John Fox wrote:
Dear Federico,
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,
John
--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox
--------------------------------
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of
Federico Gherardini
Sent: Friday, October 15, 2004 7:44 AM
To: [EMAIL PROTECTED]
Subject: [R] Testing for normality of residuals in a regression model
Hi all,
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?
Cheers,
Federico
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