Create dataset to compute partial dependence

Source: R/partialDependence.R

Create a dataset with fixed values for reference.var for all other values of data, or using mc random samples from data (Monte Carlo integration).

partialDependence_data(
  data,
  reference.var,
  support = 20,
  points = NULL,
  mc = NULL,
  keep_id = FALSE
)

Arguments

data

The 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

keep_id

Boolean. Default is false. Should output contain a row id column? marginal dependency using Monte Carlo integration. This is less computationally expensive.

Author

Caspar J. Van Lissa