Fast Computation of the Inverse of the Covariance and Correlation Matrix
The functions invcov.shrink
and invcor.shrink
implement an
algorithm to efficiently compute
the inverses of shrinkage estimates of covariance (cov.shrink
)
and correlation (cor.shrink
).
invcov.shrink(x, lambda, lambda.var, w, verbose=TRUE) invcor.shrink(x, lambda, w, verbose=TRUE)
x |
a data matrix |
lambda |
the correlation shrinkage intensity (range 0-1).
If |
lambda.var |
the variance shrinkage intensity (range 0-1).
If |
w |
optional: weights for each data point - if not specified uniform weights are assumed
( |
verbose |
output status while computing (default: TRUE) |
Both invcov.shrink
and invcor.shrink
rely on
powcor.shrink
. This allows to compute the inverses in
a very efficient fashion (much more efficient than directly inverting
the matrices - see the example).
invcov.shrink
returns the inverse of the output from cov.shrink
.
invcor.shrink
returns the inverse of the output from cor.shrink
.
Juliane Sch\"afer and Korbinian Strimmer (https://strimmerlab.github.io).
Sch\"afer, J., and K. Strimmer. 2005. A shrinkage approach to large-scale covariance estimation and implications for functional genomics. Statist. Appl. Genet. Mol. Biol. 4:32. <DOI:10.2202/1544-6115.1175>
# load corpcor library library("corpcor") # generate data matrix p = 500 n = 10 X = matrix(rnorm(n*p), nrow = n, ncol = p) lambda = 0.23 # some arbitrary lambda # slow system.time( (W1 = solve(cov.shrink(X, lambda))) ) # very fast system.time( (W2 = invcov.shrink(X, lambda)) ) # no difference sum((W1-W2)^2)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.