mlrpro (version 0.1.2)

mlrpro-package: Perform stepwise regression with verifying assumptions and identifying possible Box-Cox transformation

Description

A tool for multiple regression, select independent variables, check multiple linear regression assumptions and identify possible.

Usage

mlrpro(Data,Y,Column_Y,Alpha)

Value

An object of class mlrpro is a list containing at least the following components:

coefficients

a named vector of coefficients.

residuals

the residuals, that is response minus fitted values.

fitted.values

the fitted mean values.

rank

the numeric rank of the fitted linear model.

df.residual

the residual degrees of freedom.

call

the matched call.

terms

the terms object used.

model

if requested (the default), the model frame used.

lambda

lambda value utilized in the data conversion.

Arguments

Data

a data frame containing the variables in the model.

Y

the response variable.

Column_Y

the column response variable.

Alpha

significance level.

Examples

data(trees)
Model1 <- mlrpro(Data = trees,Y = trees$Volume, Column_Y = 3, Alpha = 0.05)
## or ##
data(mtcars)
Model2 <- mlrpro(Data = mtcars,Y = mtcars$mpg, Column_Y = 1 , Alpha = 0.01)