# ncvTest

##### 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.

- Keywords
- regression, htest

##### Usage

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

# S3 method for 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. (2008)
*Applied Regression Analysis and Generalized Linear Models*,
Second Edition. Sage.

Fox, J. and Weisberg, S. (2011)
*An R Companion to Applied Regression*, Second Edition, Sage.

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

##### See Also

##### Examples

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

*Documentation reproduced from package car, version 2.1-6, License: GPL (>= 2)*