#' Calculate C-chisq
#'
#' @description \code{pairwise_c_chisq} returns the pairwise C-score using
#' the chi-square approach. Setting na.rm = T assumes that missing values
#' are true negatives.
#'
#' @param input numeric array
#' @param num_permute numeric
#' @param na.rm boolean
#'
#' @return length-one numeric
#'
#' @examples
#' array1 <- array (0,c(5000,20))
#' array1[cbind(1:20,1:20)] <- 1
#' array1[1:5,1:3] <- 1
#' array1[6:10,4:6] <- 1
#' pairwise_c_chisq (array1)
#'
#' @export
pairwise_c_chisq <- function (input,num_permute = 10000,na.rm = F){
numcol <- ncol (input)
c_score <- array(NA, c(numcol,numcol))
for (loop1 in 1:(numcol-1)){
for (loop2 in (loop1+1):numcol){
results_chisq <- array (NA,num_permute)
input_sub <- as.matrix (input[,c(loop1,loop2)])
obs1 <- rowSums (input_sub,na.rm = na.rm)
exp1 <- array (mean(obs1), length(obs1))
chisq1 <- (obs1 - exp1)^2 / exp1
for (loop3 in 1:num_permute){
input_sub[,1] <- sample (input_sub[,1],nrow (input_sub),replace = F)
input_sub[,2] <- sample (input_sub[,2],nrow (input_sub),replace = F)
obs2 <- rowSums(input_sub,na.rm = na.rm)
exp2 <- array (mean(obs2),length(obs2))
chisq2 <- (obs2 - exp2)^2/exp2
results_chisq[loop3] <- sum (chisq2)
}
c_score [loop1,loop2] <- (sum (chisq1) - mean (results_chisq)) / sd (results_chisq)
}
}
mean(c_score,na.rm = T)
}
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