IPOD: Iterative penalized outlier detection algorithm

In leapp: Latent Effect Adjustment After Primary Projection

Iterative penalized outlier detection algorithm

Description

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

Usage

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 [e-mail], Nancy R.Zhang [e-mail], Art B.Owen [e-mail]

leapp documentation built on June 20, 2022, 1:05 a.m.