Type:	Package
Title:	Additional Predictor with Maximum Effect Size
Version:	0.2.3
Description:	Methods of selecting one from many numeric predictors for a regression model, to ensure that the additional predictor has the maximum effect size.
RoxygenNote:	7.3.3
Encoding:	UTF-8
License:	GPL-2
Depends:	R (≥ 4.5), groupedHyperframe (≥ 0.3.0)
Language:	en-US
URL:	https://github.com/tingtingzhan/maxEff, https://tingtingzhan.quarto.pub/groupedhyperframe/maxEff.html, https://tingtingzhan-groupedhyperframe.netlify.app/maxEff.html
Imports:	parallel, doParallel, foreach, caret, rpart, spatstat.geom
Suggests:	survival
NeedsCompilation:	no
Packaged:	2026-03-02 17:24:06 UTC; tingtingzhan
Author:	Tingting Zhan [aut, cre]
Maintainer:	Tingting Zhan <tingtingzhan@gmail.com>
Repository:	CRAN
Date/Publication:	2026-03-02 17:40:03 UTC

maxEff: Additional Predictor with Maximum Effect Size

Description

Methods of selecting one from many numeric predictors for a regression model, to ensure that the additional predictor has the maximum effect size.

Author(s)

Maintainer: Tingting Zhan tingtingzhan@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/tingtingzhan/maxEff

• https://tingtingzhan.quarto.pub/groupedhyperframe/maxEff.html

• https://tingtingzhan-groupedhyperframe.netlify.app/maxEff.html

Additional logical Predictor

Description

Additional logical Predictor

Usage

add_dummy(
  start.model,
  x,
  data = eval(start.model$call$data),
  mc.cores = getOption("cores"),
  ...
)

Arguments

Value

The function add_dummy() returns an object of class 'add_dummy'.

Additional logical Predictor via Repeated Partitioning

Description

Additional logical Predictor via Repeated Partitioning

Usage

add_dummy_partition(
  start.model,
  x,
  data = eval(start.model$call$data),
  times,
  mc.cores = getOption("cores"),
  ...
)

Arguments

Value

The function add_dummy_partition() returns an object of class 'add_dummy'.

Additional Predictor as numeric

Description

Additional predictor as numeric.

Usage

add_numeric(
  start.model,
  x,
  data = eval(start.model$call$data),
  mc.cores = getOption("cores"),
  ...
)

Arguments

Details

The function add_numeric() treats each additional predictor as a numeric variable, and updates the starting model with each additional predictor.

Value

The function add_numeric() returns an add_numeric object, which is a listof objects with an internal class 'add_numeric_'.

Get Cutoff Value from a Dichotomizing Rule node1()

Description

To get the cutoff value from a Dichotomizing Rule node1().

Usage

get_cutoff(x)

## S3 method for class 'node1'
get_cutoff(x)

Arguments

Value

The S3 method get_cutoff.node1() returns a numeric scalar.

labels.add_dummy

Description

labels.add_dummy

Usage

## S3 method for class 'add_dummy'
labels(object, ...)

Arguments

Value

The S3 method labels.add_dummy() returns a character vector.

labels.add_numeric

Description

labels.add_numeric

Usage

## S3 method for class 'add_numeric'
labels(object, ...)

Arguments

Value

The S3 method labels.add_numeric() returns a character vector.

Find labels from node1

Description

Find labels from node1

Usage

## S3 method for class 'node1'
labels(object, ...)

Arguments

Value

The S3 method labels.node1() returns a character scalar.

Dichotomize via 1st Node of Recursive Partitioning

Description

Dichotomize via 1st Node of Recursive Partitioning

Usage

node1(object, nm = as.symbol(rownames(s)[1L]), ...)

Arguments

Value

The function node1() returns an object of class 'node1', which is a function with one parameter newx taking a double vector.

Note

In future integer and factor predictors will be supported.

References

https://tingtingzhan.quarto.pub/groupedhyperframe/object/node1.html

Examples

rpart::rpart(
 formula = survival::Surv(pgtime, pgstat) ~ age, 
 data = rpart::stagec[1:135,], 
 cp = .Machine$double.eps, maxdepth = 2L
) |>
 node1()

Predict by node1()

Description

Predict by node1()

Usage

## S3 method for class 'node1'
predict(object, newdata, ...)

Arguments

Regression Models with Optimal Dichotomizing Predictors

Description

Regression models with optimal dichotomizing predictor(s), used either as boolean or continuous predictor(s).

Usage

## S3 method for class 'add_dummy'
predict(object, ...)

## S3 method for class 'add_dummy_'
predict(object, newdata, ...)

Arguments

Value

The S3 method predict.add_dummy() returns a listof regression models.

The function predict.add_dummy_() returns a updated regression model.

Regression Models with Optimal Dichotomizing Predictors

Description

Regression models with optimal dichotomizing predictor(s), used either as boolean or continuous predictor(s).

Usage

## S3 method for class 'add_numeric'
predict(object, ...)

## S3 method for class 'add_numeric_'
predict(object, newdata, ...)

Arguments

Value

The S3 method predict.add_numeric() returns a listof regression models.

print.add_dummy

Description

print.add_dummy

Usage

## S3 method for class 'add_dummy'
print(x, ...)

Arguments

Details

..

Value

The S3 method print.add_dummy() does not have a returned value

print.add_numeric

Description

print.add_numeric

Usage

## S3 method for class 'add_numeric'
print(x, ...)

Arguments

sort_by.add_

Description

sort_by.add_

Usage

## S3 method for class 'add_'
sort_by(x, y, ...)

Arguments

Details

The S3 method sort_by.add_() sorts the elements of an 'add_' object by a certain criterion y. We suggest using y = abc(effsize) and decreasing = TRUE order of the absolute values of the effect sizes of additional predictor.

Value

The S3 method sort_by.add_() returns an object of the same class as input x.

Split-Dichotomized Regression Model

Description

Split-dichotomized regression model.

Usage

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

Arguments

Value

The 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

The 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.

Stratified Partition

Description

A variation of createDataPartition, to split Surv y by survival status instead of the percentiles survival time.

Usage

statusPartition(y, times, p = 0.8, ...)

Arguments

Details

See vignette('intro', package = 'maxEff').

Value

The function statusPartition() returns a length-n listof integer vectors. In each integer vector indicates the training subjects.

Note

The function caTools::sample.split is not what we need.

References

https://tingtingzhan.quarto.pub/groupedhyperframe/nonS3/statusPartition.html

S3 Method Dispatches to 'add_dummy' Class

Description

S3 Method Dispatches to 'add_dummy' Class

Usage

## S3 method for class 'add_dummy'
subset(x, subset, ...)

Arguments

Details

The S3 method subset.add_dummy(), default subset (p1>.15 & p1<.85). See explanation of p_1 in function splitd().

Value

The S3 method subset.add_dummy() returns a add_dummy() object.