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.
