# durbinWatsonTest: Durbin-Watson Test for Autocorrelated Errors In car: Companion to Applied Regression

## Description

Computes residual autocorrelations and generalized Durbin-Watson statistics and their bootstrapped p-values. `dwt` is an abbreviation for `durbinWatsonTest`.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```durbinWatsonTest(model, ...) dwt(...) ## S3 method for class 'lm' durbinWatsonTest(model, max.lag=1, simulate=TRUE, reps=1000, method=c("resample","normal"), alternative=c("two.sided", "positive", "negative"), ...) ## Default S3 method: durbinWatsonTest(model, max.lag=1, ...) ## S3 method for class 'durbinWatsonTest' 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. `x` `durbinWatsonTest` object.

## Value

Returns an object of type `"durbinWatsonTest"`.

## Note

p-values are available only from the `lm` method.

## Author(s)

John Fox jfox@mcmaster.ca

## References

Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.

## Examples

 `1` ```durbinWatsonTest(lm(fconvict ~ tfr + partic + degrees + mconvict, data=Hartnagel)) ```

### Example output

``` lag Autocorrelation D-W Statistic p-value
1        0.688345     0.6168636       0
Alternative hypothesis: rho != 0
```

car documentation built on June 27, 2021, 5:07 p.m.