logLik.mvgam: Compute pointwise Log-Likelihoods from fitted 'mvgam' objects

In mvgam: Multivariate (Dynamic) Generalized Additive Models

Compute pointwise Log-Likelihoods from fitted mvgam objects

Description

Compute pointwise Log-Likelihoods from fitted mvgam objects

Usage

## S3 method for class 'mvgam'
logLik(object, linpreds, newdata, family_pars, include_forecast = TRUE, ...)

Arguments

object	list object returned from mvgam
linpreds	Optional matrix of linear predictor draws to use for calculating pointwise log-likelihoods
newdata	Optional data.frame or list object specifying which series each column in linpreds belongs to. If linpreds is supplied, then newdata must also be supplied
family_pars	Optional list containing posterior draws of family-specific parameters (i.e. shape, scale or overdispersion parameters). Required if linpreds and newdata are supplied
include_forecast	Logical. If newdata were fed to the model to compute forecasts, should the log-likelihood draws for these observations also be returned. Defaults to TRUE
...	Ignored

Value

A matrix of dimension ⁠n_samples x n_observations⁠ containing the pointwise log-likelihood draws for all observations in newdata. If no newdata is supplied, log-likelihood draws are returned for all observations that were originally fed to the model (training observations and, if supplied to the original model via the newdata argument in mvgam, testing observations)

Examples

## Not run: 
# Simulate some data and fit a model
simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
mod <- mvgam(y ~ s(season, bs = 'cc', k = 6),
             trend_model = AR(),
             data = simdat$data_train,
             burnin = 300,
             samples = 300,
             chains = 2)

# Extract logLikelihood values
lls <- logLik(mod)
str(lls)

## End(Not run)

mvgam documentation built on Sept. 11, 2024, 8:55 p.m.