ridge: Outlier detection with a ridge penalty

Description Usage Arguments Value Author(s)

View source: R/leapp.R

Description

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.

Usage

1
ridge(X, Y, H, sigma)

Arguments

X

an N by k design matrix

Y

an N by 1 response vector

H

an N by N projection matrix X(X'X)^{-1}X'

sigma

a numeric, noise standard deviation

Value

p

an N by 1 vector of p-values for each of the N genes

gamma

an N by 1 vector of estimated primary variable gamma

Author(s)

Yunting Sun yunting.sun@gmail.com, Nancy R.Zhang nzhang@stanford.edu, Art B.Owen owen@stanford.edu


leapp documentation built on May 2, 2019, 2:12 p.m.