IPOD: Iterative penalized outlier detection algorithm

Description Usage Arguments Details Value Author(s)

View source: R/leapp.R

Description

Outlier detection and robust regression through an iterative penalized regression with tuning parameter chosen by modified BIC

Usage

1
IPOD(X, Y, H, method = "hard", TOL = 1e-04, length.out = 50)

Arguments

X

an N by k design matrix

Y

an N by 1 response

H

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

method

a string, if method = "hard", hard thresholding is applied; if method = "soft", soft thresholding is applied

TOL

relative iterative converence tolerance, default to 1e-04

length.out

A numeric, number of candidate tuning parameter lambda under consideration for further modified BIC model selection, default to 50.

Details

If there is no predictors, set X = NULL.

Y = X beta + gamma + sigma epsilon

Y is N by 1 reponse vector, X is N by k design matrix, beta is k by 1 coefficients, gamma is N by 1 outlier indicator, sigma is a scalar and the noise standard deviation and epsilon is N by 1 vector with components independently distributed as standard normal N(0,1).

Value

gamma

a vector of length N, estimated outlier indicator gamma

resOpt.scale

a vector of length N, test statistics for each of the N genes

p

a vector of length N, p-values for each of the N genes

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.