John Fox wrote:
I'm not sure if this are BLUE residuals, but the following function transform to aDear 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).
smaller set of independent, homoscedastic residuals and the calls shapiro.test:
I've proposed to make this a method for shapiro.test for "lm" objects, but it is
not accepted.
shapiro.test.lm
function (obj)
{
eff <- effects(obj)
rank <- obj$rank
df.r <- obj$df.residual
if (df.r < 3)
stop("To few degrees of freedom for residual for the test.")
data.name <- deparse(substitute(obj))
x <- eff[-(1:rank)]
res <- shapiro.test(x)
res$data.name <- data.name
res$method <- paste(res$method, " for residuals of linear model")
res
}Kjetil
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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--
Kjetil Halvorsen.
Peace is the most effective weapon of mass construction.
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