| mcor | 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 data matrix; rows are observiations (“samples”), columns are variables. | 
| method | a string;  | 
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 with pcSelect. An optimal
shrinkage parameter is used. Only correlation between response and
covariates is shrinked.
A correlation matrix estimated by 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.
## 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 still much correlation
mcor(cbind(x,yNoise), method="ogkQn")    ## ditto
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