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
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
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
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 that if
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 tried
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:
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
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
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
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
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 model
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
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
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?
Dear Kjetil,
I don't believe that these are BLUS residuals, but since the last n - r
effects are projections onto an orthogonal basis for the residual
subspace, they should do just fine (as long as the basis vectors have the
same length, which I think is the case, but perhaps someone can
Dear Federico,
The problem is the same with GLS residuals -- even if the GLS transformation
produces homoskedastic errors, the residuals will be correlated and
heteroskedastic (with this problem tending to disappear in most instances as
n grows). The central point is that residuals don't behave
Dear Andy,
At the risk of muddying the waters (and certainly without wanting to
advocate the use of normality tests for residuals), I believe that your
point #4 is subject to misinterpretation: That is, while it is true that t-
and F-tests for regression coefficients in large sample retain their
Hi John,
Your point is well taken. I was only thinking about the shape of the
distribution, and neglected the cases of, say, symmetric long tailed
distributions. However, I think I'd still argue that other tools are
probably more useful than normality tests (e.g., robust methods, as you
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
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
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