wem {updog}

EM algorithm to fit weighted ash objective.

Description

Solves the following optimization problem

\max_{\pi} \sum_k w_k \log(\sum_j \pi_j \ell_jk).

It does this using a weighted EM algorithm.

Usage

wem(weight_vec, lmat, pi_init, lambda, itermax, obj_tol)

Arguments

weight_vec

A vector of weights. Each element of weight_vec corresponds to a column of lmat.

lmat

A matrix of inner weights. The columns are the "individuals" and the rows are the "classes."

pi_init

The initial values of pivec. Each element of pi_init corresponds to a row of lmat.

lambda

The penalty on the pi's. Should be greater than 0 and really really small.

itermax

The maximum number of EM iterations to take.

obj_tol

The objective stopping criterion.

Value

A vector of numerics.

Author(s)

David Gerard

Examples

set.seed(2)
n <- 3
p <- 5
lmat <- matrix(stats::runif(n * p), nrow = n)
weight_vec <- seq_len(p)
pi_init <- stats::runif(n)
pi_init <- pi_init / sum(pi_init)
wem(weight_vec = weight_vec,
    lmat       = lmat,
    pi_init    = pi_init,
    lambda     = 0,
    itermax    = 100,
    obj_tol    = 10^-6)