dpcid (version 1.0)

lshr.cov: Linear shrinkage estimates of covariance and inverse covariance matrix

Description

Linear shrinkage estimates of covariance and inverse covariance matrix.

Usage

lshr.cov(X,scaling=FALSE)

Arguments

X

An observed dataset from a specific condition.

scaling

a logical flag for scaling variable to have unit variance. Default is FALSE.

Value

shr_cov

Linear shrinkage estimate of the covariance matrix.

shr_inv

Linear shrinkage estimate of the inverse covariance matrix.

Details

shr_covp returns the optimal linear shrinkage parameter, the linear shrinkage estimates of the covariance and the precision matrix.

References

Ledoit, O. and M.~Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices, Journal of Multivariate Analysis, 88, 365--411.

Yu, D., Lee, S. H., Lim, J., Xiao, G., Craddock, R. C., and Biswal, B. B. (2018). Fused Lasso Regression for Identifying Differential Correlations in Brain Connectome Graphs. Statistical Analysis and Data Mining, 11, 203--226.

Examples

# NOT RUN {
library(MASS)

## True precision matrix
omega <- matrix(0,5,5)
omega[1,2] <- omega[1,3] <- omega[1,4] <- 1
omega[2,3] <- omega[3,4] <- 1.5
omega <- t(omega) + omega
diag(omega) <- 3

Sig = solve(omega)
X = mvrnorm(50,rep(0,5),Sig)
lshr.cov(X)
# }