shapleyvalue: ShapleyValueRegression - to calculate the relative importance...
In ShapleyValue: Shapley Value Regression for Relative Importance of Attributes
Description
Arguments
Value
Examples
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
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library(MASS)
library(tidyverse)
data <- Boston
y <- data$medv
x <- as.data.frame(data[,5:8])
shapleyvalue(y,x)
ShapleyValue documentation built on July 27, 2021, 9:07 a.m.
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Description Arguments Value Examples
Description
Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity.
Arguments
y A coloumn or data set of the dependent variable
x A matrix or data set of the independent variables
Value
The structure of the output is a datatable, with two rows:the unstandardized and standardized relative importance of each attributes using shapley value regression method.
Examples
1 2 3 4 5 6 | library(MASS)
library(tidyverse)
data <- Boston
y <- data$medv
x <- as.data.frame(data[,5:8])
shapleyvalue(y,x)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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