Description Usage Arguments Details Value Author(s) Examples
View source: R/FrankClayton.Weibull.MLE.R
Perform two-stage estimation 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). The jackknife method estimates the asymptotic covariance matrix. Parametric bootstrap is applied while doing Kolmogorov-Smirnov tests and Cramer-von Mises test. The guide for using this function shall be explained by Huang (2019), and Huang, Wang and Emura (2020).
1 2 | FrankClayton.Weibull.MLE(subject, t.event, event, t.death, death, stageI, Weibull.plot,
jackknife, plot, GOF, GOF.plot, rep.GOF, digit)
|
subject |
a vector for numbers of subject |
t.event |
a vector for event times |
event |
a vector for event indicator (=1 if recurrent; =0 if censoring) |
t.death |
a vector for death times |
death |
a vector for death indicator (=1 if death; =0 if censoring) |
stageI |
an option to select MLE or LSE method for the 1st-stage optimization |
Weibull.plot |
if TRUE, show the Weibull probability plot |
jackknife |
if TRUE, the jackknife method is used for estimate covariance matrix (default = TRUE) |
plot |
if TRUE, the plots for marginal distributions are shown (default = FALSE) |
GOF |
if TRUE, show the p-values for KS-test and CvM-test |
GOF.plot |
if TRUE, show the model diagnostic plot |
rep.GOF |
repetition number of parametric bootstrap |
digit |
accurate to some decimal places |
When jackknife=FALSE, the corresponding standard error and confidence interval values are shown as NA.
A list with the following elements:
Sample_size |
Sample size N |
Case |
Count for event occurences |
scale1 |
Scale parameter for Weib(scale1, shape1) |
shape1 |
Shape parameter for Weib(scale1, shape1) |
scale2 |
Scale parameter for Weib(scale2, shape2) |
shape2 |
Shape parameter for Weib(scale2, shape2) |
theta |
Copula parameter for the Frank copula C_theta |
alpha |
Copula parameter for the Clayton copula tilde(C)_alpha |
COV |
Asymptotic covariance estimated by the jackknife method |
KS |
Kolmogorov-Smirnov test statistics |
p.KS |
P-values for Kolmogorov-Smirnov tests |
CM |
Cramer-von Mises test statistics |
p.CM |
P-values for Cramer-von Mises tests |
Convergence |
Convergence results for each stage |
Jackknife_error |
Count for error in jackknife repititions |
Log_likelihood |
Log-likelihood values |
Xinwei Huang
1 2 3 4 5 6 7 8 | data = FrankClayton.Weibull.data(N = 30, scale1 = 1, shape1 =0.5, theta = 2,
scale2 = 0.45, shape2 = 0.5, alpha = 2, b = 10, l = 300)
FrankClayton.Weibull.MLE(subject = data$Subject,
t.event = data$T_ij, event = data$delta_ij,
t.death = data$T_i_star, death = data$delta_i_star,
jackknife= TRUE, plot = TRUE)
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