fit1<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)
fit2<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.5,data=wbc)
fit3<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)
fit4<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)
fit5<-rq(formula=op~inp1+inp2+inp3+inp4+inp5+inp6+inp7+inp8+inp9,tau=0.15,data=wbc)
*output of tau=0.15*fit1
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
*output of tau=0.3*fit2
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
*output of tau=0.5*fit3
Call:
rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 +
inp8 + inp9, tau = 0.5, data = wbc)
Coefficients:
(Intercept) inp1 inp2 inp3 inp4
-1.400000e-01 5.810236e-17 4.000000e-02 1.087160e-16 4.297771e-18
inp5 inp6 inp7 inp8 inp9
8.045868e-17 8.000000e-02 6.841101e-17 2.000000e-02 7.560947e-17
Degrees of freedom: 673 total; 663 residual
*output of tau=0.65*
Call:
rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 +
inp8 + inp9, tau = 0.65, data = wbc)
Coefficients:
(Intercept) inp1 inp2 inp3 inp4
inp5
-0.193593706 0.005012804 0.044208182 0.008994346 0.006214294
0.007622629
inp6 inp7 inp8 inp9
0.064595895 0.006214294 0.028904532 0.001775512
Degrees of freedom: 673 total; 663 residual
*output of tau=0.9:*
fit5
Call:
rq(formula = op ~ inp1 + inp2 + inp3 + inp4 + inp5 + inp6 + inp7 +
inp8 + inp9, tau = 0.9, data = wbc)
Coefficients:
(Intercept) inp1 inp2 inp3 inp4
inp5
-0.249006688 0.040430238 0.010854846 0.031021326 0.013558943
0.024867111
inp6 inp7 inp8 inp9
0.050441784 0.024867111 0.027018345 0.001079872
Degrees of freedom: 673 total; 663 residual
<
b> so fit1 fit2,fit3,fit4,fit5 are the 5 quantiles of the wbc dataset.but
why i am encoutering the following error while using anova*
*anova(fit1,fit2,fit3,fit4,fit5);
Error in solve.default(D %*% W %*% t(D), D %*% coef) :
system is computationally singular: reciprocal condition number =
5.58091e-19
In addition: 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
3: In summary.rq(x, se = "nid", covariance = TRUE) : 206 non-positive fis
4: In summary.rq(x, se = "nid", covariance = TRUE) : 53 non-positive fis
5: In summary.rq(x, se = "nid", covariance = TRUE) : 30 non-positive fis*
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