bayesmodels (version 0.1.1)

adaptive_spline: General Interface for Adaptive Spline Surface Models

Description

adaptive_spline() is a way to generate a specification of an Adaptive Spline Surface model before fitting and allows the model to be created using different packages. Currently the only package is BASS.

Usage

adaptive_spline(
  mode = "regression",
  splines_degree = NULL,
  max_degree = NULL,
  max_categorical_degree = NULL,
  min_basis_points = NULL
)

Arguments

mode

A single character string for the type of model. The only possible value for this model is "regression".

splines_degree

degree of splines. Stability should be examined for anything other than 1.

max_degree

integer for maximum degree of interaction in spline basis functions. Defaults to the number of predictors, which could result in overfitting.

max_categorical_degree

(categorical input only) integer for maximum degree of interaction of categorical inputs.

min_basis_points

minimum number of non-zero points in a basis function. If the response is functional, this refers only to the portion of the basis function coming from the non-functional predictors. Defaults to 20 or 0.1 times the number of observations, whichever is smaller.

Value

A model spec

Engine Details

Other options can be set using set_engine().

stan (default engine)

The engine uses BASS::bass().

Parameter Notes:

• xreg - This is supplied via the parsnip / bayesmodels fit() interface (so don't provide this manually). See Fit Details (below).

Fit Details

Date and Date-Time Variable

It's a requirement to have a date or date-time variable as a predictor. The fit() interface accepts date and date-time features and handles them internally.

• fit(y ~ date)

Univariate (No xregs, Exogenous Regressors):

This algorithm only accepts multivariate: you need to pass xregs (read next section).

Multivariate (xregs, Exogenous Regressors)

The xreg parameter is populated using the fit() function:

• Only factor, ordered factor, and numeric data will be used as xregs.

• Date and Date-time variables are not used as xregs

• character data should be converted to factor.

Xreg Example: Suppose you have 3 features:

1. y (target)

2. date (time stamp),

3. month.lbl (labeled month as a ordered factor).

The month.lbl is an exogenous regressor that can be passed to the sarima_reg() using fit():

• fit(y ~ date + month.lbl) will pass month.lbl on as an exogenous regressor.

Note that date or date-time class values are excluded from xreg.

Details

The data given to the function are not saved and are only used to determine the mode of the model. For adaptive_spline(), the mode will always be "regression".

The model can be created using the fit() function using the following engines:

• "stan" (default) - Connects to BASS::bass()

Main Arguments

The main arguments (tuning parameters) for the model are:

• splines_degree

• max_degree

• max_categorical_degree

• min_basis_points

These arguments are converted to their specific names at the time that the model is fit.

Other options and argument can be set using set_engine() (See Engine Details below).

If parameters need to be modified, update() can be used in lieu of recreating the object from scratch.

See Also

fit.model_spec(), set_engine()

Examples

# NOT RUN {
library(dplyr)
library(parsnip)
library(rsample)
library(timetk)
library(modeltime)
library(bayesmodels)
library(lubridate)

# Data
m750 <- m4_monthly %>% filter(id == "M750")
m750

# Split Data 80/20
splits <- rsample::initial_time_split(m750, prop = 0.8)

# ---- Adaptive Spline ----

# Model Spec
model_spec <- adaptive_spline() %>%
    set_engine("stan")

# Fit Spec
model_fit <- model_spec %>%
    fit(log(value) ~ date + month(date), data = training(splits))
model_fit
# }