ridge

an N by k design matrix

an N by 1 response vector

an N by N projection matrix <code>X(X'X)^{-1}X'</code>

a numeric, noise standard deviation

sigma

Outlier detection and robust regression with a ridge type penalty on the outlier indicator gamma. Allow non sparse outliers and require known noise standard deviation.

misc

These functions take a gene expression value matrix, a
primary covariate vector, an additional known covariates
matrix.  A two stage analysis is applied to counter the effects
of latent variables on the rankings of hypotheses.  The
estimation and adjustment of latent effects are proposed by
Sun, Zhang and Owen (2011).  "leapp" is developed in the
context of microarray experiments, but may be used as a general
tool for high throughput data sets where dependence may be
involved.

ridge: Outlier detection with a ridge penalty

Description

Usage

Arguments

Value