Nothing
if (!file.exists("test-dataset.Rdata")) {
library(robustvarComp)
if (require(mvtnorm) & require(Matrix)) {
ones <- function (n, p = n)
matrix(rep(1, n * p), nrow = n, ncol = p)
size <- 20
i <- 2
j <- 2
n <- 3
p <- i*j*n ## number of columns of Y
k <- 5 ## number of beta parameters
R <- 3 ## number of gamma parameters
beta <- c(0,rep(2,k)) ## values beta parameters k+1 parameters,
## the first the intercept
gamma <- c(1,1,2)/4 ## values gamma parameters
eta0 <- 1/4 ## value of eta_0
## recall that the vector eta is eta_0 * gamma
V <- array(0, dim=c(p,p,R)) ## V_r matrix/ces
## (just one in this example)
## with dimension pXp
V[,,1] <- V1 <- diag(i)%x%ones(j)%x%ones(n)
V[,,2] <- V2 <- ones(i)%x%diag(j)%x%ones(n)
V[,,3] <- V3 <- diag(i)%x%diag(j)%x%ones(n)
S <- eta0*(robustvarComp::Vprod(V, gamma)) ## Variance and Covariance matrix of Y
## i.e. \eta_0 (V_0+\sum_{r=1}^{R}\gamma_{0r} V_{r})
## where V_0 = I_0
## V0 is added in Vprod function automatically
VV1 <- VV2 <- VV3 <- list()
for (N in 1:size) {
VV1[[N]] <- V1
VV2[[N]] <- V2
VV3[[N]] <- V3
}
v1 <- as.matrix(bdiag(VV1))
v2 <- as.matrix(bdiag(VV2))
v3 <- as.matrix(bdiag(VV3))
set.seed(1234)
x <- array(0, dim=c(p,size,k+1))
for (N in 1:size)
x[,N,] <- cbind(rep(1,p), matrix(rnorm(p*k, mean=0, sd=1), p, k))
e <- rmvnorm(n=size, mean=rep(0, p), sigma=S)
y <- t(e)+robustvarComp::xprod(x, beta) ## the Y observations
Y <- as.vector(y)
X <- matrix(nrow=0, ncol=k+1)
for (N in 1:size) {
X <- rbind(X, x[,N,])
}
dati <- data.frame(Y=c(Y), X=X)
test.control <- varComprob.control()
test.init.control <- varComprob.control(cov.init="covOGK")
save(beta, gamma, eta0, y, x, V, Y, X, v1, v2, v3, n, p, k, R, dati, test.control, test.init.control, file="test-dataset.Rdata")
}
}
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