[R] Testing for normality of residuals in a regression model
Kjetil Brinchmann Halvorsen
kjetil at acelerate.com
Fri Oct 15 16:12:28 CEST 2004
John Fox wrote:
>A problem with applying a standard test of normality to LS residuals is that
>the residuals are correlated and heterskedastic even if the standard
>assumptions of the model hold. In a large sample, this is unlikely to be
>problematic (unless there's an unusual data configuration), but in a small
>sample the effect could be nontrivial.
>One approach is to use BLUS residuals, which transform the LS residuals to a
>smaller set of uncorrelated, homoskedastic residuals (assuming the
>correctness of the model). A search of R resources didn't turn up anything
>for BLUS, but they shouldn't be hard to compute. This is a standard topic
>covered in many econometrics texts.
>You might consider the alternative of generating a bootstrapped confidence
>envelope for the QQ plot; the qq.plot() function in the car package will do
>this for a linear model.
>I hope this helps,
>Department of Sociology
>Canada L8S 4M4
>>From: r-help-bounces at stat.math.ethz.ch
>>[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of
>>Sent: Friday, October 15, 2004 7:44 AM
>>To: R-help at stat.math.ethz.ch
>>Subject: [R] Testing for normality of residuals in a regression model
>>Is it possible to have a test value for assessing the
>>normality of residuals from a linear regression model,
>>instead of simply relying on qqplots?
>>I've tried to use fitdistr to try and fit the residuals with
>>a normal distribution, but fitdsitr only returns the
>>parameters of the distribution and the standard errors, not
>>the p-value. Am I missing something?
>>R-help at stat.math.ethz.ch mailing list
>>PLEASE do read the posting guide!
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