[R] Regression Testing

[David Winsemius]

On Jan 20, 2011, at 2:08 PM, 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)
>
> 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.  From my reading on this list, it  
> seems like I need to vcovHC.
>
> > 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.  So  
> is the first column the intercepts and the second column new  
> standard errors?

No, It's a variance-covariance matrix, so all of the elements are  
variance estimates. To get what you are expecting ... the SE's of the  
coefficients (which are the diagonal elements of a var-covar  
matrix,   .... you would wrap sqrt(diag(.)) around that object.


>
> Thanks,
> mojo
>
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David Winsemius, MD
West Hartford, CT