library(quantreg) data(airquality) head(airquality) airq <- airquality[143:150,] f <- rq(Ozone ~ ., data=airquality) predict(f,newdata=airq) f <- rq(Ozone ~ ., data=airquality,tau=1:3/4) predict(f,newdata=airq)
[edit]
1 1 #
x <- c(32,64,96,118,126,144,152.5,158) y <- c(99.5,104.8,108.5,100,86,64,35.3,15) df <- data.frame(x=x, y=y) plot(x,y,pch=19) library(splines) #bs( function)) X <- model.matrix(y ~ bs(x, df=4)) fit <- rq(y~bs(x, df=4),data=df, tau=0.5) y.fit <- X %*% fit$coef lines(x, y.fit)
[edit]
2 2 #
# install.packages("quantreg")
library("quantreg")
data(engel)
head(engel)
> head(engel)
income foodexp
1 420.1577 255.8394
2 541.4117 310.9587
3 901.1575 485.6800
4 639.0802 402.9974
5 750.8756 495.5608
6 945.7989 633.7978
par(mfrow=c(2,1)) hist(engel$income) hist(engel$foodexp) par(mfrow=c(1,1))
f<-rq((engel$foodexp)~(engel$income),tau=c(0.05,0.1,0.25,0.5,0.75,0.9,0.95)) f
fit <- rq(foodexp ~ income, data=engel, tau = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)) fit
蟆郁骸
> f
Call:
rq(formula = (engel$foodexp) ~ (engel$income), tau = c(0.05,
0.1, 0.25, 0.5, 0.75, 0.9, 0.95))
Coefficients:
tau= 0.05 tau= 0.10 tau= 0.25 tau= 0.50 tau= 0.75 tau= 0.90 tau= 0.95
(Intercept) 124.8800408 110.1415742 95.4835396 81.4822474 62.3965855 67.3508721 64.1039632
engel$income 0.3433611 0.4017658 0.4741032 0.5601806 0.6440141 0.6862995 0.7090685
Degrees of freedom: 235 total; 233 residual
predict 谿瑚
data(airquality)
airq <- airquality[143:145,]
f <- rq(Ozone ~ ., data=airquality)
predict(f,newdata=airq)
f <- rq(Ozone ~ ., data=airquality, tau=1:19/20)
fp <- predict(f, newdata=airq, stepfun = TRUE)
fpr <- rearrange(fp)
plot(fp[[2]],main = "Ozone Prediction")
par(col="red")
lines(fpr[[2]])
legend(.2,20,c("raw","cooked"),lty = c(1,1),col=c("black","red"))
鏤