sailsim: Simulated Data Used in Bhatnagar et al. (2018+) Paper

In sahirbhatnagar/sail: Sparse Additive Interaction Learning

Description

A dataset containing simulated data used in the accompanying paper to this package

Usage

sailsim

Format

A list with 7 elements:

x

a matrix of dimension 100 x 20 where rows are observations and columns are predictors

y

a numeric response vector of length 100

e

a numeric exposure vector of length 100

f1,f2,f3,f4

the true functions

Details

The code used to simulate the data is available at https://github.com/sahirbhatnagar/sail/blob/master/data-raw/SIMULATED_data.R. See gendata for more details. The true model is given by

Y = f1(X1) + f2(X2) + f3(X3) + f4(X4) + E * (2 + f3(X3) + f4(X4))

where

f1(t)=5t

f2(t)=3(2t - 1)^2

f3(t)= 4sin(2pi*t) / (2-sin(2pi*t)

f4(t)=6(0.1sin(2pi*t) + 0.2cos(2pi*t) + 0.3sin(2pi*t)^2 + 0.4cos(2pi*t)^3 + 0.5sin(2pi*t)^3)

References

Lin, Y., & Zhang, H. H. (2006). Component selection and smoothing in multivariate nonparametric regression. The Annals of Statistics, 34(5), 2272-2297.

Huang J, Horowitz JL, Wei F. Variable selection in nonparametric additive models (2010). Annals of statistics. Aug 1;38(4):2282.

Bhatnagar SR, Yang Y, Greenwood CMT. Sparse additive interaction models with the strong heredity property (2018+). Preprint.

Examples

sailsim

sahirbhatnagar/sail documentation built on July 17, 2021, 5:10 a.m.