[R-sig-eco] Quantile regressions across several predictors

[peterhouk1 .]

Greetings -

I'm wondering if folks might be able to point out the best approach for
examining the influence of any particular quantile of many predictor
variables simultaneously?  For instance, the below data show three
potential predictors of a dependent variable, but in this case, we might
want to use the 50% quantile (i.e., mean) of each predictor.  I'm wondering
if there Is any standard approach for dealing with multiple predictors,
that when binned, can no longer be contrasted in a single model.

Thanks for any discussion and guidance,

Peter


pred 1 pred 2 pred 3 dependent
2 14 4 800.5987
2 18 11 414.1341
11 15 12 825.5466
11 15 12 1143.972
11 14 3 904.4725
11 18 15 433.1852
11 22 14 726.6624
11 16 2 1450.15
12 20 2 670.4164
12 19 7 741.6311
12 15 7 1835.707
13 18 14 810.5779
13 22 5 418.6701
13 16 12 1127.189
13 20 1 782.0013
14 21 4 875.8959
14 16 13 1077.747
14 11 9 1949.56
15 15 14 972.0584
16 20 7 1048.716
16 11 8 689.4675
16 16 11 1523.632
16 21 11 816.4746
16 14 4 1303.638
16 21 13 1270.525
16 20 2 1174.816
15 13 5 1076.839
15 17 10 808.3099
15 15 9 1324.503
15 19 7 922.1628
15 16 6 1644.743
14 13 14 864.5559
13 19 10 119.296
13 19 12 659.5301
13 18 5 1214.279
13 20 5 1511.839
13 14 8 577.8826
12 12 2 1242.402
12 14 11 1422.48
12 19 6 210.9226
12 17 14 1982.219
11 9 12 1057.788
11 18 8 1723.669
11 10 3 2188.152
11 15 10 1240.588
10 16 1 1262.361
10 20 15 1092.262
10 15 4 813.7531
10 16 12 1423.387
9 15 10 1621.156
8 21 3 1184.342
8 21 5 935.7707
8 17 2 919.8948
8 15 1 960.7185
8 16 13 1041.912
7 16 8 1633.856
7 18 15 1276.876
7 18 8 1108.591
7 17 9 844.5977
7 10 6 1681.484
6 18 3 915.3588
6 21 11 938.9458
6 16 12 1309.535
6 20 3 881.339
6 17 15 952.1002
5 19 6 803.3203
5 16 13 826.4538
5 20 10 1382.564
5 21 2 851.8552
5 19 7 1400.708
4 19 14 1411.594

-- 

Peter Houk, PhD
Assistant Professor
University of Guam Marine Laboratory
*http://guammarinelab.org/peterhouk.html
<http://guammarinelab.org/peterhouk.html>*

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