Nothing
#############################
## sliding window ld estimation
############################
#' Sliding window LD estimation
#'
#' This function is a wrapper for \code{\link{ldest}()} for estimating LD
#' along a sliding window of a fixed size. Support is provided for parallelization through the
#' foreach and doParallel packages.
#'
#' See \code{\link{ldest}()} for details on the different types of LD
#' estimators supported.
#'
#' @inheritParams mldest
#' @param win The window size. Pairwise LD will be estimated plus or minus
#' these many positions. Larger sizes significantly increase the
#' computational load.
#'
#' @inherit mldest return
#'
#' @seealso
#' \describe{
#' \item{\code{\link{ldest}()}}{For the base function that estimates
#' pairwise LD.}
#' \item{\code{\link{mldest}()}}{For estimating pairwise LD between
#' \emph{all} provided SNPs.}
#' \item{\code{\link{ldfast}()}}{Fast, moment-based approach to LD estimation
#' that also accounts for genotype uncertainty.}
#' \item{\code{\link{format_lddf}()}}{For formatting the output of
#' \code{sldest()} as a matrix.}
#' \item{\code{\link{plot.lddf}()}}{For plotting the output of
#' \code{sldest()}.}
#' }
#'
#' @examples
#' set.seed(1)
#'
#' ## Simulate genotypes when true correlation is 0
#' nloci <- 100
#' nind <- 100
#' win <- 5
#' K <- 6
#' nc <- 1
#' genomat <- matrix(sample(0:K, nind * nloci, TRUE), nrow = nloci)
#'
#' ## Composite LD estimates
#' lddf <- sldest(geno = genomat,
#' K = K,
#' win = win,
#' nc = nc,
#' type = "comp")
#' plot(lddf, element = "z")
#'
#' @author David Gerard
#'
#' @export
sldest <- function(geno,
K,
win = 50,
nc = 1,
type = c("hap", "comp"),
model = c("norm", "flex"),
pen = ifelse(type == "hap", 2, 1),
se = TRUE) {
model <- match.arg(model)
type <- match.arg(type)
if (length(dim(geno)) == 2) {
outdf <- mldest_geno(genomat = geno,
K = K,
nc = nc,
pen = pen,
type = type,
se = se,
win = win)
} else if (length(dim(geno)) == 3) {
outdf <- mldest_genolike(genoarray = geno,
nc = nc,
pen = pen,
type = type,
model = model,
se = se,
win = win)
} else {
stop("sldest: geno needs to either be a matrix or a three-way array.")
}
return(outdf)
}
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