iForm: Interaction Screening for Ultra-High Dimensional Data

In kdgosik/iForm: Forward Selection Under Marginality Principle

Description

Extended variable selection approaches to jointly model main and interaction effects from high-dimensional data orignally proposed by Hao and Zhang (2014) and extended by Gosik and Wu (2016). Based on a greedy forward approach, their model can identify all possible interaction effects through two algorithms, iFORT and iFORM, which have been proved to possess sure screening property in an ultrahigh-dimensional setting.

Usage

iForm(formula, data, heredity = "strong", higher_order = FALSE)

Arguments

formula	an object of class formula, or one that can be coerced to that class,: a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.
data	data.frame of your data with the response and all p predictors
heredity	a string specifying the heredity to be considered. NULL, weak, strong
higher_order	logical TRUE indicating to include order-3 interactions in the search (default FALSE)

Details

Runs the iFORM selection procedure on the dataset and returns a linear model of the final selected model. The model is of an R object of class "lm"

Value

a summary of the linear model returned after the selection procedure

Author(s)

Kirk Gosik

See Also

lm

model.frame

Examples

iForm(formula = hp ~ ., data = mtcars, heredity = "strong", higher_order = FALSE)

kdgosik/iForm documentation built on May 23, 2019, 4:05 a.m.