simu.Weibull {double.truncation} | R Documentation |
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)
Arguments
n |
sample size |
mu |
location parameter |
sigma |
scale parameter |
delta |
a positive parameter controlling the inclusion probability |
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).
Value
u |
lower truncation limits |
y |
log-transformed lifetimes |
v |
upper truncation limits |
Author(s)
Takeshi Emura
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)
[Package double.truncation version 1.7 Index]