outlier

Hi, many thanks for your advice. I appreciate very
much. Maybe I can make the question more clear: I want
to evaluate the correlation between two variables: one
is the actual outputs of a system, another is the
predicted values of the outputs of the system using
neural networks. When I made scatterplots in excel, I
can get the linear equation and the corresponding
R-squared. In the bottom of the page
http://www.statsoftinc.com/textbook/stathome.html, it
mentioned that sometimes outliers will affect
correlation coefficient biasly. So I thought it might
be worth to remove outlier before  calculating
R-squared in R. It seems to be a bad idea according to
your comments. 

Now can you make comments on how to
evaluate the performance of the neural network model
in predicting the actual outputs?

Kan 

--- Spencer Graves <spencer.graves at PDF.COM> wrote:

	  It is also wise to make scatterplots, as shown by
the famous examples 
produced of 4 scatterplots with the same R^2, where
the first shows the 
standard ellipsoid pattern implied by the
assumptions while the other 
three indicate very clearly that the assumptions are
incorrect.  See 
Anscombe (1973) "Graphs in Statistical Analysis",
The American 
Statistician, 27: 17-22, reproduced in, e.g., du
Toit, Steyn and Stumpf 
(1986) Graphical Exploratory Data Analysis
(Springer).

hth.  spencer graves

Prof Brian Ripley wrote:

On Tue, 17 Jun 2003, kan Liu wrote:

I want to calculate the R-squared between two

variables. Can you advice

me how to identify and remove the outliers before

performing R-squared

calculation?

Easy: you don't.  It make no sense to consider R^2

after arbitrary outlier 

removal: if I remove all but two points I get R^2

= 1!

R^2 is normally used to measure the success of a

multiple regression, but 

as you mention two variables, did you just mean

the Pearson 

product-moment correlation?  It makes more sense

to use a robust measure 

of correlation, as in cov.rob (package lqs) or

even Spearman or Kendall 

measures (cov.test in package ctest).

If you intended to do this for a multiple

regression, you need to do some 

sort of robust regression and a use a robust

measure of fit.