partialDependence: Compute partial dependence
In semtree: Recursive Partitioning for Structural Equation Models

partialDependence

R Documentation

Compute partial dependence

Compute the partial dependence of a predictor, or set of predictors, on a model parameter.

partialDependence(
  x,
  data,
  reference.var,
  support = 20,
  points = NULL,
  mc = NULL,
  FUN = "median",
  ...
)

`x`	An object for which a method exists
`data`	Optional `data.frame` that was used to train the model.
`reference.var`	Character vector, referring to the (independent) reference variable or variables for which partial dependence is calculated. Providing two (or more) variables allows for probing interactions, but note that this is computationally expensive.
`support`	Integer. Number of grid points for interpolating the `reference.var`. Alternatively, use `points` for one or more variables named in `reference.var`.
`points`	Named list, with elements corresponding to `reference.var` . Use this argument to provide specific points for which to obtain marginal dependence values; for example, the mean and +/- 1SD of `reference.var`.
`mc`	Integer. If `mc` is not `NULL`, the function will sample `mc` number of rows from `data` with replacement, to estimate marginal dependency using Monte Carlo integration. This is less computationally expensive.
`FUN`	Character string with function used to integrate predictions across all elements of `x`.
`...`	Extra arguments passed to `FUN`.