maxEff (version 0.2.2)

splitd: Split-Dichotomized Regression Model

Description

Split-dichotomized regression model.

Usage

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

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 \(y_1\sim\mathcal{D}(x_1)\)

Arguments

start.model

a regression model

x_

language

x

numeric vector

data

hyperframe

id

logical vector, indices of training (TRUE) and test (FALSE) subjects

...

additional parameters, currently not in use

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.