weib.gpo: Generalized PO model with Weibull baseline

In mable: Maximum Approximate Bernstein/Beta Likelihood Estimation

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

• convergence an integer code, 0 indicates successful completion(the iteration is convergent), 1 indicates that the maximum iteration had been reached in the calculation;

• delta the final convergence criterion for Newton iteration;

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)

mable documentation built on Oct. 1, 2024, 9:06 a.m.