One last thing. At the outset of this discussion I provided the results of a
validation procedure on a model (see below). As discussed previously, the
model overall seems to fair well, with the exception of the slope. With
that in mind, is there a way to correct the coefficients of the model to
account for the corrected slope so that future predictions on a new data set
are more accurate? Or is that not recommended at all?
index.orig training test optimism index.corrected n
Dxy 0.9932 0.9940 0.9905 0.0035 0.9897 363
R2 0.9291 0.9364 0.9163 0.0202 0.9089 363
Intercept 0.0000 0.0000 0.0233 -0.0233 0.0233 363
Slope 1.0000 1.0000 0.7836 0.2164 0.7836 363
Emax 0.0000 0.0000 0.0582 0.0582 0.0582 363
D 0.9118 0.9190 0.8915 0.0275 0.8844 363
U -0.0110 -0.0110 0.0124 -0.0234 0.0124 363
Q 0.9228 0.9299 0.8791 0.0508 0.8720 363
B 0.0205 0.0172 0.0239 -0.0067 0.0272 363
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