ncvTest: Score Test for Non-Constant Error Variance
Description
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.
Usage
ncvTest(model, ...)
## S3 method for class 'lm':
ncvTest(model, var.formula, data=NULL, subset, na.action, ...)
## 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.
data
an optional data frame containing the variables in the model.
By default the variables are taken from the environment from which
ncvTest is called. The data argument may therefore need to be specified even when
t
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 NAs.
The default is set by the na.action setting of options.
...
arguments passed down to methods functions.
Value
The function returns a chisqTest object, which is usually just printed.
Details
This test is often called the Breusch-Pagan test; it was independently
suggested by Cook and Weisberg (1983).
ncvTest.glm is a dummy function to generate an error when a glm
model is used.
References
Breusch, T. S. and Pagan, A. R. (1979)
A simple test for heteroscedasticity and random coefficient variation.
Econometrica47, 1287--1294.
Cook, R. D. and Weisberg, S. (1983)
Diagnostics for heteroscedasticity in regression.
Biometrika70, 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. (2005) Applied Linear Regression, Third Edition, Wiley.