predict.logicDT: Prediction for logicDT models

Description

Supply new input data for predicting the outcome with a fitted logicDT model.

Usage

# S3 method for logic.bagged
predict(object, X, Z = NULL, type = "prob", ...)
# S3 method for logic.boosted
predict(object, X, Z = NULL, type = "prob", ...)
# S3 method for logicDT
predict(
  object,
  X,
  Z = NULL,
  type = "prob",
  ensemble = FALSE,
  leaves = "4pl",
  ...
)
# S3 method for genetic.logicDT
predict(
  object,
  X,
  Z = NULL,
  models = "best",
  n_models = 10,
  ensemble = NULL,
  leaves = "4pl",
  ...
)

Value

A numeric vector of predictions. For binary outcomes, this is a vector with estimates for \(P(Y=1 \mid X = x)\).

Arguments

object: Fitted logicDT model. Usually a product of a call to logicDT.
X: Matrix or data frame of binary input data. This object should correspond to the binary matrix for fitting the model.
Z: Optional quantitative covariables supplied as a matrix or data frame. Only used (and required) if the model was fitted using them.
type: Prediction type. This can either be "prob" for probability estimates or "class" for (hard) classification of binary responses. Ignored for regression.
...: Parameters supplied to predict.logicDT
ensemble: If the model was fitted using the inner validation approach, shall the prediction be constructed using the final validated ensemble (TRUE) or using the single final tree (FALSE)?
leaves: If leaf regression models (such as four parameter logistic models) were fitted, shall these models be used for the prediction ("4pl") or shall the constant leaf means be used ("constant")?
models: Which logicDT models fitted via genetic programming shall be used for prediction? "best" leads to the single best model in the final generation, "all" uses the average over the final generation and "n_models" uses the n_models best models.
n_models: How many models shall be used if models = "n_models" and genetic programming was employed?