importance.test.boosting: Term importance test based on boosted linear models

In logicDT: Identifying Interactions Between Binary Predictors

Term importance test based on boosted linear models

Description

This function takes a fitted linear.logic model and independent test data as input for testing if the included terms are influential with respect to the outcome. This hypothesis test is based on a likelihood-ratio test.

Usage

importance.test.boosting(model, X, y, Z, Z.interactions = TRUE)

Arguments

model	A fitted linear.logic model (i.e., a model created via fitLinearLogicModel or fitLinearBoostingModel)
X	Matrix or data frame of binary input data. This object should correspond to the binary matrix for fitting the model.
y	Response vector. 0-1 coding for binary outcomes.
Z	Optional quantitative covariables supplied as a matrix or data frame. Only used (and required) if the model was fitted using them.
Z.interactions	A Boolean value determining whether interactions with quantitative covaraible Z shall be taken into account

Details

In detail, the null hypotheses

H_0: β_j = δ_j = 0

using the linear model

g(E[Y]) = β_0 + ∑_{i=1}^B β_i \cdot 1[C_i] + δ_0 \cdot E + ∑_{i=1}^B δ_i \cdot 1[C_i] \cdot E

are tested for each j \in \lbrace 1,…,B \rbrace if Z.interactions is set to TRUE. Otherwise, the null hypotheses

H_0: β_j = 0

using the linear model

g(E[Y]) = β_0 + ∑_{i=1}^B β_i \cdot 1[C_i] + δ_0 \cdot E

are tested.

Value

A data frame consisting of three columns,

var	The tested term,
vim	The associated variable importance, and
p.value	The corresponding p-value for testing if the term is influential.

logicDT documentation built on Jan. 14, 2023, 5:06 p.m.