durbin.watson {car} | R Documentation |
Computes residual autocorrelations and generalized Durbin-Watson statistics and their bootstrapped p-values.
durbin.watson(model, ...) ## S3 method for class 'lm': durbin.watson(model, max.lag=1, simulate=TRUE, reps=1000, method=c("resample","normal"), alternative=c("two.sided", "positive", "negative"), ...) ## Default S3 method: durbin.watson(model, max.lag=1, ...) ## S3 method for class 'durbin.watson': print(x, ...)
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. |
Returns an object of type "durbin.watson"
.
John Fox jfox@mcmaster.ca
Fox, J. (1997) Applied Regression, Linear Models, and Related Methods. Sage.
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