generate: Data simulation

In meszre/tauPFC: Computes Robust Estimators for the PFC Model

Description

Generates samples following the Principal Fitted Components model with r = 2, d = 1 and covariance matrix σ² times the p x p identity matrix (for more details see Bergesio et al. (2020)). The simulated model has covariate Fy = (y,y²)^t, where y has uniform distribution in the (0,4) interval.

Usage

generate(p, n, mutrue, gammatrue, betatrue, sigmatrue)

Arguments

p	dimension of the response vector in the inverse model
n	size of the sample required
mutrue	vector of dimension p with the true value of parameter μ
gammatrue	matrix p x d with the true value of parameter Γ
betatrue	matrix d x r, with d=1, containing the true value of the parameter β
sigmatrue	constant standard deviation of the error term

Value

List with the following components

X	n x p matrix, each row is a response vector
Fy	n x r matrix, each row is the corresponding covariate vector

References

Bergesio, A., Szretter Noste, M. E. and Yohai, V. J. (2020). A robust proposal of estimation for the sufficient dimension reduction problem

Examples

require(MASS)
p=10
generate(p,n=1,mutrue=rep(0,p),gammatrue=as.matrix(c(1,rep(0,p-1))),
betatrue=t(as.matrix(c(1,1))),sigmatrue=1)

meszre/tauPFC documentation built on Feb. 28, 2020, 8:21 a.m.