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.
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