| 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","shrink"))
dm |
numeric matrix of data; rows are samples, columns are variables. |
method |
"standard" (default), "Qn", "QnStable", "ogkQn" and "shrink"
envokes standard,
elementwise robust (based on Q_n scale estimator, see
Qn), robust (Qn using OGK, see
covOGK) or shrinked ()
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. "shrink" is only useful when used wiht pcSelect. An optimal shrinkage parameter is used. Only correlation between response and covariates is shrinked.
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