[R] Regression Testing

[Achim Zeileis]

On Thu, 20 Jan 2011, Mojo wrote:

> I'm new to R and some what new to the world of stats.  I got frustrated with 
> excel and found R.  Enough of that already.
>
> I'm trying to test and correct for Heteroskedasticity
>
> I have data in a csv file that I load and store in a dataframe.
>
>> ds <- read.csv("book2.csv")
>> df <- data.frame(ds)
>
> I then preform a OLS regression:
>
>> lmfit <- lm(df$y~df$x)

Just btw: lm(y ~ x, data = df) is somewhat easier to read and also easier 
to write when the formula involves more regressors.

> To test for Heteroskedasticity, I run the BPtest:
>
>> bptest(lmfit)
>
>        studentized Breusch-Pagan test
>
> data:  lmfit
> BP = 11.6768, df = 1, p-value = 0.0006329
>
> From the above, if I'm interpreting this correctly, there is 
> Heteroskedasticity present.  To correct for this, I need to calculate robust 
> error terms.

That is one option. Another one would be using WLS instead of OLS - or 
maybe FGLS. As the model just has one regressor, this might be possible 
and result in a more efficient estimate than OLS.

> From my reading on this list, it seems like I need to vcovHC.

That's another option, yes.

>> vcovHC(lmfit)
>              (Intercept)         df$x
> (Intercept)  1.057460e-03 -4.961118e-05
> df$x       -4.961118e-05  2.378465e-06
>
> I'm having a little bit of a hard time following the help pages.

Yes, the manual page is somewhat technical but the first thing the 
"Details" section does is: It points you to some references that should be 
easier to read. I recommend starting with

      Zeileis A (2004), Econometric Computing with HC and HAC Covariance
      Matrix Estimators. _Journal of Statistical Software_, *11*(10),
      1-17. URL <URL: http://www.jstatsoft.org/v11/i10/>.

That has also some worked examples.

> So is the first column the intercepts and the second column new standard 
> errors?

As David pointed out, it's the full covariance matrix estimate.

hth,
Z

> Thanks,
> mojo
>
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