Ensemble: Ensemble model

In sharp: Stability-enHanced Approaches using Resampling Procedures

Ensemble model

Description

Creates an ensemble predictive model from VariableSelection outputs.

Usage

Ensemble(stability, xdata, ydata)

Arguments

stability	output of VariableSelection.
xdata	matrix of predictors with observations as rows and variables as columns.
ydata	optional vector or matrix of outcome(s). If family is set to "binomial" or "multinomial", ydata can be a vector with character/numeric values or a factor.

Value

An object of class ensemble_model. A list with:

intercept	a vector of refitted intercepts for the K calibrated models.
beta	a matrix of beta coefficients from the K calibrated models.
models	a list of K models that can be used for prediction. These models are of class "lm" if family="gaussian" or "glm" if family="binomial".
family	type of regression, extracted from stability. Possible values are "gaussian" or "binomial".

See Also

Other ensemble model functions: EnsemblePredictions()

Examples

# Linear regression
set.seed(1)
simul <- SimulateRegression(n = 100, pk = 50, family = "gaussian")
stab <- VariableSelection(xdata = simul$xdata, ydata = simul$ydata, family = "gaussian")
ensemble <- Ensemble(stability = stab, xdata = simul$xdata, ydata = simul$ydata)

# Logistic regression
set.seed(1)
simul <- SimulateRegression(n = 200, pk = 20, family = "binomial")
stab <- VariableSelection(xdata = simul$xdata, ydata = simul$ydata, family = "binomial")
ensemble <- Ensemble(stability = stab, xdata = simul$xdata, ydata = simul$ydata)

sharp documentation built on April 11, 2025, 5:44 p.m.