On 1/20/2011 3:37 PM, David Winsemius wrote:
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.
Perfect. Thank you very much!
Mojo
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