mvgam-class: Fitted 'mvgam' object description

In mvgam: Multivariate (Dynamic) Generalized Additive Models

Fitted mvgam object description

Description

A fitted mvgam object returned by function mvgam. Run methods(class = "mvgam") to see an overview of available methods.

Details

A mvgam object contains the following elements:

• call the original observation model formula

• trend_call If a ⁠trend_formula was supplied⁠, the original trend model formula is returned. Otherwise NULL

• family character description of the observation distribution

• trend_model character description of the latent trend model

• trend_map data.frame describing the mapping of trend states to observations, if supplied in the original model. Otherwise NULL

• drift Logical specifying whether a drift term was used in the trend model

• priors If the model priors were updated from their defaults, the prior dataframe will be returned. Otherwise NULL

• model_output The MCMC object returned by the fitting engine. If the model was fitted using Stan, this will be an object of class stanfit (see stanfit-class for details). If JAGS was used as the backend, this will be an object of class runjags (see runjags-class for details)

• model_file The character string model file used to describe the model in either Stan or JAGS syntax

• model_data If return_model_data was set to TRUE when fitting the model, the list object containing all data objects needed to condition the model is returned. Each item in the list is described in detail at the top of the model_file. Otherwise NULL

• inits If return_model_data was set to TRUE when fitting the model, the initial value functions used to initialise the MCMC chains will be returned. Otherwise NULL

• monitor_pars The parameters that were monitored during MCMC sampling are returned as a ⁠character vector⁠

• sp_names A ⁠character vector⁠ specifying the names for each smoothing parameter

• mgcv_model An object of class gam containing the mgcv version of the observation model. This object is used for generating the linear predictor matrix when making predictions for new data. The coefficients in this model object will contain the posterior median coefficients from the GAM linear predictor, but these are only used if generating plots of smooth functions that mvgam currently cannot handle (such as plots for three-dimensional smooths). This model therefore should not be used for inference. See gamObject for details

• trend_mgcv_model If a ⁠trend_formula was supplied⁠, an object of class gam containing the mgcv version of the trend model. Otherwise NULL

• ytimes The matrix object used in model fitting for indexing which series and timepoints were observed in each row of the supplied data. Used internally by some downstream plotting and prediction functions

• resids A named list object containing posterior draws of Dunn-Smyth randomized quantile residuals

• use_lv Logical flag indicating whether latent dynamic factors were used in the model

• n_lv If use_lv == TRUE, the number of latent dynamic factors used in the model

• upper_bounds If bounds were supplied in the original model fit, they will be returned. Otherwise NULL

• obs_data The original data object (either a list or dataframe) supplied in model fitting.

• test_data If test data were supplied (as argument newdata in the original model), it will be returned. Othwerise NULL

• fit_engine Character describing the fit engine, either as stan or jags

• backend Character describing the backend used for modelling, either as rstan, cmdstanr or rjags

• algorithm Character describing the algorithm used for finding the posterior, either as sampling, laplace, pathfinder, meanfield or fullrank

• max_treedepth If the model was fitted using Stan, the value supplied for the maximum treedepth tuning parameter is returned (see stan for details). Otherwise NULL

• adapt_delta If the model was fitted using Stan, the value supplied for the adapt_delta tuning parameter is returned (see stan for details). Otherwise NULL

Author(s)

Nicholas J Clark

See Also

mvgam

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