Regression Testing

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.