Approximate spline on data

It approximates data with spline function by fitting GAM model.

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)
#> 
#> Family: gaussian 
#> Link function: identity 
#> 
#> Formula:
#> y ~ s(x)
#> 
#> Estimated degrees of freedom:
#> 1  total = 2 
#> 
#> GCV score: 4.577151     

approx_with_monotonic_spline(data.frame(x = x, y = y), "y", "x", env, "up")
#> Warning: initial point very close to some inequality constraints
#> Warning: initial parameters very close to inequality constraints
#> 
#> Family: gaussian 
#> Link function: identity 
#> 
#> Formula:
#> y ~ s(x)
#> 
#> Estimated degrees of freedom:
#> 1  total = 2 
#> 
#> GCV score: 4.577151