search for: inp3

...legend/key together with the 2nd y-axis under general xyplot procedures. See example below. The combination of the par.settings with simpleTheme and other settings seems to go wrong. I couldn't find a solution from previous topics. Any suggestions? thanks in advance! Robbert #data example inp3<-data.frame(group=rep(LETTERS[1:4],4), GF=as.vector(sapply(1:4,function(x)rep(x,4))), val=c(rep(c(2,3,7,8),2),rep(c(2,3,7,8),2)*1.1)) require(lattice) #this works fine, without second y-axis: xyplot(val~GF,groups=group, data=inp3,type="o", ylab="Relatedness", xlab=&quo...

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,...

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...

...and intercept is -0.009 . sir,how to interpret the above beta coefficients and what do they mean exactly??. t=0.5 means are we considering first 50% of the total data? t=0.6 means are we considering the first 60% of the total data? can we write a equation like y=intercept+b1*inp11+b2*inp29+b3*inp3+b4*inp4+b5*inp5+b6*inp6+b7*inp7+b8*inp8+b9*inp9 as in Linear Regression to calculate the predicted output of y or not?? If we are taking into consideration 5 quantiles of data ,Does it mean that we are dividing data it into 5 parts??which variables i have to consider if the data is to be divid...