Computes a score test of the hypothesis of constant error variance against the alternative that the error variance changes with the level of the response (fitted values), or with a linear combination of predictors.

`ncvTest(model, ...)`# S3 method for lm
ncvTest(model, var.formula, ...)

# S3 method for glm
ncvTest(model, ...) # to report an error

model

a weighted or unweighted linear model, produced by `lm`

.

var.formula

a one-sided formula for the error variance; if omitted, the error variance depends on the fitted values.

…

arguments passed down to methods functions; not currently used.

The function returns a `chisqTest`

object, which is usually just printed.

This test is often called the Breusch-Pagan test; it was independently suggested with some extension by Cook and Weisberg (1983).

`ncvTest.glm`

is a dummy function to generate an error when a `glm`

model is used.

Breusch, T. S. and Pagan, A. R. (1979)
A simple test for heteroscedasticity and random coefficient variation.
*Econometrica* **47**, 1287--1294.

Cook, R. D. and Weisberg, S. (1983)
Diagnostics for heteroscedasticity in regression.
*Biometrika* **70**, 1--10.

Fox, J. (2016)
*Applied Regression Analysis and Generalized Linear Models*,
Third Edition. Sage.

Fox, J. and Weisberg, S. (2019)
*An R Companion to Applied Regression*, Third Edition, Sage.

Weisberg, S. (2014) *Applied Linear Regression*, Fourth Edition, Wiley.

# NOT RUN { ncvTest(lm(interlocks ~ assets + sector + nation, data=Ornstein)) ncvTest(lm(interlocks ~ assets + sector + nation, data=Ornstein), ~ assets + sector + nation, data=Ornstein) # }