regTest: Regression-based information loss test

In causalDisco: Tools for Causal Discovery on Observational Data

Regression-based information loss test

Description

We test whether x and y are associated, given S using a generalized linear model.

Usage

regTest(x, y, S, suffStat)

Arguments

x	Index of x variable
y	Index of y variable
S	Index of S variable(s), possibly NULL
suffStat	Sufficient statistic; list with data, binary variables and order.

Details

All included variables should be either numeric or binary. If y is binary, a logistic regression model is fitted. If y is numeric, a linear regression model is fitted. x and S are included as explanatory variables. Any numeric variables among x and S are modeled with spline expansions (natural splines, 3 df). This model is tested against a numeric where x (including a possible spline expansion) has been left out using a likelihood ratio test. The model is fitted in both directions (interchanging the roles of x and y). The final p-value is the maximum of the two obtained p-values.

Value

A numeric, which is the p-value of the test.

causalDisco documentation built on May 12, 2022, 9:05 a.m.