[R] Regression Testing
Achim.Zeileis at uibk.ac.at
Thu Jan 20 22:42:26 CET 2011
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:
> 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.
> (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
As David pointed out, it's the full covariance matrix estimate.
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> and provide commented, minimal, self-contained, reproducible code.
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