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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
R/addToSummary.R
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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
View file

@ -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]
)