#'ASCORE: Adaptive Score Test
#'
#'The Adaptive Score test has been proposed by Hand and Pan (2010) in an
#'attempt to overcome some of the drawbacks of the Score test (from logistic
#'regression) by extending the idea of the adaptive Neymans test. The approach
#'behind the adaptive test is to use the the first components of the U-score
#'vector in order to construct a test statistic
#'
#'\code{ASCORE} gives the normal (unordered) test. \cr \code{ASCORE.Ord} gives
#'the ordered (decreasing) test. \cr
#'
#'There is no imputation for the missing data. Missing values are simply
#'ignored in the computations.
#'
#'@aliases ASCORE ASCORE.Ord
#'@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 ascore.stat ascore 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{SCORE}}
#'@references Han F, Pan W (2010) A Data-Adaptive Sum Test for Disease
#'Association with Multiple Common or Rare Variants. \emph{Human Heredity},
#'\bold{70}: 42-54 \cr
#'
#'Pan W, Shen X (2011) Adaptive Tests for Association of Rare Variants.
#'\emph{Genetic Epidemiology}, \bold{35}: 381-388
#'@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 ASCORE with 500 permutations
#' myascore = ASCORE(phenotype, genotype, perm=500)
#' myascore
#'
#' # apply ASCORE.Ord with 500 permutations
#' myascoreord = ASCORE.Ord(phenotype, genotype, perm=500)
#' myascoreord
#' }
#'
ASCORE <-
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$perm
## get U and V
getuv = my_getUV(y, X)
U = getuv$U
V = getuv$V
## run score method
stat.sco = my_ascore_method(U, V)
score.stat = stat.sco[1]
## permutations
perm.pval = NA
if (perm > 0)
{
x.perm = rep(0, perm)
ymean = mean(y)
for (i in 1:perm)
{
perm.sample = sample(1:length(y))
# center phenotype y
y.perm = y[perm.sample] - ymean
# get score vector
U.perm = colSums(y.perm * X, na.rm=TRUE)
perm.sco = my_ascore_method(U.perm, V)
x.perm[i] = perm.sco[1]
}
# p-value
perm.pval = sum(x.perm > score.stat) / perm
}
## results
name = "ASCORE: Adaptive Score Test"
arg.spec = c(sum(y), length(y)-sum(y), ncol(X), perm)
names(arg.spec) = c("cases", "controls", "variants", "n.perms")
res = list(ascore.stat = score.stat,
perm.pval = perm.pval,
args = arg.spec,
name = name)
class(res) = "assoctest"
return(res)
}
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