# secure.sim: Simulation model In secure: Sequential Co-Sparse Factor Regression

## Description

genertate random samples from a sparse factor regression model

## Usage

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```#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 ```

secure documentation built on May 2, 2019, 5:58 a.m.