LossFunction_GLM: Penalised GLM objective function

In pmesperanca/mlevcm: ML-based Epidemiological Vector Control Monitoring using FDA techniques for NIRS data

Description

Objective function for the penalised GLM, to be optimised using stats::nlm().

Usage

LossFunction_GLM(pars, y_vec, XBD_mat, penmat, lam, l1, lv)

Arguments

pars	Parameter vector (alpha, omega), with alpha scalar and omega a Q-dimensional vector.
y_vec	Response vector.
XBD_mat	Functional predictor matrix after dimension reduction, plus an intercept if appropriate.
penmat	Penalty matrix.
lam	Penalty parameter.
l1	First column of functional predictor, equal to 1 if intercept=FALSE and 2 if intercept=TRUE.
lv	Last column of functional predictor, equal tocol(XBD_mat).

Value

The penalised GLM objective function evaluated at pars.

pmesperanca/mlevcm documentation built on March 17, 2021, 10:03 p.m.