Score Test for Non-Constant Error Variance

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 class 'lm'
ncvTest(model, var.formula, ...)

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

Arguments

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.

Details

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.

Value

The function returns a chisqTest object, which is usually just printed.

References

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.

Author

John Fox jfox@mcmaster.ca, Sandy Weisberg sandy@umn.edu

See also

hccm, spreadLevelPlot

Examples

ncvTest(lm(interlocks ~ assets + sector + nation, data=Ornstein))
#> Non-constant Variance Score Test 
#> Variance formula: ~ fitted.values 
#> Chisquare = 46.98537, Df = 1, p = 7.1518e-12

ncvTest(lm(interlocks ~ assets + sector + nation, data=Ornstein), 
    ~ assets + sector + nation, data=Ornstein)
#> Non-constant Variance Score Test 
#> Variance formula: ~ assets + sector + nation 
#> Chisquare = 74.73535, Df = 13, p = 1.0663e-10