2019-12-16 15:10:56 +01:00
|
|
|
#' @title Learn partition (modified)
|
|
|
|
|
#' @param ordered ordered
|
|
|
|
|
#' @return part
|
|
|
|
|
#' @description This function is called only if some individual has less than
|
|
|
|
|
#' 90 per cent non-missing data. The function uses fuzzy clustering for the
|
|
|
|
|
#' "non-missingness" values, finding maximum three clusters. If two of the
|
|
|
|
|
#' found clusters are such that all the values are >0.9, then those two are
|
|
|
|
|
#' further combined.
|
|
|
|
|
learn_partition_modified <- function(ordered) {
|
|
|
|
|
part <- learn_simple_partition(ordered, 0.05)
|
|
|
|
|
nclust <- length(unique(part))
|
|
|
|
|
if (nclust == 3) {
|
2019-12-16 16:56:41 +01:00
|
|
|
mini_1 <- min(ordered(which(part == 1)))
|
2019-12-16 15:10:56 +01:00
|
|
|
mini_2 <- min(ordered(which(part == 2)))
|
|
|
|
|
mini_3 <- min(ordered(which(part == 3)))
|
|
|
|
|
if (mini_1 > 0.9 & mini_2 > 0.9) {
|
|
|
|
|
part[part == 2] <- 1
|
|
|
|
|
part[part == 3] <- 2
|
|
|
|
|
} else if (mini_1 > 0.9 & mini_3 > 0.9) {
|
|
|
|
|
part[part == 3] <- 1
|
|
|
|
|
} else if (mini_2 > 0.9 & mini_3 > 0.9) {
|
|
|
|
|
# This is the one happening in practice, since the values are
|
|
|
|
|
# ordered, leading to mini_1 <= mini_2 <= mini_3
|
|
|
|
|
part[part == 3] <- 2
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
return(part)
|
|
|
|
|
}
|
