
Draws from the expected value of the posterior predictive distribution for mvgam objects
Source:R/posterior_epred.mvgam.R
posterior_epred.mvgam.Rd
Compute posterior draws of the expected value of the posterior predictive
distribution (i.e. the conditional expectation). Can be performed for the
data used to fit the model (posterior predictive checks) or for new data.
By definition, these predictions have smaller variance than the posterior
predictions performed by the posterior_predict.mvgam
method.
This is because only the uncertainty in the expected value of the posterior
predictive distribution is incorporated in the draws computed by
posterior_epred
while the residual error is ignored there. However,
the estimated means of both methods averaged across draws should be very
similar.
Usage
# S3 method for mvgam
posterior_epred(
object,
newdata,
data_test,
ndraws = NULL,
process_error = TRUE,
...
)
Arguments
- object
list
object of classmvgam
orjsdgam
. Seemvgam()
- newdata
Optional
dataframe
orlist
of test data containing the same variables that were included in the originaldata
used to fit the model. If not supplied, predictions are generated for the original observations used for the model fit.- data_test
Deprecated. Still works in place of
newdata
but users are recommended to usenewdata
instead for more seamless integration intoR
workflows- ndraws
Positive
integer
indicating how many posterior draws should be used. IfNULL
(the default) all draws are used.- process_error
logical
. IfTRUE
andnewdata
is supplied, expected uncertainty in the process model is accounted for by using draws from any latent trend SD parameters. IfFALSE
, uncertainty in the latent trend component is ignored when calculating predictions. If nonewdata
is supplied, draws from the fitted model's posterior predictive distribution will be used (which will always include uncertainty in any latent trend components)- ...
Ignored
Value
A matrix
of dimension n_samples x n_obs
, where
n_samples
is the number of posterior samples from the fitted object
and n_obs
is the number of observations in newdata
Details
Note that for all types of predictions for models that did not
include a trend_formula
, uncertainty in the dynamic trend component can
be ignored by setting process_error = FALSE
. However, if a
trend_formula
was supplied in the model, predictions for this component
cannot be ignored. If process_error = TRUE
, trend predictions will
ignore autocorrelation coefficients or GP length scale coefficients,
ultimately assuming the process is stationary. This method is similar to
the types of posterior predictions returned from brms
models when using
autocorrelated error predictions for newdata. This function is therefore
more suited to posterior simulation from the GAM components of a
mvgam
model, while the forecasting functions
plot_mvgam_fc
and forecast.mvgam
are better
suited to generate h-step ahead forecasts that respect the temporal
dynamics of estimated latent trends.
Examples
# \donttest{
# Simulate some data and fit a model
simdat <- sim_mvgam(
n_series = 1,
trend_model = AR()
)
mod <- mvgam(
y ~ s(season, bs = 'cc'),
trend_model = AR(),
noncentred = TRUE,
data = simdat$data_train,
chains = 2,
silent = 2
)
# Compute posterior expectations
expectations <- posterior_epred(mod)
str(expectations)
#> num [1:1000, 1:75] 0.326 0.607 1.03 0.604 0.314 ...
# }