quantreg package is used. *fit1 results are* Call: rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9, tau = 0.15, data = wbc)
Coefficients: (Intercept) inp1 inp2 inp3 inp4 inp5 -0.191528450 0.005276347 0.021414032 0.016034803 0.007510343 0.005276347 inp6 inp7 inp8 inp9 0.058708544 0.005224906 0.006804871 -0.003931540 Degrees of freedom: 673 total; 663 residual *fit2 results are* Call: rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9, tau = 0.3, data = wbc) Coefficients: (Intercept) inp1 inp2 inp3 inp4 -1.111111e-01 5.776765e-19 4.635734e-18 1.874715e-18 2.099872e-18 inp5 inp6 inp7 inp8 inp9 -4.942052e-19 1.111111e-01 2.205289e-18 4.138435e-18 9.300642e-19 Degrees of freedom: 673 total; 663 residual anova(fit1,fit2); Quantile Regression Analysis of Deviance Table Model: op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 + inp8 + inp9 Joint Test of Equality of Slopes: tau in { 0.15 0.3 } Df Resid Df F value Pr(>F) 1 9 1337 0.5256 0.8568 Warning messages: 1: In summary.rq(x, se = "nid", covariance = TRUE) : 93 non-positive fis 2: In summary.rq(x, se = "nid", covariance = TRUE) : 138 non-positive fis how to interpret the above results?? what is the use of anova function?? will it give the best among fit1 && fit2.. -- View this message in context: http://r.789695.n4.nabble.com/about-interpretation-of-anova-results-tp4159510p4159510.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.