On Sep 4, 2013, at 10:39 PM, Euna Jeong wrote:
Hi,
I have questions about R2 used in pls (or multivariate analysis).
Is R2 same with the square of the PCC (Pearson Correlation
Coefficient)?
I found the following description from wiki (Coefficient of
determination)
------------------------
Similarly, in linear least squares regression with an estimated
intercept
term, R2 equals the square of the Pearson correlation coefficient
between
the observed and modeled (predicted) data values of the dependent
variable.
-------------------------
If so, Q2 (R2 of cross validation) should range between 0 and 1.
But it doesn't. I got negative values of Q2 when running my dataset.
Of course, from the definition of Q2, Q2 can be negative when my
model is
not at all predictive.
My question is what the relationship between R2 and pcc^2 is.
"Adjusted R-squareds" can become negative when the adjustment for the
added number of predictors overwhelms the increased model fit on the
scale of adjustment.
Do a search of the archives for negative r-squared. Here's just one of
many:
http://r-project.markmail.org/search/?q=list%3Aorg.r-project.r-help%20%20negative%20r-squared#query
:list%3Aorg.r-project.r-help%20%20negative%20r-squared+page:1+mid:rhiqm5bcm4maxnef+state:results
--
David Winsemius, MD
Alameda, CA, USA
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