monodr {cequre}

Restoration of monotonicity respecting using adaptive interpolation of Huang (2017)

Description

Restoration of monotonicity respecting for dynamic regression models such as quantile regression model, Aalen's additive hazards model, and dynamic survival models of Peng and Huang (2007, Biometrika, pp 719–733).

Usage

monodr(origrc,zch,initau=0.5,taus=numeric(0))

Arguments

origrc

(piecewise-constant) original regression coefficient.

zch

collection of covariate values for which the monotonicity is enforced. In the presence of intercept in "origrc", unity element needs to be included. "zch" could be the original covariate matrix. However, using its convext hull, if readily available, may be computationally more efficient.

initau

starting tau value from which monotonicity-respecting is restored.

taus

(increasing) tau values at which quantile coefficient is of interest.

Value

airc

regression coefficient after adaptive interpolation: each column corresponds to an interpolating point (the intercept is followed by slope coefficients, and final element is tau, the probability index.)

bt

adaptively interpolated coefficient at taus, only available if taus is specified.

References

Huang, Y. (2017) Restoration of monotonicity respecting in dynamic regression. Journal of the American Statistical Association 112, 613–622.

Examples

## run the example in cequre first
num <- 200
beta <- c(.5, .5)
cvt.1 <- as.numeric(runif(num)<0.5)
cvt.2 <- runif(num)
resid <- rexp(num)
tres <- 1-exp(-resid)
event.t <- log(resid)+beta[1]*cvt.1*ifelse(tres<.4,tres/.4,1)+beta[2]*cvt.2
censr.t <- log(runif(num, 0, 5))
x <- pmin(event.t, censr.t)
dlt <- as.numeric(event.t<=censr.t)
fit <- cequre(x,dlt,cbind(cvt.1,cvt.2),taus=.1*seq(1,7,2),res=200)

## resotration of monotonicity respecting
zch <- matrix(c(1,1,1,1,0,0,1,1,0,1,0,1),ncol=3)
mfit <- monodr(fit$curve,zch,initau=fit$tau.bnd/2,taus=.1*seq(1,7,2))