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 <[EMAIL PROTECTED]> 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. > > > > __________________________________ SBC Yahoo! DSL - Now only $29.95 per month! ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
