#'RBT: Replication Based Test
#'
#'The Replication-Based Test has been proposed by Ionita-Laza et al (2011). RBT
#'is based on a weighted-sum statistic that is similar to a pooled association
#'test but purposefully designed to deal with possibly different association
#'directions. The significance of the statistic has to be calculated by
#'permutations.
#'
#'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 rbt.stat rbt 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{WSS}}, \code{\link{CMC}}
#'@references Ionita-Laza I, Buxbaum JD, Laird NM, Lange C (2011) A New Testing
#'Strategy to Identify Rare Variants with Either risk or Protective Effects on
#'Disease. \emph{PLoS Genetics}, \bold{7(2)}: e1001289
#'@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 RBT with 500 permutations
#' myrbt = RBT(phenotype, genotype, perm=500)
#' myrbt
#' }
#'
RBT <-
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
## apply RBT method
rbt.stat = my_rbt_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_rbt_method(y[perm.sample], X)
}
# p-value
perm.pval = sum(x.perm > rbt.stat) / perm
}
## results
name = "RBT: Replication Based Test"
arg.spec = c(sum(y), length(y)-sum(y), ncol(X), perm)
names(arg.spec) = c("cases", "controls", "variants", "n.perms")
res = list(rbt.stat = rbt.stat,
perm.pval = perm.pval,
args = arg.spec,
name = name)
class(res) = "assoctest"
return(res)
}
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