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#' @name gl.report.monomorphs
#' @title Reports monomorphic loci
#' @description
#' This script reports the number of monomorphic loci and those with all NAs in
#' a genlight \{adegenet\} object
#' @param x Name of the input genlight object [required].
#' @param verbose Verbosity: 0, silent or fatal errors; 1, begin and end; 2,
#' progress log; 3, progress and results summary; 5, full report
#' [default NULL, unless specified using gl.set.verbosity].
#' @details
#' A DArT dataset will not have monomorphic loci, but they can arise, along with
#' loci that are scored all NA, when populations or individuals are deleted.
#' Retaining monomorphic loci unnecessarily increases the size of the dataset
#' and will affect some calculations.
#'
#' Note that for SNP data, NAs likely represent null alleles; in tag
#' presence/absence data, NAs represent missing values (presence/absence could
#' not be reliably scored)
#' @return An unaltered genlight object
#' @rawNamespace import(adegenet, except = plot)
#' @author Custodian: Arthur Georges -- Post to
#' \url{https://groups.google.com/d/forum/dartr}
#' @examples
#' # SNP data
#' gl.report.monomorphs(testset.gl)
#' # SilicoDArT data
#' gl.report.monomorphs(testset.gs)
#' @seealso \code{\link{gl.filter.monomorphs}}
#' @family report functions
#' @export
gl.report.monomorphs <- function(x,
verbose = NULL) {
# SET VERBOSITY
verbose <- gl.check.verbosity(verbose)
# FLAG SCRIPT START
funname <- match.call()[[1]]
utils.flag.start(func = funname,
build = "Jody",
verbosity = verbose)
# CHECK DATATYPE
datatype <- utils.check.datatype(x, verbose = verbose)
# DO THE JOB
hold <- x
na.counter <- 0
loc.list <- array(NA, nLoc(x))
if (verbose >= 2) {
cat(report(" Identifying monomorphic loci\n"))
}
# # Tag presence/absence data
# if (datatype == "SilicoDArT") {
# mat <- as.matrix(x)
# lN <- locNames(x)
# for (i in 1:nLoc(x)) {
# row <- mat[, i] # Row for each locus
# if (all(row == 0, na.rm = TRUE) |
# all(row == 1, na.rm = TRUE) | all(is.na(row))) {
# loc.list[i] <- lN[i]
# if (all(is.na(row))) {
# na.counter <-na.counter + 1
# }
# }
# }
# }
#
# # SNP data
# if (datatype == "SNP") {
# mat <- as.matrix(x)
# lN <- locNames(x)
# for (i in 1:nLoc(x)) {
# row <- mat[, i] # Row for each locus
# if (all(row == 0, na.rm = TRUE) |
# all(row == 2, na.rm = TRUE) | all(is.na(row))) {
# loc.list[i] <- lN[i]
# if (all(is.na(row))) {
# na.counter <-na.counter + 1
# }
# }
# }
# }
mono_tmp <- gl.alf(x)
loc.list <- rownames(mono_tmp[which(mono_tmp$alf1==1 |
mono_tmp$alf1 == 0),])
loc.list_NA <- rownames(mono_tmp[which(is.na(mono_tmp$alf1)),])
# Remove NAs from list of monomorphic loci and loci with all NAs
loc.list <- loc.list[!is.na(loc.list)]
# remove monomorphic loci and loci with all NAs
if (length(loc.list) > 0) {
x <- gl.drop.loc(x, loc.list = loc.list, verbose = 0)
}
# PRINTING OUTPUTS Report results
cat("\n No. of loci:", nLoc(hold), "\n")
cat(" Polymorphic loci:", nLoc(x), "\n")
cat(" Monomorphic loci:", nLoc(hold) - nLoc(x), "\n")
cat(" Loci scored all NA:", length(loc.list_NA), "\n")
cat(" No. of individuals:", nInd(x), "\n")
cat(" No. of populations:", nPop(x), "\n\n")
# FLAG SCRIPT END
if (verbose >= 1) {
cat(report("Completed:", funname, "\n"))
}
# RETURN
invisible(x)
}
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