Function reference • mvgam

All functions

add_residuals(): Calculate randomized quantile residuals for mvgam objects

all_neon_tick_data: NEON Amblyomma and Ixodes tick abundance survey data

augment(<mvgam>): Augment an mvgam object's data

code() stancode(<mvgam_prefit>) stancode(<mvgam>) standata(<mvgam_prefit>): Stan code and data objects for mvgam models

conditional_effects(<mvgam>) plot(<mvgam_conditional_effects>) print(<mvgam_conditional_effects>): Display Conditional Effects of Predictors

dynamic(): Defining dynamic coefficients in mvgam formulae

ensemble(): Combine mvgam forecasts into evenly weighted ensembles

eval_mvgam() roll_eval_mvgam() compare_mvgams(): Evaluate forecasts from fitted mvgam objects

fevd(): Calculate latent VAR forecast error variance decompositions

fitted(<mvgam>): Expected Values of the Posterior Predictive Distribution

forecast(): Extract or compute hindcasts and forecasts for a fitted mvgam object

formula(<mvgam>) formula(<mvgam_prefit>): Extract formulae from mvgam objects

get_mvgam_priors(): Extract information on default prior distributions for an mvgam model

GP(): Specify dynamic Gaussian processes

drawDotmvgam() eval_smoothDothilbertDotsmooth() eval_smoothDotmodDotsmooth() eval_smoothDotmoiDotsmooth(): Enhance mvgam post-processing using gratia functionality

hindcast(): Extract hindcasts for a fitted mvgam object

variables(<mvgam>): Index mvgam objects

irf(): Calculate latent VAR impulse response functions

jsdgam(): Fit Joint Species Distribution Models in mvgam

lfo_cv(): Approximate leave-future-out cross-validation of fitted mvgam objects

logLik(<mvgam>): Compute pointwise Log-Likelihoods from fitted mvgam objects

loo(<mvgam>) loo_compare(<mvgam>): LOO information criteria for mvgam models

lv_correlations(): Calculate trend correlations based on mvgam latent factor loadings

mcmc_plot(<mvgam>): MCMC plots as implemented in bayesplot

model.frame(<mvgam>) model.frame(<mvgam_prefit>): Extract model.frame from a fitted mvgam object

smooth.construct(<moi.smooth.spec>) smooth.construct(<mod.smooth.spec>) Predict.matrix(<moi.smooth>) Predict.matrix(<mod.smooth>): Monotonic splines in mvgam

mvgam-class: Fitted mvgam object description

mvgam(): Fit a Bayesian dynamic GAM to a univariate or multivariate set of time series

nuts_params(<mvgam>) log_posterior(<mvgam>) rhat(<mvgam>) neff_ratio(<mvgam>): Extract diagnostic quantities of mvgam models

as.data.frame(<mvgam>) as.matrix(<mvgam>) as.array(<mvgam>) as_draws(<mvgam>) as_draws_matrix(<mvgam>) as_draws_df(<mvgam>) as_draws_array(<mvgam>) as_draws_list(<mvgam>) as_draws_rvars(<mvgam>): Extract posterior draws from fitted mvgam objects

tweedie() student_t() betar() nb() lognormal() student() bernoulli() beta_binomial() nmix(): Supported mvgam families

mvgam_fevd-class: mvgam_fevd object description

mvgam_forecast-class: mvgam_forecast object description

mvgam_formulae: Details of formula specifications in mvgam

mvgam_irf-class: mvgam_irf object description

get_coef(<mvgam>) set_coef(<mvgam>) get_vcov(<mvgam>) get_predict(<mvgam>) get_data(<mvgam>) get_data(<mvgam_prefit>) find_predictors(<mvgam>) find_predictors(<mvgam_prefit>): Helper functions for mvgam marginaleffects calculations

mvgam_trends: Supported mvgam trend models

pairs(<mvgam>): Create a matrix of output plots from a mvgam object

PW(): Specify piecewise linear or logistic trends

plot(<mvgam>): Default mvgam plots

plot(<mvgam_fevd>): Plot forecast error variance decompositions from an mvgam_fevd object This function takes an mvgam_fevd object and produces a plot of the posterior median contributions to forecast variance for each series in the fitted Vector Autoregression

plot(<mvgam_irf>): Plot impulse responses from an mvgam_irf object This function takes an mvgam_irf object and produces plots of Impulse Response Functions

plot(<mvgam_lfo>): Plot Pareto-k and ELPD values from a leave-future-out object

plot_mvgam_factors(): Latent factor summaries for a fitted mvgam object

plot_mvgam_fc() plot(<mvgam_forecast>): Plot mvgam posterior predictions for a specified series

plot_mvgam_pterms(): Plot mvgam parametric term partial effects

plot_mvgam_randomeffects(): Plot mvgam random effect terms

plot_mvgam_resids(): Residual diagnostics for a fitted mvgam object

plot_mvgam_series(): Plot observed time series used for mvgam modelling

plot_mvgam_smooth(): Plot mvgam smooth terms

plot_mvgam_trend(): Plot mvgam latent trend for a specified series

plot_mvgam_uncertainty(): Plot mvgam forecast uncertainty contributions for a specified series

portal_data: Portal Project rodent capture survey data

posterior_epred(<mvgam>): Draws from the Expected Value of the Posterior Predictive Distribution

posterior_linpred(<mvgam>): Posterior Draws of the Linear Predictor

posterior_predict(<mvgam>): Draws from the Posterior Predictive Distribution

ppc(): Plot mvgam conditional posterior predictive checks for a specified series

pp_check(<mvgam>): Posterior Predictive Checks for mvgam Objects

predict(<mvgam>): Predict from the GAM component of an mvgam model

print(<mvgam>): Summary for a fitted mvgam object

residuals(<mvgam>): Posterior draws of mvgam residuals

residual_cor(): Extract residual correlations based on latent factors from a fitted jsdgam

RW() AR() CAR() VAR(): Specify autoregressive dynamic processes in mvgam

score(): Compute probabilistic forecast scores for mvgam objects

series_to_mvgam(): This function converts univariate or multivariate time series (xts or ts objects) to the format necessary for mvgam

sim_mvgam(): Simulate a set of time series for mvgam modelling

stability(): Calculate measures of latent VAR community stability

summary(<mvgam>) summary(<mvgam_prefit>) coef(<mvgam>): Summary for a fitted mvgam object

update(<mvgam>) update(<jsdgam>): Update an existing mvgam object

ZMVN(): Specify correlated residual processes in mvgam