splitd: Split-Dichotomized Regression Model

In maxEff: Additional Predictor with Maximum Effect Size

Split-Dichotomized Regression Model

Description

Split-dichotomized regression model.

Usage

splitd(start.model, x_, data, id, ...)

Arguments

start.model	a regression model
x_	language
data	data.frame
id	logical vector, indices of training (TRUE) and test (FALSE) subjects
...	additional parameters, currently not in use

Value

Function splitd() returns a function, the dichotomizing rule \mathcal{D} based on the training set (y_0, x_0), with additional attributes

attr(,'p1')

double scalar, p_1 = \text{Pr}(\mathcal{D}(x_1)=1)

attr(,'effsize')

double scalar, univariable regression coefficient estimate of y_1\sim\mathcal{D}(x_1)

Split-Dichotomized Regression Model

Function splitd() performs a univariable regression model on the test set with a dichotomized predictor, using a dichotomizing rule determined by a recursive partitioning of the training set. Specifically, given a training-test sample split,

1. find the dichotomizing rule \mathcal{D} of the predictor x_0 given the response y_0 in the training set (via function node1());

2. fit a univariable regression model of the response y_1 with the dichotomized predictor \mathcal{D}(x_1) in the test set.

Currently the Cox proportional hazards (coxph) regression for Surv response, logistic (glm) regression for logical response and linear (lm) regression for gaussian response are supported.

maxEff documentation built on April 12, 2025, 2:11 a.m.