| Clayton.Markov.GOF.binom {Copula.Markov} | R Documentation |
A goodness-of-fit test for the marginal binomial distribution.
Description
Perform a parametric bootstrap test based on the Cramer-von Mises and Kolmogorov-Smirnov statistics as proposed by Huang and Emura (2019) and Huang et al. (2019-).
Usage
Clayton.Markov.GOF.binom(Y, k = 3, size, B = 200,GOF.plot=FALSE, method = "Newton")
Arguments
Y |
vector of datasets |
k |
constant determining the length between LCL and UCL (k=3 corresponds to 3-sigma limit) |
size |
number of binomial trials |
B |
the number of Bootstrap replications |
GOF.plot |
if TRUE, show the model diagnostic plots for B bootstrap replications |
method |
Newton-Raphson method or nlm can be chosen |
Value
CM |
The Cramer-von Mises statistic and its P-value |
KS |
The Kolmogorov-Smirnov statistic and its P-value |
CM.boot |
Bootstrap values of the Cramer-von Mises statistics |
KS.boot |
Bootstrap values of the Kolmogorov-Smirnov statistics |
Author(s)
Huang XW, Emura T
References
Huang XW, Emura T (2021), Model diagnostic procedures for copula-based Markov chain models for statistical process control, Communications in Statistics - Simulation and Computation, doi: 50(8):2345-67
Huang XW, Emura T (2021-), Computational methods for a copula-based Markov chain model with a binomial time series, in review
Examples
size=50
prob=0.5
alpha=2
set.seed(1)
Y=Clayton.Markov.DATA.binom(n=500,size,prob,alpha)
Clayton.Markov.GOF.binom(Y,size=size,B=5,k=3,GOF.plot=TRUE) ## B=5 to save time