generate_data: generate response from nonparametric additive model

In kko: Kernel Knockoffs Selection for Nonparametric Additive Models

Description

The function generate response from additive models of various components.

Usage

generate_data(X, reg_coef, model = "linear", err_sd = 1)

Arguments

X	design matrix of additive model; rows are observations and columns are variables.
reg_coef	regression coefficient vector.
model	types of components. Default is "linear". Other choices are linear linear regression. poly polynomial of degree sampled from 2 to 4. sinpoly sum of polynomial of sin and cos. sinratio ratio of sin. sinmix sampled from poly and sinratio.
err_sd	standard deviation of regression error.

Value

reponse vector

Author(s)

Xiaowu Dai, Xiang Lyu, Lexin Li

Examples

p=5 # number of predictors
s=2  # sparsity, number of nonzero component functions
sig_mag=100 # signal strength
n= 200 # sample size
model="poly" # component function type
X=matrix(rnorm(n*p),n,p) %*%chol(toeplitz(0.3^(0:(p-1))))   # generate design
reg_coef=c(rep(1,s),rep(0,p-s))  # regression coefficient
reg_coef=reg_coef*(2*(rnorm(p)>0)-1)*sig_mag
y=generate_data(X,reg_coef,model) # reponse vector

kko documentation built on Feb. 1, 2022, 5:08 p.m.