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Posterior Draws of the Linear Predictor

Source: R/posterior_epred.mvgam.R
posterior_linpred.mvgam.Rd

Compute posterior draws of the linear predictor, that is draws before applying any link functions or other transformations. Can be performed for the data used to fit the model (posterior predictive checks) or for new data.

Usage

# S3 method for mvgam
posterior_linpred(
  object,
  transform = FALSE,
  newdata,
  ndraws = NULL,
  data_test,
  process_error = TRUE,
  ...
)

Arguments

object

list object returned from mvgam. See mvgam()

transform

Logical; if FALSE (the default), draws of the linear predictor are returned. If TRUE, draws of the transformed linear predictor, i.e. the conditional expectation, are returned.

newdata

Optional dataframe or list of test data containing the same variables that were included in the original data used to fit the model. If not supplied, predictions are generated for the original observations used for the model fit.

ndraws

Positive integer indicating how many posterior draws should be used. If NULL (the default) all draws are used.

data_test

Deprecated. Still works in place of newdata but users are recommended to use newdata instead for more seamless integration into R workflows

process_error

Logical. If TRUE and newdata is supplied, expected uncertainty in the process model is accounted for by using draws from any latent trend SD parameters. If FALSE, uncertainty in the latent trend component is ignored when calculating predictions. If no newdata 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 new_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.

See also

posterior_epred.mvgam posterior_predict.mvgam

hindcast.mvgam posterior_epred.mvgam posterior_predict.mvgam

Examples

# \donttest{
# Simulate some data and fit a model
simdat <- sim_mvgam(n_series = 1, trend_model = 'AR1')
mod <- mvgam(y ~ s(season, bs = 'cc'),
             trend_model = AR(),
             noncentred = TRUE,
             data = simdat$data_train,
             chains = 2)
#> Compiling Stan program using cmdstanr
#> 
#> Start sampling
#> Running MCMC with 2 parallel chains...
#> 
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#> Chain 2 finished in 0.4 seconds.
#> Chain 1 Iteration: 1000 / 1000 [100%]  (Sampling) 
#> Chain 1 finished in 0.4 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 0.4 seconds.
#> Total execution time: 0.5 seconds.
#> 

# Extract linear predictor values
linpreds <- posterior_linpred(mod)
str(linpreds)
#>  num [1:1000, 1:75] -0.172 0.183 -0.907 -0.164 0.167 ...
# }