#' Reliability Density Function
#'
#' A function for computing the reliability density function of a dataset.
#'
#' @param dist [n, n]: a distance matrix for n subjects.
#' @param ids [n]: a vector containing the subject ids for each subject.
#' @return rdf [n]: the reliability per subject.
#' @author Shangsi Wang, Eric Bridgeford and Gregory Kiar
#' @export
discr.rdf <- function(dist, ids) {
N <- dim(dist)[1]
if (is.null((N))) {
stop('Invalid datatype for N')
}
uniqids <- unique(as.character(ids))
countvec <- vector(mode="numeric",length=length(uniqids))
for (i in 1:length(uniqids)) {
countvec[i] <- sum(uniqids[i] == ids) # total number of scans for the particular id
}
scans <- max(countvec) # assume that we will worst case have the most
rdf <- array(NaN, N*(scans-1)) # initialize empty ra
count <- 1
for (i in 1:N) {
ind <- which(ids[i] == ids) # all the indices that are the same subject, but different scan
for (j in ind) {
if (!isTRUE(all.equal(j, i))) { # if j != i, then we want j to have a close distance to i, and estimate where it ranks
di <- dist[i,] # get the entire ra for the particular scan
di[ind] <- Inf # don't want to consider the particular scan itself
d <- dist[i,j] # the distance between the particular scan of a subject and another scan of the subject
rdf[count] <- 1 - (sum(di[!is.nan(di)] < d) + 0.5*sum(di[!is.nan(di)] == d)) / (N-length(ind)) # 1 for less than, .5 if equal, then average
count <- count + 1
}
}
}
return(rdf[1:count-1]) # return only the occupied portion
}
#' Discrmininability
#'
#' A function for computing the discriminability.
#'
#' @param rdf [n]: the reliability density function.
#' @param remove_outliers=TRUE boolean indicating whether to ignore subjects with rdf below a certain cutoff.
#' @param thresh=0 [1]: the threshold below which to ignore subjects.
#' @param output=FALSE a boolean indicating whether to ignore output.
#' @return discr [1]: the discriminability statistic.
#' @author Eric Bridgeford and Gregory Kiar
#' @export
discr.discr <- function(rdf, remove_outliers=TRUE, thresh=0, output=FALSE) {
if (remove_outliers) {
discr <- mean(rdf[which(rdf[!is.nan(rdf)] > thresh)]) # mean of the rdf
ol <- length(which(rdf<thresh))
if (output) {
print(paste('Graphs with reliability <',thresh,'(outliers):', ol))
}
} else {
ol <- 0
discr <- mean(rdf[!is.nan(rdf)])
}
nopair <- length(rdf[is.nan(rdf)])
if (output) {
print(paste('Graphs with unique ids:',nopair))
print(paste('Graphs available for reliability analysis:', length(rdf)-ol-nopair))
print(paste('discr:', discr))
}
return(discr)
}
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