secure.sim {secure}

Simulation model

Description

genertate random samples from a sparse factor regression model

Usage

secure.sim(U, D, V, n, snr, Xsigma, rho = 0)

Arguments

U

specified value of U

D

specified value of D

V

specified value of V

n

sample size

snr

signal to noise ratio

Xsigma

covariance matrix for generating sample of X

rho

parameter defining correlated error

Value

Y

Generated response matrix

X

Generated predictor matrix

Examples

#require(secure)

# Simulate data from a sparse factor regression model
p <- 100; q <- 50; n <- 300
snr <- 0.5; ssigma <- 0.5; nlambda <- 200 
nrank <- 3

U <- matrix(0,ncol=nrank ,nrow=p);  V <- matrix(0,ncol=nrank ,nrow=q)
U[,1]<-c(sample(c(1,-1),8,replace=TRUE),rep(0,p-8))
U[,2]<-c(rep(0,5),sample(c(1,-1),9,replace=TRUE),rep(0,p-14))
U[,3]<-c(rep(0,11),sample(c(1,-1),9,replace=TRUE),rep(0,p-20))
V[,1]<-c(sample(c(1,-1),5,replace=TRUE)*runif(5,0.3,1),rep(0,q-5))
V[,2]<-c(rep(0,5),sample(c(1,-1),5,replace=TRUE)*runif(5,0.3,1),rep(0,q-10))
V[,3]<-c(rep(0,10),sample(c(1,-1),5,replace=TRUE)*runif(5,0.3,1),rep(0,q-15))
U[,1:3]<- apply(U[,1:3],2,function(x)x/sqrt(sum(x^2)))
V[,1:3]<- apply(V[,1:3],2,function(x)x/sqrt(sum(x^2)))
D <- diag(c(20,15,10)) 
C <- U%*%D%*%t(V)

Xsigma <- ssigma^abs(outer(1:p, 1:p,FUN="-"))
sim.sample <- secure.sim(U,D,V,n,snr,Xsigma)
Y <- sim.sample$Y
X <- sim.sample$X