inst/examples/ex_qris.R

In qris: Quantile Regression Model for Residual Lifetime Using an Induced Smoothing Approach

## ######################################### 
## Simulated data
## #########################################
data.gen <- function(n) {
  r0 <- .2 * sqrt(log(2))
  r1 <- .1 * sqrt(log(2))
  dat <- data.frame(censoring = runif(n, 0, 24.35),
                    Time0 = sqrt(-log(1 - runif(n))),
                    X = rbinom(n, 1, .5))
  dat$Time0 <- ifelse(dat$X > 0, dat$Time0 / r1, dat$Time0 / r0)
  dat$Time <- pmin(dat$Time0, dat$censoring)
  dat$status <- 1 * (dat$Time0 < dat$censoring)
  subset(dat, select = c(Time, status, X))
}

set.seed(1)
dat <- data.gen(200)
fm <- Surv(Time, status) ~ X
fit1 <- qris(fm, data = dat, t0 = 1, Q = 0.5, nB = 100, "smooth", "pmb", c(1,1))
fit2 <- qris(fm, data = dat, t0 = 1, Q = 0.5, nB = 100, "nonsmooth", "fmb", "rq")
fit3 <- qris(fm, data = dat, t0 = 1, Q = 0.5, nB = 100, "iterative", "fmb", "rq",
             control = qris.control(maxit = 20, tol = 1e-3, trace = TRUE))

summary(fit1)
summary(fit2)
summary(fit3)

## #########################################
## Real data application
## #########################################
data(cancer, package = "survival")
lung2 <- subset(lung, select = c(time, status, age, sex))
## tidy up the data
lung2$status <- lung2$status - 1
lung2$sex <- lung2$sex - 1

fm <- Surv(time, status) ~ age + sex
fit1 <- qris(fm, data = lung2, t0 = 0, Q = 0.5, nB = 100, "iterative", "pmb", "rq")
fit2 <- qris(fm, data = lung2, t0 = 30, Q = 0.5, nB = 100, "nonsmooth", "fmb", c(1, 0, 1))
fit3 <- qris(fm, data = lung2, t0 = 100, Q = 0.5, nB = 100,"smooth", "pmb", "rq")

summary(fit1)
summary(fit2)
summary(fit3)

plot(fit2, Qs = 4:6 / 10)