skedastic

The purpose of the skedastic package is to make diagnostic methods for detecting heteroskedasticity in linear regression models accessible to R users.

Installation

# Install from CRAN
install.packages("skedastic", dependencies = c("Depends", "Imports"))

# Or the development version from GitHub:
install.packages("devtools")
devtools::install_github("tjfarrar/skedastic")

Usage

The purpose of the skedastic package is to make diagnostic methods for detecting heteroskedasticity in linear regression models accessible to R users. Heteroskedasticity (sometimes spelt ‘heteroscedasticity’) is a violation of one of the assumptions of the classical linear regression model (the Gauss-Markov Assumptions). This assumption, known as homoskedasticity, holds that the variance of the random error term remains constant across all observations.

Most of the functions in the package implement particular hypothesis testing methods for detecting heteroskedasticity that have been published in a journal article or book. Other functions implement graphical methods for detecting heteroskedasticity or compute transformations of the Ordinary Least Squares (OLS) residuals that are useful in heteroskedasticity detection.

Nearly all of the exported functions in the package require as an argument an
lm object (or, for advanced users desiring faster computation, a list containing the response vector and the design matrix in the exact form required by lm.fit). Thus, to use this package a user must first be familiar with how to fit linear regression models using the lm function from package stats. Note that the package currently supports only linear regression models fit using OLS.

For heteroskedasticity tests that are implemented in other R packages on CRAN, or in other statistical software such as SAS or SHAZAM, the functions in the skedastic package have been checked against them to ensure that they produce the same values of the test statistic and \(p\)-value. This is true for the following functions: breusch_pagan, cook_weisberg, glejser, goldfeld_quandt (parametric test only), harvey, and white_lm.

Here is an example of implementing the Breusch-Pagan Test for heteroskedasticity on a linear regression model fit to the cars dataset, with distance (cars$dist) as the response (dependent) variable and speed (cars$speed) as the explanatory (independent) variable.

library(skedastic)
mylm <- lm(dist ~ speed, data = cars)
bp <- breusch_pagan(mylm)
bp
attributes(bp)
bp$statistic
bp$p.value

To compute BLUS residuals for the same model:

myblusres <- blus(mylm, omit = "last")
myblusres
# Plot OLS residuals against BLUS residuals
plot(mylm$residuals, myblusres)

To create customised residual plots for the same model:

hetplot(mylm, horzvar = c("explanatory", "log_explanatory"), vertvar = c("res", "res_stud"), vertfun = "2", filetype = NULL)

Learn More

No vignettes have been created yet for this package. Watch this space.