| IPOD {leapp} | R Documentation |
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 |
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