On Fri, 15 Oct 2004, Kjetil Brinchmann Halvorsen wrote:
Liaw, Andy wrote:
Also, I was told by someone very smart that fitting OLS to data with
heteroscedastic errors can make the residuals look `more normal' than they
really are... Don't know how true that is, though.
Certainly true, since the re
I am assuming everyone is on R-help and doesn't want two copies so have
trimmed the Cc: list to R-help.
On Sat, 16 Oct 2004, Philippe Grosjean wrote:
> > Prof Brian Ripley wrote:
[ Other contributions previously excised here without comment. ]
> > >>However, stats 901 or some such tells you th
> Prof Brian Ripley wrote:
>
> >>However, stats 901 or some such tells you that if the distributions
> >>have even slightly longer tails than the normal you can get much
> >>better estimates than OLS, and this happens even before a test of
> >>normality rejects on a sample size of thousands.
>
Prof Brian Ripley wrote:
However, stats 901 or some such tells you that if the distributions have
even slightly longer tails than the normal you can get much better
estimates than OLS, and this happens even before a test of normality
rejects on a sample size of thousands.
Robustness of efficien
Liaw, Andy wrote:
.
.
.
.
Also, I was told by someone very smart that fitting OLS to data with
heteroscedastic errors can make the residuals look `more normal' than they
really are... Don't know how true that is, though.
Certainly true, since the residuals will be a kind of average, so the
C
OK, I'll expose myself:
I tend to do normal probability plots of residuals (usely deletion
/ studentized residuals as described by Venables and Ripley in Modern
Applied Statistics with S, 4th ed, MASS4). If the plots look strange, I
do something. I'll check apparent outliers for co
ster 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 Liaw, Andy
> > Sent: Friday, Octob
On Fri, 15 Oct 2004, Liaw, Andy wrote:
> Let's see if I can get my stat 101 straight:
>
> We learned that linear regression has a set of assumptions:
>
> 1. Linearity of the relationship between X and y.
> 2. Independence of errors.
> 3. Homoscedasticity (equal error variance).
> 4. Normality of
, October 15, 2004 11:55 AM
> To: 'Federico Gherardini'; Berton Gunter
> Cc: R-help mailing list
> Subject: RE: [R] Testing for normality of residuals in a
> regression model
>
> Let's see if I can get my stat 101 straight:
>
> We learned that linear regression
rom: [EMAIL PROTECTED]
> [mailto:[EMAIL PROTECTED] On Behalf Of
> Federico Gherardini
> Sent: Friday, October 15, 2004 11:22 AM
> To: [EMAIL PROTECTED]
> Subject: Re: [R] Testing for normality of residuals in a
> regression model
>
> Thank you very much for your suggestions! The
cmaster.ca/jfox
> -Original Message-
> From: Kjetil Brinchmann Halvorsen [mailto:[EMAIL PROTECTED]
> Sent: Friday, October 15, 2004 9:12 AM
> To: John Fox
> Cc: 'Federico Gherardini'; [EMAIL PROTECTED]
> Subject: Re: [R] Testing for norm
Let's see if I can get my stat 101 straight:
We learned that linear regression has a set of assumptions:
1. Linearity of the relationship between X and y.
2. Independence of errors.
3. Homoscedasticity (equal error variance).
4. Normality of errors.
Now, we should ask: Why are they needed? Can
>
> Berton Gunter wrote:
>
> >>>Exactly! My point is that normality tests are useless for
> this purpose for
> >>>reasons that are beyond what I can take up here.
> >>>
> Thanks for your suggestions, I undesrtand that! Could you
> possibly give
> me some (not too complicated!)
> links so tha
Berton Gunter wrote:
Exactly! My point is that normality tests are useless for this purpose for
reasons that are beyond what I can take up here.
Thanks for your suggestions, I undesrtand that! Could you possibly give
me some (not too complicated!)
links so that I can investigate this matter furt
Berton Gunter wrote:
Quite right, John!
I have 2 additional questions:
1) Why test for normality of residuals? Suppose you reject -- then what?
(residual plots may give information on skewness, multi-modality, data
"anomalies" that can affect the data analysis).
Because I want to know if my mode
ni'; [EMAIL PROTECTED]
> Subject: RE: [R] Testing for normality of residuals in a
> regression model
>
> 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
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 confi
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 confi
Thank you very much for your suggestions! The residuals come from a gls
model, because I had to correct for heteroscedasticity using a weighted
regression... can I simply apply one of these tests (like shapiro.test)
to the standardized residuals from my gls model?
Cheers,
Federico
_
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
Hi Frederico,
take also a look at the package "nortest":
help(package="nortest")
Best,
Dimitris
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven
Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/16/396887
Fax: +32/16/337015
Web: h
What about shapiro.test(resid(fit.object))
Stefano
On Fri, Oct 15, 2004 at 02:44:18PM +0200, Federico Gherardini wrote:
> 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 tr
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