Perform Park test for heteroscedasticity

Source: man/performParkTest.Rd · Help: ?performParkTest

Regresses the log of squared residuals on the log of a suspected variable.

Usage

performParkTest(model, data = NULL, variable)

Arguments

model

an object of class lm.

data

The data frame model was fitted to, or NULL, the default. The residuals and weights are read from the fit itself, and variable from the rows of data that hold the observations of the fit. Without data it is read from the model frame of model, which holds it only when it is a variable of the model under its own name (x, not log(x)); a model that transforms the variable needs data, as does a variable the model does not use. Since 0.16.0, data that are given are matched to the observations of the fit: rows with row names of their own by name, in any order; rows that only number themselves (a tibble, a data.table, a data frame whose row names were reset) by the row numbers of the fit, or in the order of the fit when they hold one row per observation. The rows the fit dropped may be there or not. Data whose rows cannot be shown to hold the observations of the fit are refused; see test_results.

variable

name of the suspected explanatory variable.

Value

An object of class htest containing the t statistic, p-value and degrees of freedom.

Details

The slope coefficient from \(\log(e^2)\) regressed on \(\log x\) is tested for zero using a t statistic. A significant coefficient indicates scale depending on \(x\).

Supported models

It accepts an lm and a glm with the Gaussian family and the identity link, unweighted or with positive weights, a fitWLS() fit and, since 0.16.0, a rank-deficient fit, which it tests on the estimable part of its design: it gives the value of the same model fitted without the aliased columns. It refuses a glm of another family or link, a survey::svyglm() fit, a fit with a zero weight, a coefficient that is not finite for another reason, and data that lack rows or variables of the fit. It refuses a multivariate lm ("needs a model with one response"), since 0.16.0; before, it stopped with a coercion error. An mgcv::gam() fit is tested as before 0.16.0, with penalised smooth terms too: the test reads its residuals and does not fit it again, so it does not take them for least-squares residuals (decision 37 of the design of the model context); since 0.16.0 its observations are matched to the rows of data by the row names of its model frame. Before, they were matched by position, since the residuals of a gam carry no names, and data with the rows of the fit in another order gave a wrong value. Which model each test accepts, and why, is set out in test_results, under Supported models.

References

Park, R. E. (1966). Estimation with heteroscedastic error terms. Econometrica, 34(4), 888. doi:10.2307/1910108

Examples

 data(mtcars)
 m <- lm(mpg ~ wt + qsec, data = mtcars)
 performParkTest(m, mtcars, "wt")