Description Usage Arguments Value Author(s) References Examples
View source: R/FrankClayton.Weibull.data.R
The data generation process is based on the Frank copula C_theta for serial dependence and the Clayton copula tilde(C)_alpha for dependent censoring with the marginal distributions Weib(scale1, shape1) and Weib(scale2, shape2). Censoring percentage can be controlled by constant c. This function is used when doing parametric bootstrap. The guide for using this function shall be explained by Huang (2019), and Huang, Wang and Emura (2020).
1 | FrankClayton.Weibull.data(N, scale1, shape1, theta, scale2, shape2, alpha, b, l)
|
N |
sample size |
scale1 |
scale parameter for Weib(scale1, shape1), scale1 > 0 |
shape1 |
shape parameter for Weib(scale1, shape1), shape1 > 0 |
theta |
copula parameter for C_theta, theta \neq 0 |
scale2 |
scale parameter for Weib(scale2, shape2), scale2 > 0 |
shape2 |
shape parameter for Weib(scale2, shape2), shape2 > 0 |
alpha |
copula parameter for tilde(C)_alpha, alpha > 0 |
b |
parameter of Unif(0, b) for controlling censoring percentage |
l |
length for data generation (default = 300) |
A list with the following elements:
Subject |
a vector for numbers of subject |
T_ij |
a vector for event times |
delta_ij |
a vector for event indicator (=1 if recurrent; =0 if censoring) |
T_i_star |
a vector for death times |
delta_i_star |
a vector for death indicator (=1 if death; =0 if censoring) |
Xinwei Huang
Huang XW, Wang W, Emura T (2020) A copula-based Markov chain model for serially dependent event times with a dependent terminal event. Japanese Journal of Statistics & Data Science. Accepted.
1 2 | Y = FrankClayton.Weibull.data(N = 100, scale1 = 1, shape1 =0.5, theta = 2,
scale2 = 0.45, shape2 = 0.5, alpha = 2, b = 10, l = 300)
|
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