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Type: Package
Title: Shapley Value Regression for Relative Importance of Attributes
Version: 0.2.0
Author: Jingyi Liang
Maintainer: Jingyi Liang <jingyiliang19@163.com>
Description: Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
Imports: tidyverse,kableExtra,MASS,utils
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2021-07-27 02:54:36 UTC; liji0004
Repository: CRAN
Date/Publication: 2021-07-27 04:50:01 UTC

ShapleyValueRegression – to calculate the relative importance of attributes in linear regression

Description

Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity.

Arguments

y A coloumn or data set of the dependent variable

x A matrix or data set of the independent variables

Value

The structure of the output is a datatable, with two rows:the unstandardized and standardized relative importance of each attributes using shapley value regression method.

Examples

library(MASS)
library(tidyverse)
data <- Boston
y <- data$medv
x <- as.data.frame(data[,5:8])
shapleyvalue(y,x)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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