mid.conditional: Calculate MID Conditional Expectations

Description

mid.conditional() calculates the data required to draw Individual Conditional Expectation (ICE) curves from a fitted MID model. ICE curves visualize how a single observation's prediction changes as a specified variable's value varies, while all other variable are held constant.

Usage

mid.conditional(
  object,
  variable,
  data = NULL,
  n.samples = 100L,
  max.nrow = 100000L,
  type = c("response", "link"),
  keep.effects = TRUE
)

Value

mid.conditional() returns an object of class "mid.conditional". This is a list with the following components:

terms: a character vector of relevant terms for the variable.
observed: a data frame of the original observations used, along with their predictions.
conditional: a data frame of the hypothetical observations and their corresponding predictions.
values: a vector of the sample points for the variable used in the ICE calculation

Arguments

object: a "mid" object.
variable: a character string or expression specifying the single predictor variable for which to calculate ICE curves.
data: a data frame containing the observations to be used for the ICE calculations. If not provided, data is automatically extracted based on the function call.
n.samples: the number of sample points for the variable's range.
max.nrow: the maximum number of rows for the output data frames. If the number of evaluation points exceeds this limit, the original data is randomly subsampled.
type: the type of prediction to return. "response" (default) for the original scale or "link" for the scale of the linear predictor.
keep.effects: logical. If TRUE, the effects of individual component functions are stored in the output object.

Details

The function generates a set of hypothetical observations by creating copies of the original data and varying the specified variable across a range of sample points. It then obtains a prediction for each of these hypothetical observations from the MID model. The returned object can be plotted to visualize the ICE curves.

Examples

Run this code

data(airquality, package = "datasets")
mid <- interpret(Ozone ~ .^2, data = airquality, lambda = 1)

# Calculate the ICE values for a fitted MID model
ice <- mid.conditional(mid, variable = "Wind", data = airquality)
print(ice)