Compute (Large) Correlation Matrix
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
## 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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