CorShrink2DataLoss: CorShrink Loss method for correlation shrinkage

In kkdey/CorShrink: Adaptive Shrinkage of Correlation and Covariance Matrices

Description

Performs the CorShrink-Loss approach of correlation shrinkage, that uses the Taylor series expansion of the empirical correlation matrix around the population correlation matrix to derive the distribution.

Usage

CorShrink2DataLoss(data_with_missing, alpha = 1)

Arguments

data_with_missing	The samples by features data matrix. May contain NA values.
alpha	The tuning parameter for the L-1 scaling.

Examples

data("sample_by_feature_data")
out = CorShrink2DataLoss(sample_by_feature_data, alpha = 1)
corrplot::corrplot(as.matrix(out), diag = FALSE,
        col = colorRampPalette(c("blue", "white", "red"))(200),
        tl.pos = "td", tl.cex = 0.4, tl.col = "black",
        rect.col = "white",na.label.col = "white",
        method = "color", type = "upper")
out2 = CorShrink2DataLoss(sample_by_feature_data, alpha = 10)
corrplot::corrplot(out2, diag = FALSE,
        col = colorRampPalette(c("blue", "white", "red"))(200),
        tl.pos = "td", tl.cex = 0.4, tl.col = "black",
        rect.col = "white",na.label.col = "white",
        method = "color", type = "upper")

kkdey/CorShrink documentation built on May 20, 2019, 10:28 a.m.