#' Evaluate the associations (1 - chi-square or conditional proability) between several pairs of categorical variables.
#' @param x Dataframe or Tibble. Table containing the categorical variables for which the association should be computed.
#' @return A tibble indicating the proportion of missing values per variable.
#' @importFrom stats chisq.test
#' @importFrom stats ftable
#' @importFrom purrr map_lgl
#' @export
datexp_assocat <- function(x){
# Select categorical variables
x <- x[, map_lgl(x, is.numeric)==FALSE]
# Prepare the association matrix
matrix <- as.data.frame(matrix(nrow = length(x), ncol = length(x)))
names(matrix) <- names(x)
row.names(matrix) <- names(x)
# Fill in the matrix with 1 - the result of the chi-square test
for (i in 1:length(x)){
for (j in i:length(x)){
test <- round(1-chisq.test(ftable(x[,c(i,j)]),
simulate.p.value = TRUE,
B = 100)$p.value,3)
matrix[j,i] <- test
matrix[i,j] <- test
}
}
matrix
}
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