[R] Regression Testing

Mojo mojo at sispyrc.com
Fri Jan 21 15:10:29 CET 2011

On 1/20/2011 4:42 PM, Achim Zeileis wrote:
> 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.

I thought that WLS (which I guessing is a weighted regression) is really 
only useful when you know or at least have an idea of what is causing 
the Heteroskedasticity?  I'm not familiar with FGLS.  I plan on adding 
additional independent variables as I get more comfortable with everything.

>> 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/>.

I will look into that.


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