[R] Regression Testing
rstuff.miles at gmail.com
Fri Jan 21 02:41:30 CET 2011
Perhaps the easiest way to incorporate the heteroskedasticity
consistent SE's and output them in a familiar and easy to interpret
format is to use coeftest() in the lmtest package.
On Jan 20, 2011, at 4:42 PM, Achim Zeileis wrote:
> On Thu, 20 Jan 2011, 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)
> Just btw: lm(y ~ x, data = df) is somewhat easier to read and also
> easier to write when the formula involves more regressors.
>> To test for Heteroskedasticity, I run the BPtest:
>> 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.
> That is one option. Another one would be using WLS instead of OLS -
> or maybe FGLS. As the model just has one regressor, this might be
> possible and result in a more efficient estimate than OLS.
>> From my reading on this list, it seems like I need to vcovHC.
> That's another option, yes.
>> (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.
> Yes, the manual page is somewhat technical but the first thing the
> "Details" section does is: It points you to some references that
> should be easier to read. I recommend starting with
> Zeileis A (2004), Econometric Computing with HC and HAC Covariance
> Matrix Estimators. _Journal of Statistical Software_, *11*(10),
> 1-17. URL <URL: http://www.jstatsoft.org/v11/i10/>.
> That has also some worked examples.
>> So is the first column the intercepts and the second column new
>> standard errors?
> As David pointed out, it's the full covariance matrix estimate.
>> 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.
> 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.
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