double.truncation (version 1.7)

simu.Weibull: Simulating doubly-truncated data from the Weibull model

Description

A data frame is generated by simulated data from the Weibull model.

Usage

simu.Weibull(n,mu,sigma,delta)

Value

u

lower truncation limits

y

log-transformed lifetimes

v

upper truncation limits

Arguments

n

sample size

mu

location parameter

sigma

scale parameter

delta

a positive parameter controlling the inclusion probability

Author

Takeshi Emura

Details

The data are generated from the random vector (U,Y,V) subject to the inclusion criterion U<=Y<=V. The random vector are defined as U=mu-delta+sigma*W, Y=mu+sigma*W, and U=mu+delta+sigma*W, where P(W>w)=exp(-exp(w)). See Section 5.1 of Dorre et al. (2020-) for details. The inclusion probability is P(U<=Y<=V).

References

Dorre A, Huang CY, Tseng YK, Emura T (2020-) Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model, Computation Stat, DOI:10.1007/s00180-020-01027-6

Examples

## A simulation from Dorre et al.(2020) ##
simu.Weibull(n=100,mu=5,sigma=2,delta=2.08)

Dat=simu.Weibull(n=100,mu=5,sigma=2,delta=2.08)
PMLE.Weibull(Dat$u,Dat$y,Dat$v)