durbin.watson {car}R Documentation

Durbin-Watson Test for Autocorrelated Errors

Description

Computes residual autocorrelations and generalized Durbin-Watson statistics and their bootstrapped p-values.

Usage

durbin.watson(model, ...)

durbin.watson.lm(model, max.lag=1, simulate=T, reps=1000,
    method=c("resample","normal"))

durbin.watson.default(residuals, max.lag=1)

print.durbin.watson(x, ...)

Arguments

model a linear-model object.
max.lag maximum lag to which to compute residual autocorrelations and Durbin-Watson statistics.
simulate if TRUE p-values will be estimated by bootstrapping.
reps number of bootstrap replications.
method bootstrap method: "resample" to resample from the observed residuals; "normal" to sample normally distributed errors with 0 mean and standard deviation equal to the standard error of the regression.
residuals vector of residuals from a linear model.
... arguments to be passed down to method functions.
x durbin.watson object.

Value

Returns an object of type "durbin.watson".

Author(s)

John Fox jfox@mcmaster.ca

References

Fox, J. (1997) Applied Regression, Linear Models, and Related Methods. Sage.

Examples

data(Hartnagel)
durbin.watson(lm(fconvict ~ tfr + partic + degrees + mconvict, data=Hartnagel))
##  lag Autocorrelation D-W Statistic p-value 
##    1        0.688345     0.6168636       0
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