ncv.test
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
ncv.test(model, ...)
## S3 method for class 'lm':
ncv.test(model, var.formula, data=NULL, subset, na.action, ...)
## S3 method for class 'glm':
ncv.test(model, ...)
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.
- data
- an optional data frame containing the variables in the model.
By default the variables are taken from the environment from which
ncv.test
is called. - subset
- an optional vector specifying a subset of observations to be used.
- na.action
- a function that indicates what should happen when the data contain
NA
s. The default is set by thena.action
setting ofoptions
. - ...
- arguments passed down to methods functions.
Details
This test is often called the Breusch-Pagan test; it was independently
suggested by Cook and Weisberg (1983).
ncv.test.glm
is a dummy function to generate an error when a glm
model is used.
Value
- The function returns a
chisq.test
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. (1997) Applied Regression, Linear Models, and Related Methods. Sage.
See Also
Examples
ncv.test(lm(interlocks~assets+sector+nation, data=Ornstein))
## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 46.98537 Df = 1 p = 7.151835e-12
ncv.test(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 = 1.066320e-10