approx_with_spline: Approximate spline on data

In xspliner: Assisted Model Building, using Surrogate Black-Box Models to Train Interpretable Spline Based Additive Models

Description

It approximates data with spline function by fitting GAM model.

Usage

approx_with_spline(effect_data, response, predictor,
  env = parent.frame(), ...)

approx_with_monotonic_spline(effect_data, response, predictor,
  env = parent.frame(), monotonic, ...)

Arguments

effect_data	Black box response data, for example pdp curve.
response	Name of response value from effect_data.
predictor	Name of predictor value from effect_data.
env	Formula environment that should be used for fitting gam model.
...	Other arguments passed to s function.
monotonic	Possible options "up", "down" and "auto. If up the spline is increasing, when down decreasing.

Value

Object of class "gam". See gamObject

Examples

x <- sort(rnorm(20, 5, 5))
y <- rnorm(20, 2, 2)
env <- new.env()
approx_with_spline(data.frame(x = x, y = y), "y", "x", env)

approx_with_monotonic_spline(data.frame(x = x, y = y), "y", "x", env, "up")

xspliner documentation built on Sept. 26, 2019, 1:02 a.m.