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#' Get the p-values of the multiple SNPs based on the Wald test in a logistic regresion model.
#'
#' Get p-value using logistic regresion for each of the multiple SNPs
#'
#' @param Y Response or phenotype data. It can be a disease indicator; =0 for controls, =1 for cases.
#'
#' @param X Genotype or other data; each row for a subject, and each column
#' for an SNP (or a predictor). The value of each SNP is the # of the copies
#' for an allele. A matrix with dimension n by p (n : number of observation, p : number of SNPs (or predictors) ).
#'
#' @return p-values for each SNPs.
#'
#' @examples
#'
#' #simula <- simPathAR1Snp(nGenes=20, nGenes1=1, nSNPlim=c(1, 20), nSNP0=1:3,
#' # LOR=.2, rholim=c(0,0),
#' # n=10, MAFlim=c(0.05, 0.4), p0=0.05)
#' #logitp <- getlogitp(simula$Y, simula$X)
#'
#'
#' @seealso \code{\link{GATES2}} \code{\link{GatesSimes}} \code{\link{Hyst}}
#'
#' @export
getlogitp <- function(Y, X) {
n.Y <- length(Y)
nc.X <- ncol(X)
logitPs <- NULL
for(i in 1:nc.X) { # i =1
tdat1<-data.frame(trait=Y, X = X[,i])
fit1<-glm(trait~.,family="binomial",data=tdat1)
logitPs <- c(logitPs, summary(fit1)$coeff[2,4])
}
logitPs
}
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