#'WSS: Weighted Sum Statistic
#'
#'The WSS method has been proposed by Madsen and Browning (2009) as a pooling
#'approach. In WSS, rare-variant counts within the same gene for each
#'individual are accumulated rather than collapsing on them. Second, it
#'introduces a weighting term to emphasize alleles with a low frequency in
#'controls. Finally, the scores for all samples are ordered, and the WSS is
#'computed as the sum of ranks for cases. The significance is determined by a
#'permutation procedure.
#'
#'There is no imputation for the missing data. Missing values are simply
#'ignored in the computations.
#'
#'@param y numeric vector with phenotype status: 0=controls, 1=cases. No
#'missing data allowed
#'@param X numeric matrix or data frame with genotype data coded as 0, 1, 2.
#'Missing data is allowed
#'@param perm positive integer indicating the number of permutations (100 by
#'default)
#'@return An object of class \code{"assoctest"}, basically a list with the
#'following elements:
#'@returnItem wss.stat wss statistic
#'@returnItem perm.pval permuted p-value
#'@returnItem args descriptive information with number of controls, cases,
#'variants, and permutations
#'@returnItem name name of the statistic
#'@author Gaston Sanchez
#'@seealso \code{\link{CMC}}
#'@references Madsen BE, Browning SR (2009) A Groupwise Association Test for
#'Rare Mutations Using a Weighted Sum Statistic. \emph{PLoS Genetics},
#'\bold{5(2)}: e1000384
#'@examples
#'
#' \dontrun{
#'
#' # number of cases
#' cases = 500
#'
#' # number of controls
#' controls = 500
#'
#' # total (cases + controls)
#' total = cases + controls
#'
#' # phenotype vector
#' phenotype = c(rep(1, cases), rep(0, controls))
#'
#' # genotype matrix with 10 variants (random data)
#' set.seed(123)
#' genotype = matrix(rbinom(total*10, 2, 0.05), nrow=total, ncol=10)
#'
#' # apply WSS with 500 permutations
#' mywss = WSS(phenotype, genotype, perm=500)
#' mywss
#' }
#'
WSS <-
function(y, X, perm=100)
{
## checking arguments
Xy_perm = my_check(y, X, perm)
y = Xy_perm$y
X = Xy_perm$X
perm = Xy_perm$perm
## running wss method
wss.stat = my_wss_method(y, X)
## permutations
perm.pval = NA
if (perm > 0)
{
x.perm = rep(0, perm)
for (i in 1:perm)
{
perm.sample = sample(1:length(y))
x.perm[i] = my_wss_method(y[perm.sample], X)
}
# p-value
perm.pval = sum(x.perm >= wss.stat) / perm
}
## results
name = "WSS: Weighted Sum Statistic"
arg.spec = c(sum(y), length(y)-sum(y), ncol(X), perm)
names(arg.spec) = c("cases", "controls", "variants", "n.perms")
res = list(wss.stat = wss.stat,
perm.pval = perm.pval,
args = arg.spec,
name = name)
class(res) = "assoctest"
return(res)
}
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