car (version 1.0-2)

durbin.watson: 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=TRUE, reps=1000,
    method=c("resample","normal"),
    alternative=c("two.sided", "positive", "negative"), ...)

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

## S3 method for class 'durbin.watson':
print(x, ...)

Arguments

model

a linear-model object, or a vector of residuals from a linear model.

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.

alternative

sign of autocorrelation in alternative hypothesis; specify only if max.lag = 1; if max.lag > 1, then alternative is taken to be "two.sided".

...

arguments to be passed down to method functions.

x

durbin.watson object.

Value

• Returns an object of type "durbin.watson".

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 
##  Alternative hypothesis: rho != 0