README.md

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

PIE

Penalized Interaction Estimation(PIE) for high dimensional quadratic regression

Reference

Wang, Cheng, Binyan Jiang, and Liping Zhu. "Penalized Interaction Estimation for Ultrahigh Dimensional Quadratic Regression." arXiv preprint arXiv:1901.07147 (2019).

Getting Started

These instructions will give you a toy example for implementing the package.

Prerequisites

What things you need to install the software and how to install them

install.packages("MASS")
install.packages("Rcpp")
install.packages("Matrix")
install.packages("devtools")

Install PIE

library("devtools")
devtools::install_github("cescwang85/PIE")

Toy example

#!/usr/local/bin/Rscript
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));
##Centering
## PIE basd on the response
yOme<-PIE(X,Y)
##PIE based on the residual
hbeta<-as.vector(coef(cv.glmnet(X,Y,nfolds =5),s="lambda.min"))[-1];  
rOme<-PIE(X,Y-X%*%hbeta)

##Show the estimation
yOme[1:10,1:10]
rOme[1:10,1:10]

Overall, the computation complexity for each iteration of the algorithm is linear in both the sample size(n) and the number of parameters (p^2). Welcome any comments or suggesiongs.

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