Ramsey's RESET test for nonlinearity

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

Adds powers of the fitted values and performs an F-test to check for neglected nonlinearity.

Usage

performRESETTest(model, data = NULL, power = 2:3)

Arguments

model

A fitted lm model.

data

optional data frame the model was fitted to, or NULL, the default. The statistic is computed from the response, fitted values, design, weights and offset of the fit itself, so data does not change the result; it is accepted so that every test takes (model, data, ...); since 0.16.0 its rows are matched to the observations of the fit as by every test, data with row names of their own in any order and data that number their rows in the order of the fit, and other data are refused, where it needed only the variables of the model and the row names of the fit. Before 0.15.0 the second argument was power: a number passed there by position is still read as power in 0.15.x, with a warning that shows the new call, and stops working in 1.0.0.

power

Numeric vector of powers to include.

Value

An object of class htest.

Details

On a fit with weights the augmented model is fitted by weighted least squares and both residual sums of squares are weighted. Releases before 0.12.0 refitted the augmented model without the weights, so the two models were not nested.

On a fit with an offset the fitted values are the design times the coefficients plus the offset, and the test is the nested F test of the model with its offset against the model augmented with the powers of those fitted values, fitted with the same offset and weights (since 0.16.0, decided 2026-10-18). It is not RESET of the model fitted to the response minus the offset, which augments that model with the powers of the design times the coefficients alone. Before 0.16.0 the augmented model was fitted without the offset, so the two models were not nested.

Supported models

It accepts an lm and a glm with the Gaussian family and the identity link, weighted or not, 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. A fit with a zero weight is accepted too: the test compares two weighted least-squares fits, which both leave the observation out, and gives the value of the fit to the observations with positive weight (kept so, decided on 2026-10-17). It accepts a model with an offset, given as an argument or as an offset() term, weighted or not, and tests the model with its offset (since 0.16.0; see Details). It refuses a glm of another family or link, a survey::svyglm() fit, a coefficient that is not finite for another reason, data that lack rows or variables of the fit, and, since 0.16.0, a fit with model = FALSE whose model frame, rebuilt from the data it was fitted to, no longer holds its response, because those data changed after the fit: it read that frame and returned a value. A fit with model = FALSE whose data are gone is tested on data that give its response and fitted values (since 0.16.0; it refused such a fit until the tests computed from the model alone were built on the model context), and refused without them. Since then it also refuses numbered data in another order than the fit, rows in front of those of the fit and repeated row names, as every test does, where it gave the value of the fit. It refuses a multivariate lm ("needs a model with one response"), which it tested without data before 0.16.0, and, since 0.16.0, an mgcv::gam() fit with penalised smooth terms, which is not the least-squares fit of its design, the basis of its smooths, that the test fits again. Since 0.17.0 a gam that is the least-squares fit of its design, as one whose smooth terms are all fixed (s(x, fx = TRUE)), is tested on that design and gives the value of the lm() of it; before, it was refused as a penalised one. A gam without smooth terms is tested as the Gaussian glm it is (decisions 37 and 38 of the design of the model context). Which model each test accepts, and why, is set out in test_results, under Supported models.

Examples

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