prNlChooseDf: Chooses the degrees of freedom for the non-linearity
In gforge/Greg: Regression Helper Functions

prNlChooseDf

R Documentation

Chooses the degrees of freedom for the non-linearity

Looks for the model with the minimal min_fn within the flex_param span.

prNlChooseDf(
  model,
  flex_param,
  variable,
  spline_fn,
  min_fn,
  simplest_nonlinear,
  verbal,
  workers,
  libraries
)

`model`	The model that is to be evaluated and adapted for non-linearity
`flex_param`	A `vector` with values that are to be tested as the default second parameter for the non-linearity function that you want to evaluate. This defaults to 2:7, for the `ns()` it tests the degrees of freedom ranging between 2 and 7.
`variable`	The name of the parameter that is to be tested for non-linearity. Note that the variable should be included plain (i.e. as a linear variable) form in the model.
`spline_fn`	Either a string or a function that is to be used for testing alternative non-linearity models
`min_fn`	This is the function that we want to minimized if the variable supports the non-linearity assumption. E.g. `BIC()` or `AIC`, note that the `BIC()` will in the majority of cases support a lower complexity than the `AIC()`.
`simplest_nonlinear`	The simplest non-linear form that the ANOVA has been tested against
`verbal`	Set this to `TRUE` if you want print statements with the anova test and the chosen knots.
`workers`	The function tries to run everything in parallel. Under some circumstances you may want to restrict the number of parallel threads to less than the default `detectCores() - 1`, e.g. you may run out of memory then you can provide this parameter. If you do not want to use parallel then simply set workers to `FALSE`. The cluster created using `makeCluster()` function.
`libraries`	If we use the parallel approach we need to make sure that the right libraries are available in the threads