Peter: Your question is not quite clear to me. I thought at first you might be talking about quantile regression but then you mentioned the 50% quantile (which is not the mean) of the predictor and binning. So I'm not sure exactly what you are after. But under the presumption that you might really be thinking along the lines of quantile regression (which does not require binning by predictors), I took your example data and ran it through a linear quantile regression from quantreg package, where quantiles of the continuous dependent variable are estimated conditional on an additive effect of the three predictors provided. Some summary output below for 0.10, 0.25, 0.50, 0.75, and 0.90 quantiles. Here it looks as if only pred2 has a strong nonzero (negative) effect for the upper quantiles (0.50, 0.75, and 0.90) of the dependent variable based on 95% confidence intervals not overlapping zero. If this is along the lines of what you were thinking about, then perhaps you can frame you question in a more focused fashion and I might be able to provide better advice. There is much more that can be done with quantile regression. Plotting this sort of summary info is especially useful.
Brian example.qr.results <- rq(dependent ~ pred1 + pred2 + pred3,data=example.data,tau=c(0.10,0.25,0.50,0.75,0.90)) summary(example.qr.results,se="rank",iid=F,alpha=0.05) Call: rq(formula = dependent ~ pred1 + pred2 + pred3, tau = c(0.1, 0.25, 0.5, 0.75, 0.9), data = example.data) tau: [1] 0.1 Coefficients: coefficients lower bd upper bd (Intercept) 1665.53049 -17.44156 2493.10597 pred1 8.81923 -40.77369 53.37269 pred2 -57.39947 -85.39144 23.59046 pred3 -19.74443 -60.76278 61.19992 Call: rq(formula = dependent ~ pred1 + pred2 + pred3, tau = c(0.1, 0.25, 0.5, 0.75, 0.9), data = example.data) tau: [1] 0.25 Coefficients: coefficients lower bd upper bd (Intercept) 1231.52601 821.28092 1935.37219 pred1 -2.25995 -29.68130 30.79243 pred2 -20.83135 -62.10712 3.75916 pred3 -3.51839 -23.45116 13.38838 Call: rq(formula = dependent ~ pred1 + pred2 + pred3, tau = c(0.1, 0.25, 0.5, 0.75, 0.9), data = example.data) tau: [1] 0.5 Coefficients: coefficients lower bd upper bd (Intercept) 1714.10796 729.52807 2553.46234 pred1 2.02560 -39.70704 29.34070 pred2 -41.81862 -81.38048 -4.06101 pred3 2.90515 -18.68419 21.02118 Call: rq(formula = dependent ~ pred1 + pred2 + pred3, tau = c(0.1, 0.25, 0.5, 0.75, 0.9), data = example.data) tau: [1] 0.75 Coefficients: coefficients lower bd upper bd (Intercept) 2118.28691 1186.20556 3496.67829 pred1 17.75399 -38.41521 32.63466 pred2 -62.43047 -113.90480 -15.35846 pred3 10.53731 -41.48255 35.23541 Call: rq(formula = dependent ~ pred1 + pred2 + pred3, tau = c(0.1, 0.25, 0.5, 0.75, 0.9), data = example.data) tau: [1] 0.9 Coefficients: coefficients lower bd upper bd (Intercept) 2855.31941 1631.16351 4217.13007 pred1 1.31388 -71.21536 65.65507 pred2 -77.54635 -106.11297 -30.33534 pred3 1.74284 -63.49143 56.91477 Warning messages: 1: In rq.fit.br(x, y, tau = tau, ci = TRUE, ...) : Solution may be nonunique 2: In rq.fit.br(x, y, tau = tau, ci = TRUE, ...) : Solution may be nonunique 3: In rq.fit.br(x, y, tau = tau, ci = TRUE, ...) : 4.22535211267606 percent fis <=0 4: In rq.fit.br(x, y, tau = tau, ci = TRUE, ...) : Solution may be nonunique > Brian S. Cade, PhD U. S. Geological Survey Fort Collins Science Center 2150 Centre Ave., Bldg. C Fort Collins, CO 80526-8818 email: ca...@usgs.gov <brian_c...@usgs.gov> tel: 970 226-9326 On Wed, May 25, 2016 at 4:43 PM, peterhouk1 . <peterh...@gmail.com> wrote: > 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>* > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology