PIE: Main function for the Penalized Interaction Estimation (PIE)...

In cescwang85/PIE: Penalized Interaction Estimation for Ultrahigh Dimensional Quadratic Regression

Main function for the Penalized Interaction Estimation (PIE) where the tuning parameter is chosen by BIC

Description

Main function for the Penalized Interaction Estimation (PIE) where the tuning parameter is chosen by BIC

Usage

PIE(X, Y, lambda = NULL, nlambda = 50, lambda.min.ratio = 0.1,
  err = 1e-04, maxIter = 1000, rho = 1)

Arguments

X	covariates data matrix of dimension n*p after centering.
Y	response variable with length n.
lambda	user supplied tuning parameter; Default is NULL and the program compute its own sequence based on nlambda.
nlambda	the length of the tuning parameter sequence which is available when lambda is NULL. Default is 50.
lambda.min.ratio	smallest value for lambda, as a fraction of lambda.max which is available when lambda is NULL. Default is 0.1.
err	the precision used to stop the convergence. Default is 1e-4.
maxIter	Maximum number of iterations. Default is 1000.
rho	step parameter for the ADMM. Default is 1.

Value

A sparse asymmetric p*p matrix for the interactions.

Examples

rm(list = ls())
library('PIE')
library('glmnet')
set.seed(99)
p=100
n=200;
Omega=matrix(0,nrow=p,ncol=p);
Omega[6,6]=1
Omega[1,6]=2;Omega[6,10]=2;Omega=(Omega+t(Omega))/2;
beta<-rep(0,p);beta[c(1,6,10)]=1;
X=matrix(rnorm(n*p),nrow =n);
Y=as.vector(diag(X%*%Omega%*%t(X))+X%*%beta+rnorm(n));
yOme<-PIE(X,Y)
hbeta<-as.vector(coef(cv.glmnet(X,Y,nfolds =5),s="lambda.min"))[-1];  
rOme<-PIE(X,Y-X%*%hbeta)
yOme[1:10,1:10]
rOme[1:10,1:10]

cescwang85/PIE documentation built on Sept. 10, 2022, 3:28 p.m.