @@
rand_disc <- function(CDF) {
# %returns an index of a value from a discrete distribution using inversion method
slump <- rand
har <- find(CDF > slump)
har <- matlab2r::find(CDF > slump)
svar <- har(1)
return(svar)
}

2
R/simuloiAlleeli.R
View file

@ -26,6 +26,6 @@ simuloiAlleeli <- function(allfreqs, pop, loc) {
cumsumma <- cumsum(freqs)
arvo <- runif(1)
isommat <- which(cumsumma > arvo)
all <- min(isommat)
all <- base::min(isommat)
return(all)
}

2
R/testaaOnkoKunnollinenBapsData.R
View file

@ -12,7 +12,7 @@ testaaOnkoKunnollinenBapsData <- function(data) {
return(ninds)
}
lastCol <- data[, ncol(data)]
ninds <- max(lastCol)
ninds <- base::max(lastCol)
if (any(1:ninds != unique(lastCol))) {
ninds <- 0
return(ninds)

8
R/updateGlobalVariables.R
View file

@ -14,7 +14,7 @@ updateGlobalVariables <- function(ind, i2, diffInCounts, adjprior, priorTerm) {
)
LOGDIFF[, c(i1, i2)] <- -Inf
inx <- c(find(PARTITION == i1), find(PARTITION == i2))
inx <- c(matlab2r::find(PARTITION == i1), matlab2r::find(PARTITION == i2))
LOGDIFF[inx, ] <- -Inf
}
@ -22,7 +22,7 @@ updateGlobalVariables2 <- function(i1, i2, diffInCounts, adjprior, priorTerm) {
# % Suorittaa globaalien muuttujien muutokset, kun kaikki
# % korissa i1 olevat yksil<69>t siirret<65><74>n koriin i2.
inds <- find(PARTITION == i1)
inds <- matlab2r::find(PARTITION == i1)
PARTITION[inds] <- i2
COUNTS[, , i1] <- COUNTS[, , i1] - diffInCounts
@ -34,7 +34,7 @@ updateGlobalVariables2 <- function(i1, i2, diffInCounts, adjprior, priorTerm) {
POP_LOGML[i2] <- computePopulationLogml(i2, adjprior, priorTerm)
LOGDIFF[, c(i1, i2)] <- -Inf
inx <- c(find(PARTITION == i1), find(PARTITION == i2))
inx <- c(matlab2r::find(PARTITION == i1), matlab2r::find(PARTITION == i2))
LOGDIFF[inx, ] <- -Inf
}
@ -56,6 +56,6 @@ updateGlobalVariables3 <- function(muuttuvat, diffInCounts, adjprior, priorTerm,
)
LOGDIFF[, c(i1, i2)] <- -Inf
inx <- c(find(PARTITION == i1), find(PARTITION == i2))
inx <- c(matlab2r::find(PARTITION == i1), matlab2r::find(PARTITION == i2))
LOGDIFF[inx, ] <- -Inf
}

16
R/writeMixtureInfo.R
View file

@ -64,7 +64,7 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
append(fid, c("Best Partition: ", "\n"))
}
for (m in 1:cluster_count) {
indsInM <- find(PARTITION == m)
indsInM <- matlab2r::find(PARTITION == m)
length_of_beginning <- 11 + floor(log10(m))
cluster_size <- length(indsInM)
@ -139,8 +139,8 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
nimi <- as.character(popnames[i])
nameSizes[i] <- length(nimi)
}
maxSize <- max(nameSizes)
maxSize <- max(maxSize, 5)
maxSize <- base::max(nameSizes)
maxSize <- base::max(maxSize, 5)
erotus <- maxSize - 5
alku <- blanks(erotus)
ekarivi <- c(alku, " ind", blanks(6 + erotus))
@ -193,8 +193,8 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
nloci <- size(COUNTS, 2)
d <- zeros(maxnoalle, nloci, npops)
prior <- adjprior
prior[find(prior == 1)] <- 0
nollia <- find(all(prior == 0)) # Loci in which only one allele was detected.
prior[matlab2r::find(prior == 1)] <- 0
nollia <- matlab2r::find(all(prior == 0)) # Loci in which only one allele was detected.
prior[1, nollia] <- 1
for (pop1 in 1:npops) {
d[, , pop1] <- (squeeze(COUNTS[, , pop1]) + prior) /
@ -261,7 +261,7 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
partitionSummary <- sortrows(partitionSummary, 2)
partitionSummary <- partitionSummary[size(partitionSummary, 1):1, ]
partitionSummary <- partitionSummary[find(partitionSummary[, 2] > -1e49), ]
partitionSummary <- partitionSummary[matlab2r::find(partitionSummary[, 2] > -1e49), ]
if (size(partitionSummary, 1) > 10) {
vikaPartitio <- 10
} else {
@ -298,12 +298,12 @@ writeMixtureInfo <- function(logml, rowsFromInd, data, adjprior, priorTerm, outP
len <- length(npopsTaulu)
probs <- zeros(len, 1)
partitionSummary[, 2] <- partitionSummary[, 2] -
max(partitionSummary[, 2])
base::max(partitionSummary[, 2])
sumtn <- sum(exp(partitionSummary[, 2]))
for (i in 1:len) {
npopstn <- sum(
exp(
partitionSummary[find(
partitionSummary[matlab2r::find(
partitionSummary[, 1] == npopsTaulu[i]
), 2]
)

6
man/linkage.Rd
View file

@ -23,5 +23,9 @@ output format of PDIST.
Z = linkage(X) returns a matrix Z that encodes a tree containing hierarchical clusters of the rows of the input data matrix X.
}
\note{
This is also a base Matlab function. The reason why the source code is also present here is unclear.
This is also a base MATLAB function. The reason why the BAPS
source code also contains a LINKAGE function is unclear. One could speculate
that BAPS should use this function instead of the base one, so this is why
this function is part of this package (instead of a MATLAB-replicating
package such as matlab2r)
}