65 lines
2.0 KiB
R
65 lines
2.0 KiB
R
#' Fitting Generalized Multi-Linear Models
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#'
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#' @export
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GMLM <- function(...) {
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stop("Not Implemented")
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}
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#' @export
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GMLM.default <- function(X, Fy, sample.axis = 1L,
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family = "normal",
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...,
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eps = sqrt(.Machine$double.eps),
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logger = NULL
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) {
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stopifnot(exprs = {
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length(sample.axis) == 1L
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(1L <= sample.axis) && (sample.axis <= length(dim(X)))
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(dim(X) == dim(Fy))[sample.axis]
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})
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# rearrange `X`, `Fy` such that the last axis enumerates observations
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axis.perm <- c(seq_along(dim(X))[-sample.axis], sample.axis)
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X <- aperm(X, axis.perm)
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Fy <- aperm(Fy, axis.perm)
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# setup family specific GLM (pseudo) "inverse" link
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family <- make.gmlm.family(family)
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# wrap logger in callback for NAGD
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callback <- if (is.function(logger)) {
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function(iter, params) {
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do.call(logger, c(list(iter), params))
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}
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}
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# optimize likelihood using Nesterov Excelerated Gradient Descent
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params.fit <- NAGD(
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fun.loss = function(params) {
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# scaled negative log-likelihood
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# eta1 alphas Omegas
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family$log.likelihood(X, Fy, params[[1]], params[[2]], params[[3]])
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},
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fun.grad = function(params) {
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# gradient of the scaled negative log-likelihood
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# eta1 alphas Omegas
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family$grad(X, Fy, params[[1]], params[[2]], params[[3]])
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},
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params = family$initialize(X, Fy), # initial parameter estimates
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fun.lincomb = function(a, lhs, b, rhs) {
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list(
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a * lhs[[1]] + b * rhs[[1]],
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Map(function(l, r) a * l + b * r, lhs[[2]], rhs[[2]]),
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Map(function(l, r) a * l + b * r, lhs[[3]], rhs[[3]])
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)
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},
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fun.norm2 = function(params) {
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sum(unlist(params)^2)
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},
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callback = callback,
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...
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)
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structure(params.fit, names = c("eta1", "alphas", "Omegas"))
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}
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