Package 'mlrpro'

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)

Arguments

Data	a data frame containing the variables in the model.
Y	the response variable.
Column_Y	the column response variable.
Alpha	significance level.

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.

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)

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