mable (version 4.1.1)

weib.gpo: 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
)

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;

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

???

Examples

# \donttest{
## 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)
# }