Compute pointwise Log-Likelihoods from fitted mvgam objects

Compute pointwise Log-Likelihoods from fitted mvgam objects

Usage

# S3 method for 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

if (FALSE) {
# 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)
}