#require(MASS)
dd <- d + d^2 + d^3
W <<- MASS::ginv( matrix( .C("Compute_Omega",
- X=as.double(X), Y=as.double(Y), M=as.double(Moments(θ)),
- pn=as.integer(n), pd=as.integer(d),
+ X=as.double(X), Y=Y, M=Moments(θ), pn=as.integer(n), pd=as.integer(d),
W=as.double(W), PACKAGE="morpheus")$W, nrow=dd, ncol=dd ) )
NULL #avoid returning W
},
f = function(θ)
{
- "Product t(Mi - hat_Mi) W (Mi - hat_Mi) with Mi(theta)"
+ "Product t(hat_Mi - Mi) W (hat_Mi - Mi) with Mi(theta)"
L <- expArgs(θ)
- A <- as.matrix(Moments(L) - Mhat)
+ A <- as.matrix(Mhat - Moments(L))
t(A) %*% W %*% A
},
# Gradient on p: K-1 columns, dim rows
km1 = 1:(K-1)
-
res <- cbind(res, rbind(
sweep(as.matrix(β [,km1]), 2, G1[km1], '*') - G1[K] * β [,K],
sweep(as.matrix(β2[,km1]), 2, G2[km1], '*') - G2[K] * β2[,K],
sweep(as.matrix(β3[,km1]), 2, G3[km1], '*') - G3[K] * β3[,K] ))
- # TODO: understand derivatives order and match the one in optim init param
for (i in 1:d)
{
# i determines the derivated matrix dβ[2,3]