In UBC-MDS/linregasm: Package for testing linear and multiple linear regression assumptions

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linregasm

Package for testing linear and multiple linear regression assumptions

This package is built to contain functions to be able to quickly and easily test the linearity assumptions before preforming linear regression or multiple linear regression for a specified dataset.

The package contains 3 functions one for checking multicolliniarity, one for checking constant variance and one for checking normality in the residuals. The three assumptions should be satisfied to ensure the effectiveness of a linear regression model for a particular dataset and are described as follows:

• No Multicollinearity: individual predictors within a model should not be linearly correlated to avoid unstable linear estimators
• Constant Variance of Residuals (homoscedasticity): Since data should be individually and identically distributed, the residuals should be independent of fitted values
• Normality of residuals: Since the conditional expectation of the predicted value should be normal, the error terms of the resulting model should also be normally distributed

Function 1: Multicolliniarity.

• Takes a R dataframe and a VIF threshold and checks if any of the calculated vif values exceed the given threshold. If so, the function will advise the user that this assumtion is violated, and vice versa.

• Returns the Calculated VIF values and a statement telling the user whether or not the assumpton is violated.

Function 2: Constant Variance.

• Takes a R dataframe for the predictors, a R dataframe for the response and some sort of variability threshold and checks if the variabiliy of the residuals is contant by comparing it to the given threshold. If the threshold is exceeded the function will advise the user that this assumtion is violated, and vice versa.

• Returns a plot of the fitted values vs residuals, the calculated variability value and a statement telling the user whether or not the assumpton is violated.

Function 3: Normality.

• Takes a R dataframe for the predictors, a R dataframe for the response and performs a shapiro wilk test for normality. If the P-value of the test does not exceed the threshold, the function will advise the user that this assumtion is violated, and vice versa.

• Returns the Calculated P-value and a statement telling the user whether or not the assumpton is violated.

Installation

You can install the most revent version of linregasm from github using the following code:

library(devtools)
install_github("UBC-MDS/linregasm")

You can install the released version of linregasm from CRAN with:

install.packages("linregasm")

Usage

To use linregasm to check linear regression assumptions in your own projects, see the following usage example.

Test for Normality To test for normality, use the normality() function and pass in your data and linear regression formula as follows:

norm_results <- normality(data, formula)

Test for Homoscedasticity To test for homoscedasticity, use the homoscedasticity() function as follows:

hsc_results <- linregasm::homoscedasticity(iris_df, formula)

Test for Multicollinearity To test for homoscedasticity, use the multicollinearity() function as follows:

mult_results <- linregasm::multicollinearity(iris_df, formula)

Ecosystem

As of January 2022, there are functions and packages which contain tools for linear regression evaluation such as plot() in base R, or autoplot() in ggfortify, which generate evaluation plots. Additionally, there are many linear regression reporting tools contained in packages such as broom, which tidies and reports linear regression statistics. The linregasm package seeks to build upon existing R packages, and to incorporate additional statistical tests that were not previously present. This package aggregates the functions offered by base R, broom, and more, seeking to build upon them for the purpose of evaluating linear regression models. This is intended to make it more accessible for users to access the functionality of the previously mentioned packages, as well as improve the clarity of results.

Contributing

Authors: Yair Guterman, Hatef Rahmani, Song Bo Andy Yang
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

linregasm was created by Yair Guterman, Hatef Rahmani, Song Bo Andy Yang . It is licensed under the terms of the MIT license.

UBC-MDS/linregasm documentation built on Feb. 6, 2022, 7 a.m.