Package index • smimodel

Estimating SMI model

Functions to estimate a SMI model with or without penalty parameter tuning.

model_smimodel(): Sparse Multiple Index (SMI) Models
greedy_smimodel(): SMI model estimation through a greedy search for penalty parameters

Estimating other benchmark models

Functions to fit some benchmark comparison methods.

model_backward(): Nonparametric Additive Model with Backward Elimination
model_gaim(): Groupwise Additive Index Models (GAIM)
model_ppr(): Projection Pursuit Regression (PPR) models
model_gam(): Generalised Additive Models
model_lm(): Linear Regression models

Augment methods

Obtain residuals and fitted values of the fitted models.

augment(<smimodel>): Augment function for class smimodel
augment(<backward>): Augment function for class backward
augment(<gaimFit>): Augment function for class gaimFit
augment(<pprFit>): Augment function for class pprFit
augment(<gamFit>): Augment function for class gamFit
augment(<lmFit>): Augment function for class lmFit

Predict methods

Obtain residuals and fitted values of the fitted models.

predict(<smimodel>): Obtaining forecasts on a test set from a fitted smimodel
predict(<backward>): Obtaining forecasts on a test set from a fitted backward
predict(<gaimFit>): Obtaining forecasts on a test set from a fitted gaimFit
predict(<pprFit>): Obtaining forecasts on a test set from a fitted pprFit
predict(<gamFit>): Obtaining forecasts on a test set from a fitted gamFit
predict(<lmFit>): Obtaining forecasts on a test set from a fitted lmFit

Point estimate accuracy measures

Calculate point estimate accuracy measures.

MAE() MSE() point_measures: Point estimate accuracy measures

Prediction Intervals

Functions to construct and evaluate prediction intervals in time series forecasting problems.

bb_cvforecast(): Single season block bootstrap prediction intervals through time series cross-validation forecasting
cb_cvforecast(): Conformal bootstrap prediction intervals through time series cross-validation forecasting
avgCoverage(): Calculate interval forecast coverage
avgWidth(): Calculate interval forecast width

Other user-facing functions

Other exported functions that can be called by users.

autoplot(<smimodel>): Plot estimated smooths from a fitted smimodel
residuals(<smimodel>): Extract residuals from a fitted smimodel
lag_matrix(): Function for adding lags of time series variables
print(<smimodel>): Printing a smimodel object
print(<smimodelFit>): Printing a smimodelFit object
print(<backward>): Printing a backward object
print(<gaimFit>): Printing a gaimFit object
print(<pprFit>): Printing a pprFit object
forecast(<smimodel>): Forecasting using SMI models
forecast(<backward>): Forecasting using nonparametric additive models with backward elimination
forecast(<gaimFit>): Forecasting using GAIMs
forecast(<pprFit>): Forecasting using PPR models
forecast(<gamFit>): Forecasting using GAMs

Other non-user-facing functions

Other internal functions that are not exported.

smimodel.fit(): SMI model estimation
new_smimodelFit(): Constructor function for the class smimodelFit
update_smimodelFit(): Updating a smimodelFit
make_smimodelFit(): Converting a fitted gam object to a smimodelFit object
inner_update(): Updating index coefficients and non-linear functions iteratively
init_alpha(): Initialising index coefficients
update_alpha(): Updating index coefficients using MIP
greedy.fit(): Greedy search for tuning penalty parameters
tune_smimodel(): SMI model with a given penalty parameter combination
normalise_alpha(): Scaling index coefficient vectors to have unit norm
loss(): Calculating the loss of the MIP used to estimate a SMI model
allpred_index(): Constructing index coefficient vectors with all predictors in each index
split_index(): Splitting predictors into multiple indices
scaling(): Scale data
unscaling(): Unscale a fitted smimodel
augment(<smimodelFit>): Augment function for class smimodelFit
predict(<smimodelFit>): Obtaining forecasts on a test set from a smimodelFit
predict_gam(): Obtaining recursive forecasts on a test set from a fitted mgcv::gam
eliminate(): Eliminate a variable and fit a nonparametric additive model
blockBootstrap(): Futures through single season block bootstrapping
residBootstrap(): Generate multiple single season block bootstrap series
seasonBootstrap(): Single season block bootstrap
randomBlock(): Randomly sampling a block
possibleFutures_smimodel(): Possible future sample paths (multi-step) from smimodel residuals
possibleFutures_benchmark(): Possible future sample paths (multi-step) from residuals of a fitted benchmark model
prep_newdata(): Prepare a data set for recursive forecasting
remove_lags(): Remove actual values from a data set for recursive forecasting
truncate_vars(): Truncating predictors to be in the in-sample range

Package metadata

Package information.

smimodel smimodel-package: smimodel: Sparse Multiple Index Models for Nonparametric Forecasting