Nothing
compute.beta.hat <- function(x, M){
l <- lm(x ~ M - 1)
return(as.numeric(l$coef))
}
compute.z <- function(x, M, beta=NULL){
if (!is.null(beta)) {
z <- x - M %*% beta
} else { # in that case, z depends on the MLE estimate of beta (which is useless to compute here)
Q <- qr.Q(qr(M))
H <- Q %*% t(Q)
z <- x - H %*% x
}
return(as.numeric(z))
}
compute.sigma2.hat <- function(z){
drop(crossprod(z)/length(z))
}
computeAuxVariables <- function(model) {
aux <- covMatrix(model@covariance, X=model@X, noise.var=model@noise.var)
C <- aux[[1]]
T <- chol(C)
x <- backsolve(t(T), model@y, upper.tri = FALSE)
M <- backsolve(t(T), model@F, upper.tri = FALSE)
model@T <- T
model@M <- M
if (length(model@trend.coef)>0){
z <- backsolve(t(T), model@y-model@F%*%as.matrix(model@trend.coef), upper.tri=FALSE)
model@z <- as.numeric(z)
} else {
model@z <- numeric(0)
}
return(model)
}
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