splitd {maxEff}R Documentation

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.


[Package maxEff version 0.1.1 Index]