Bias-corrected Bayesian classification initial state

Generate initial Markov chain state with Bias-corrected Bayesian classification.

Usage

bcbcsf_deltas(X, y, alpha = 0)

Arguments

X

Design matrix of traning data; rows should be for the cases, and columns for different features.

y

Vector of class labels in training or test data set. Must be coded as non-negative integers, e.g., 1,2,...,C for C classes.

alpha

The regularization proportion (between 0 and 1) for mixing the diagonal covariance estimates and the sample covariance estimated with the training samples. The default is 0, the covariance matrix is assumed to be diagonal, which is the most robust.

Value

A matrix - the initial state of Markov Chain for HTLR model fitting.

Details

Caveat: This method can be used only for continuous predictors such as gene expression profiles, and it does not make sense for categorical predictors such as SNP profiles.

References

Longhai Li (2012). Bias-corrected hierarchical Bayesian classification with a selected subset of high-dimensional features. Journal of the American Statistical Association, 107(497), 120-134.

See also

lasso_deltas