dependence: Partial Dependence

In MachineShop: Machine Learning Models and Tools

Partial Dependence

Description

Calculate partial dependence of a response on select predictor variables.

Usage

dependence(
  object,
  data = NULL,
  select = NULL,
  interaction = FALSE,
  n = 10,
  intervals = c("uniform", "quantile"),
  distr = character(),
  method = character(),
  stats = MachineShop::settings("stats.PartialDependence"),
  na.rm = TRUE
)

Arguments

object	model fit result.
data	data frame containing all predictor variables. If not specified, the training data will be used by default.
select	expression indicating predictor variables for which to compute partial dependence (see subset for syntax) [default: all].
interaction	logical indicating whether to calculate dependence on the interacted predictors.
n	number of predictor values at which to perform calculations.
intervals	character string specifying whether the n values are spaced uniformly ("uniform") or according to variable quantiles ("quantile").
distr, method	arguments passed to predict.
stats	function, function name, or vector of these with which to compute response variable summary statistics over non-selected predictor variables.
na.rm	logical indicating whether to exclude missing predicted response values from the calculation of summary statistics.

Value

PartialDependence class object that inherits from data.frame.

See Also

plot

Examples

## Requires prior installation of suggested package gbm to run

gbm_fit <- fit(Species ~ ., data = iris, model = GBMModel)
(pd <- dependence(gbm_fit, select = c(Petal.Length, Petal.Width)))
plot(pd)

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.