Nothing
skewIPEC <- function( expr, theta, x, y, tol=sqrt(.Machine$double.eps), method = "Richardson",
method.args = list(eps = 1e-04, d = 0.11, zero.tol = sqrt(.Machine$double.eps/7e-07),
r = 6, v = 2, show.details = FALSE), side = NULL )
{
x <- rbind( x )
y <- as.vector(y)
if( min(dim(x))[1] ==1 ) x <- cbind( x )
if( nrow(x) != length(y) ) x <- t(x)
n <- nrow(x)
p <- length(theta)
yhat <- expr(theta, x)
sigma <- sqrt(sum((y - yhat)^2)/(n - p))
v1 <- matrix(NA, nrow = n, ncol = p)
v2 <- array(NA, c(p, p, n))
for (k in 1L:n) {
resu <- derivIPEC(expr, theta, x[k, ], method = method,
method.args = method.args, side = side)
v1[k, ] <- resu$Jacobian
v2[, , k] <- resu$Hessian
}
# L is a matrix with p rows and p columns
L <- t(v1) %*% v1
L <- ginv(L, tol=tol)
W <- array(NA, c(p, p, p))
for (k1 in 1L:p) {
for (k2 in 1L:p){
for (k3 in 1L:p){
temp.sum <- 0
for(k4 in 1L:n){
temp.val <- sum( v1[k4, k1] * v2[k2, k3, k4] )
temp.sum <- temp.sum + temp.val
}
W[k2, k3, k1] <- temp.sum
}
}
}
numer <- c()
for (k4 in 1:p){
mysum <- 0
for (k1 in 1:p){
for (k2 in 1:p){
for (k3 in 1:p){
mysum <- mysum + L[k4, k1] * L[k4, k2] * L[k4, k3] * (
W[k2,k3,k1] + W[k1,k3,k2] + W[k2,k1,k3])
}
}
}
numer[k4] <- -sigma^4 * mysum
}
denomi <- ( sigma^2 * diag(L) )^(3/2)
g <- numer / denomi
list(skewness = g)
}
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