partial_correlation {netassoc} | R Documentation |
Estimates the inverse covariance matrix then uses this matrix to calculate partial correlation coefficents.
Assumes that matrix rows correspond to different variables of interest.
The one exception is if method="correlation"
; see below for details.
partial_correlation(mat, method, verbose=FALSE)
mat |
Input matrix. |
method |
One of the following
|
verbose |
Binary flag determining whether diagnostic output is shown. |
Returns a m x m upper triangular matrix of partial correlation coefficients from an input m x n matrix.
# load highly collinear economic data time series
data(longley)
longley_ss <- t(longley[,c(1:5,7)]) # put data in correct input format
colors <- colorRampPalette(c("red","white","blue"))(10)
pc_shrinkage <- partial_correlation(longley_ss,method="shrinkage")
image(pc_shrinkage,zlim=c(-1,1),col=colors)