Can you help me interpret the output I get with "polymars"?
I'd like to find a regression model to predict 2 dependent variables (then
called Y1 and Y2) with 2 independent variables (then called X1 and X2).
Here is the output:
polymars(responses = data[, 13:14], predictors = data[, 2:3])
Model fitting
0/1 size RSS 1 RSS 2 GCV
1 1 1 0.2486744 0.6520499 0.01756785
2 1 2 0.2316042 0.3820036 0.01391882
3 1 3 0.2312835 0.3819480 0.01637875
4 1 4 0.2299605 0.3766946 0.01935784
5 1 5 0.2243804 0.3766148 0.02331277
6 1 6 0.2243499 0.3764709 0.02893749
7 0 5 0.2243804 0.3766148 0.02331277
8 0 4 0.2299605 0.3766946 0.01935784
9 0 3 0.2312835 0.3819480 0.01637875
10 0 2 0.2316042 0.3820036 0.01391882
11 0 1 0.2486744 0.6520499 0.01756785
Model produced
pred1 knot1 pred2 knot2 Coefs 1 Coefs 2 SE 1 SE 2
1 0 NA 0 NA 0.40139070 0.14049628 0.02195431 0.02819553
2 2 NA 0 NA -0.02146696 -0.08538267 0.01047337 0.01345076
RESPONSES : 2
Rsquared : 0.069 0.414
If I have well understood, polymars tries to build several models for each
dependent variable (with one function if it is homogeneous or more ones if
it is not homogeneous) and selects the best one. I suppose the model is the
best one when the GCV is smallest. So here I thought I should have had a
model with 2 functions for each dependent variable (size 2).
Why do I get only one result (1 function for each dependent variable)?
How can I get a model with different functions over different ranges of the
independent variables? Do you think it would be a better model?
Are these the good equations?
Y1 = 0,40139 - 0,02147 X2
Y2 = 0,1405 - 0,08538 X2
Does polymars take the interaction between Y1 and Y2 into account ?
Thanks
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