[R] Regression Testing
dwinsemius at comcast.net
Thu Jan 20 21:37:26 CET 2011
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.
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT
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