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#'ASUM: Adaptive Sum Statistic
#'
#'The adaptive Adaptive Sum test has been proposed by Han and Pan (2010) in an
#'attempt to overcome some of the drawbacks of the SUM test, by extending the
#'idea of the adaptive Neyman's test (Fan, 1996). The approach behind the
#'adaptive test is to use the U-statistics of the score test (from logistic
#'regression models) in order to construct a statistic with the first
#'components of the score vector U.
#'
#'\code{ASUM} gives the normal (unordered) test. \cr \code{ASUM.Ord} gives the
#'ordered (decreasing) test. \cr
#'
#'There is no imputation for the missing data. Missing values are simply
#'ignored in the computations.
#'
#'@aliases ASUM ASUM.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 asum.stat asum 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{SUM}}
#'@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 ASUM with 500 permutations
#' myasum = ASUM(phenotype, genotype, perm=500)
#' myasum
#'
#' # apply ASUM.Ord with 500 permutations
#' myasumord = ASSU.Ord(phenotype, genotype, perm=500)
#' myasumord
#' }
#'
ASUM <-
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.asum = my_asum_method(U, V)
asum.stat1 = stat.asum[1] # stat normal
p1.asum = stat.asum[2] # pval normal
## permutations
perm.pval = NA
if (perm > 0)
{
p1.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.asum = my_asum_method(U.perm, V)
p1.perm[i] = perm.asum[2]
}
# p-value
perm.pval = sum(p1.perm < p1.asum) / perm # normal
}
## results
name = "ASUM: Adaptive Sum Test"
arg.spec = c(sum(y), length(y)-sum(y), ncol(X), perm)
names(arg.spec) = c("cases", "controls", "variants", "n.perms")
res = list(asum.stat = asum.stat1,
perm.pval = perm.pval,
args = arg.spec,
name = name)
class(res) = "assoctest"
return(res)
}
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