Nothing
pred.penv <- function(m, X1new, X2new) {
if(is.null(m$ratio)) stop("The asymptotic distribution part of penv is missing. Rerun penv with asy = T")
r <- ncol(m$Sigma)
n <- m$n
if (is.null(m$Gamma)) {
u <- 0
} else {
u <- ncol(m$Gamma)
}
if (u == 0) {
value <- m$mu + m$beta2 %*% X2new
temp <- kronecker(X2new, diag(r))
temp2 <- crossprod(temp, m$covMatrix) %*% temp / n
covMatrix.estm <- m$Sigma / n + temp2
covMatrix.pred <- (1 + 1 / n) * m$Sigma + temp2
} else {
X1new <- as.matrix(X1new)
X2new <- as.matrix(X2new)
if (nrow(X1new) == 1) X1new <- t(X1new)
if (nrow(X2new) == 1) X2new <- t(X2new)
Xnew <- rbind(X1new, X2new)
value <- m$mu + m$beta1 %*% X1new + m$beta2 %*% X2new
temp <- kronecker(Xnew, diag(r))
covMatrix.estm <- m$Sigma / n + crossprod(temp, m$covMatrix) %*% temp / n
covMatrix.pred <- (1 + 1 / n) * m$Sigma + crossprod(temp, m$covMatrix) %*% temp / n
}
SE.estm <- sqrt(diag(covMatrix.estm))
SE.pred <- sqrt(diag(covMatrix.pred))
return(
list(
value = value, covMatrix.estm = covMatrix.estm, SE.estm = SE.estm, covMatrix.pred = covMatrix.pred, SE.pred = SE.pred
)
)
}
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