mcor {pcalg} | R Documentation |
Compute a correlation matrix, possibly by robust methods, applicable also for the case of a large number of variables.
mcor(dm, method= c("standard", "Qn","QnStable", "ogkScaleTau2", "ogkQn"))
dm |
numeric matrix of data; rows are samples, columns are variables. |
method |
"standard" (default), "Qn", "QnStable" and "ogkQn"
envokes standard,
elementwise robust (based on Q_n scale estimator, see
Qn ) or robust (Qn using OGK, see
covOGK ) correlation estimate
respectively. |
The "standard" method envokes a standard correlation estimator. "Qn" envokes a robust, elementwise correlation estimator based on the Qn scale estimte. "QnStable" also uses the Qn scale estimator, but uses an improved way of transforming that into the correlation estimator. "ogkQn" envokes a correlation estimator based on Qn using OGK.
A correlation matrix estimated according to the specified method.
Markus Kalisch kalisch@stat.math.ethz.ch and Martin Maechler
See those in the help pages for Qn
and covOGK
from package
robustbase.
Qn
) and covOGK
from package robustbase.
pcorOrder
for computing partial correlations and
condIndFisherZ
for testing zero partial correlation.
## produce uncorrelated normal random variables set.seed(42) x <- rnorm(100) y <- 2*x + rnorm(100) ## compute correlation of var1 and var2 mcor(cbind(x,y), method="standard") ## repeat but this time with heavy-tailed noise yNoise <- 2*x + rcauchy(100) mcor(cbind(x,yNoise), method="standard") ## shows almost no correlation mcor(cbind(x,yNoise), method="Qn") ## shows a lot correlation mcor(cbind(x,yNoise), method="QnStable") ## shows a lot correlation mcor(cbind(x,yNoise), method="ogkQn") ## dito