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 = 0.0006329From 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-06I'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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help 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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
