R/autoplot.R

In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

#' Plot estimated smooths from a fitted \code{smimodel}
#'
#' Plots the graphs of fitted spline(s). If a set of multiple models are fitted,
#' plots graphs of fitted spline(s) of a specified model (in argument
#' \code{model}) out of the set of multiple models fitted.
#'
#' @param object A \code{smimodel} object.
#' @param model An \code{integer} to indicate the smooths of which model (out of
#'   the set of multiple models fitted) to be plotted.
#' @param ... Other arguments not currently used.
#'
#' @return Plot(s) of fitted spline(s).
#'
#' @method autoplot smimodel
#' 
#' @examples
#' if(requireNamespace("gurobi", quietly = TRUE)){
#'   library(dplyr)
#'   library(ROI)
#'   library(tibble)
#'   library(tidyr)
#'   library(tsibble)
#'
#'   # Simulate data
#'   n = 1005
#'   set.seed(123)
#'   sim_data <- tibble(x_lag_000 = runif(n)) |>
#'     mutate(
#'       # Add x_lags
#'       x_lag = lag_matrix(x_lag_000, 5)) |>
#'     unpack(x_lag, names_sep = "_") |>
#'     mutate(
#'       # Response variable
#'       y = (0.9*x_lag_000 + 0.6*x_lag_001 + 0.45*x_lag_003)^3 + rnorm(n, sd = 0.1),
#'       # Add an index to the data set
#'       inddd = seq(1, n)) |>
#'     drop_na() |>
#'     select(inddd, y, starts_with("x_lag")) |>
#'     # Make the data set a `tsibble`
#'     as_tsibble(index = inddd)
#'
#'   # Index variables
#'   index.vars <- colnames(sim_data)[3:8]
#'
#'   # Model fitting
#'   smimodel_ppr <- model_smimodel(data = sim_data,
#'                                 yvar = "y",
#'                                 index.vars = index.vars,
#'                                 initialise = "ppr")
#'
#'   autoplot(smimodel_ppr)
#' }
#'
#' @export
autoplot.smimodel <- function(object, model = 1, ...){
  draw(object$fit[[model]]$best$gam)
}