[pymvpa] regression analysis to predict subject-specific score

[Yaroslav Halchenko]

indeed we have no good tutorial/example for regressions yet besides one
for GPR

doc/examples/gpr.py

also we interface to SVR regressions (from libsvm and shogun) and started
to add interfaces to scikits.learn.  Some samples of them are available from
regrswh, so  on my system:

*In [5]: print '\n'.join([str(x) for x in regrswh[:]])
<libsvm epsilon-SVR>
<libsvm nu-SVR>
<sg.LinSVMR()/libsvr>
<skl.PLSRegression_1d()>
<skl.LARS()>
<skl.LassoLARS()>
<GPR(kernel='linear')>
<GPR(kernel='sqexp')>

As for "howto" -- just the same way you use classifiers -- then ConfusionMatrix
in .stats would be replaced with RegressionStatistics. e.g.

In [5]: from mvpa.suite import *

In [6]: from mvpa.testing.datasets import datasets as testing_datasets

In [7]: cve = CrossValidation(regrswh[:][0], NFoldPartitioner(), postproc=mean_sample(), errorfx=corr_error, enable_ca=['training_stats', 'stats'])

In [8]: print cve(testing_datasets['chirp_linear'])
<Dataset: 1x1 at float64, <sa: cvfolds>>

In [9]: print cve.ca.stats
Statistics  Mean  Std   Min       Max
---------- ----- ----- -----     -----
Data:
   RMP_t   0.668 0.015 0.639     0.681
   STD_t   0.661 0.015 0.631     0.675
   RMP_p   0.644 0.043 0.593     0.731
   STD_p   0.637 0.042 0.583     0.721
Results:
    CCe     0.06 0.016 0.036     0.084
   RMSE    0.232 0.027 0.184     0.266
RMSE/RMP_t 0.348 0.043  0.27      0.4
Summary:
    CCe     0.06         p=  3.65268e-137
   RMSE     0.23
RMSE/RMP_t  0.35
 # of sets   6


On Mon, 08 Aug 2011, Zhen Zonglei wrote:

>    Hi Guys:



>    I am trying to do multivariate regression analysis to predict
>    subject-specific score with pymvpa 0.6. But, I did not find some
>    examples about this in the manual. What regressions are implemented in
>    the toolbox? Could you please show me how to do regression analysis in
>    the toolbox?


>    Best


>    Zonglei Zhen

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