aimer: aimer: Amplified, Initially Marginal, Eigenvector Regression

Description Details Estimation functions Selecting tuning parameters

Description

The aimer package implements the aimer algorithm as described in Ding and McDonald (2017). The main goal is to use marginal regression to select a subset of coefficients, use matrix approximation to estimate the principal components, and then extend those estimates to the original predictor space before thresholding.

Details

The package uses fast C++ routines to quickly perform matrix computations and cross validation for selection of tuning parameters.

The package provides functions for estimating the model, choosing tuning parameters, and generating simulated data as well as methods for prediction, plotting, and extraction.

Estimation functions

raimer() Perform Amplified, Initially Marginal, Eigenvector Regression (AIMER) for fixed t, b, and d.

Selecting tuning parameters

findThresholdSelect() Find Optimal Threshold for Amplified, Initially Marginal, Eigenvector Regression (AIMER) With Further Selection


dajmcdon/aimer documentation built on May 6, 2019, 1:31 a.m.