weib.gpo {mable}R Documentation

Generalized PO model with Weibull baseline

Description

Maximum likelihood estimation in generalized proportional odds rate regression model with Weibull baseline based on interal censored event time data

Usage

weib.gpo(
  formula,
  data,
  g,
  scale,
  shape,
  eta = 1,
  eta.known = TRUE,
  controls = mable.ctrl(),
  progress = TRUE
)

Arguments

formula

regression formula. Response must be cbind. See 'Details'.

data

a dataset

g

initial d-vector of regression coefficients

scale

initial guess of the scale parameter for Weibull baseline

shape

initial guess of the shape parameter for Weibull baseline

eta

the given positive value of \eta. See 'Details'.

eta.known

logical. If TRUE eta is the known values of \eta, else eta is an initial guess of \eta. See 'Details'.

controls

Object of class mable.ctrl() specifying iteration limit and other control options. Default is mable.ctrl.

progress

if TRUE a text progressbar is displayed

Details

???

Value

a class 'mable_reg' object, a list with components

Examples


## Simulated Weibull data
require(icenReg)
set.seed(111)
simdata<-simIC_weib(100, model = "po", inspections = 2, 
   inspectLength = 2.5, prob_cen=1)
sp<-ic_sp(cbind(l, u) ~ x1 + x2, data = simdata, model="po") 
gt<--sp$coefficients
res0<-maple.po(cbind(l, u) ~ x1 + x2, data = simdata, M=c(1,20), g=gt, tau=6)
op<-par(mfrow=c(1,2))
plot(res0,  which=c("likelihood","change-point"))
par(op)
res1<-mable.po(cbind(l, u) ~ x1 + x2, data = simdata, M=c(1,20), g=gt, 
   tau=6, x0=data.frame(x1=max(simdata$x1),x2=-1))
res2<-weib.gpo(cbind(l, u) ~ x1 + x2, data = simdata, g=gt, scale=2, shape=2)  
op<-par(mfrow=c(2,2))    
plot(res1,  which=c("likelihood","change-point")) 
plot(res0, y=data.frame(x1=0,x2=0), which="density", add=FALSE, type="l",
    xlab="Time", main="Desnity Function")
plot(res1, y=data.frame(x1=0,x2=0), which="density", add=TRUE, lty=2, col=4)
lines(xx<-seq(0, 7, len=512), dweibull(xx, 2,2), lty=3, col=2, lwd=1.5)
lines(xx, dweibull(xx, res2$shape, res2$scale), lty=5, col=5, lwd=1.5)
legend("topright", bty="n", lty=1:3, col=c(1,4,2), c(expression(hat(f)[0]),
    expression(tilde(f)[0]), expression(f[0])))
plot(res0, y=data.frame(x1=0,x2=0), which="survival", add=FALSE, type="l",
    xlab="Time", main="Survival Function")
plot(res1, y=data.frame(x1=0,x2=0), which="survival", add=TRUE, lty=2, col=4)
lines(xx, 1-pweibull(xx, 2, 2), lty=2, col=2)
lines(xx, 1-pweibull(xx, res2$shape, res2$scale), lty=5, col=5, lwd=1.5)
legend("topright", bty="n", lty=1:3, col=c(1,4,2), c(expression(hat(S)[0]),
    expression(tilde(S)[0]), expression(S[0])))
par(op)


[Package mable version 4.1.1 Index]