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#-----------------------------------------------------------------------------#
# #
# GENERALIZED NETWORK-BASED DIMENSIONALITY REDUCTION AND ANALYSIS (GNDA) #
# #
# Written by: Zsolt T. Kosztyan*, Marcell T. Kurbucz, Attila I. Katona, #
# Zahid Khan #
# *Department of Quantitative Methods #
# University of Pannonia, Hungary #
# kosztyan.zsolt@gtk.uni-pannon.hu #
# #
# Last modified: February 2024 #
#-----------------------------------------------------------------------------#
######## MATRIX-BASED DISTANCE SEMI-PARTIAL CORRELATION ########
#' @export
spdCor<-function(x){
if (!requireNamespace("energy", quietly = TRUE)) {
stop(
"Package \"energy\" must be installed to use this function.",
call. = FALSE
)
}
if (!requireNamespace("MASS", quietly = TRUE)) {
stop(
"Package \"MASS\" must be installed to use this function.",
call. = FALSE
)
}
if (is.data.frame(x))
x <- as.matrix(x)
if (!is.matrix(x))
stop("supply a matrix-like 'x'")
if (!(is.numeric(x) || is.logical(x)))
stop("'x' must be numeric")
stopifnot(is.atomic(x))
# sample number
n <- dim(x)[1]
# given variables' number
gp <- dim(x)[2]-2
# covariance matrix
cvx <- dCov(x)
# inverse covariance matrix
if(det(cvx) < .Machine$double.eps){
warning("The inverse of variance-covariance matrix is calculated using Moore-Penrose generalized matrix invers due to its determinant of zero.")
icvx <- MASS::ginv(cvx)
}else
icvx <- Rfast::spdinv(cvx)
rownames(icvx)<-rownames(cvx)
colnames(icvx)<-colnames(cvx)
# semi-partial correlation
spcor <- -stats::cov2cor(icvx)/sqrt(diag(cvx))/sqrt(abs(diag(icvx)-t(t(icvx^2)/diag(icvx))))
diag(spcor) <- 1
spdCor<-spcor
spdCor
}
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