#'SEQSUM: Sequential Sum Score Test
#'
#'SEQSUM has been proposed by Basu and Pan (2011) as a modification of the Sum
#'test based on a model selection approach, following a similar philosophy as
#'the CARV and RARECOVER methods. Assuming that there are M variants, the main
#'idea behind the Sequential Sum test is to associate a sign to each variant
#'indicating whether it has a positive effect or a negative effect. In other
#'words, the purpose is to give signs to the variants so they reflect their
#'effect (positive or negative).
#'
#'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 seqsum.stat seqsum statistic
#'@returnItem perm.pval permuted p-value
#'@returnItem signs a numeric vector with signs for the variants (1=positive,
#'-1=negative)
#'@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}}, \code{\link{SUM}}
#'@references Basu S, Pan W (2011) Comparison of Statistical Tests for Disease
#'Association with Rare Variants. \emph{Genetic Epidemiology}, \bold{35}:
#'606-619 \cr
#'@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 SEQSUM with 500 permutations
#' myseq = SEQSUM(phenotype, genotype, perm=500)
#' myseq
#' }
#'
SEQSUM <-
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
## vector for storing stastitic and signs
seqsum.stat = rep(0, ncol(X))
signs = rep(-1, ncol(X))
## start with first variant
X.new = X
## positive effect
seqsum.stat = my_uni_score(y, rowSums(X.new, na.rm=TRUE))
## negative effect
X.new[,1] = (-1) * X[,1]
aux.neg = my_uni_score(y, rowSums(X.new, na.rm=TRUE))
## who is the best
if (seqsum.stat > aux.neg)
{
X.new[,1] = X[,1]
signs[1] = 1
} else seqsum.stat = aux.neg
## continue with the other variants
for (j in 2:ncol(X))
{
## negative effect model
X.new[,j] = (-1) * X[,j]
aux.neg = my_uni_score(y, rowSums(X.new, na.rm=TRUE))
# who is the best?
if (seqsum.stat > aux.neg)
{
X.new[,j] = X[,j]
signs[j] = 1
} else seqsum.stat = aux.neg
}
## permutations
perm.pval = NA
if (perm > 0)
{
x.perm = rep(0, perm)
for (i in 1:perm)
{
perm.sample = sample(1:length(y))
y.perm = y[perm.sample]
## start with first variant
X.new = X
perm.seqsum = my_uni_score(y.perm, rowSums(X.new, na.rm=TRUE))
## negative effect
X.new[,1] = (-1) * X[,1]
aux.neg = my_uni_score(y.perm, rowSums(X.new, na.rm=TRUE))
if (perm.seqsum > aux.neg)
{
X.new[,1] = X[,1]
} else perm.seqsum = aux.neg
## continue with the other variants
for (j in 2:ncol(X))
{
X.new[,j] = (-1) * X[,j]
aux.neg = my_uni_score(y.perm, rowSums(X.new, na.rm=TRUE))
if (perm.seqsum > aux.neg)
{
X.new[,j] = X[,j]
} else perm.seqsum = aux.neg
}
x.perm[i] = perm.seqsum
}
# p-value
perm.pval = sum(x.perm > seqsum.stat) / perm
}
## results
name = "SEQSUM: Sequential 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(seqsum.stat = seqsum.stat,
perm.pval = perm.pval,
signs = signs,
args = arg.spec,
name = name)
class(res) = "assoctest"
return(res)
}
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