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The gofPHCS package implements a unified framework for
conducting goodness-of-fit (GOF) tests on lifetime data subject to
complete sampling, progressive Type-II censoring, and Type-I / Type-II
hybrid censoring schemes based on Cramer & Balakrishnan (2023).
For complete failure time data, standard EDF test statistics
including Kolmogorov-Smirnov (KS), Cramér-von Mises
(CvM), and Anderson-Darling (AD) are
supported.
set.seed(123)
x_complete <- rexp(25, rate = 0.5)
# Create censored data container
cd_comp <- cens_data(x = x_complete, scheme = "complete")
# Define target exponential distribution
dist_exp <- make_distribution(
cdf = function(x, rate) pexp(x, rate = rate),
params = c(rate = 0.5),
support = c(0, Inf)
)
# Perform KS test
test_ks <- gof_test(cd_comp, distribution = dist_exp, statistic = "KS", p.method = "montecarlo", nsim = 499)
print(test_ks)
#>
#> Monte Carlo Test (499 replicates) (KS for complete scheme)
#>
#> data: cd_comp
#> KS = 0.12946, n = 25, n_obs = 25, p-value = 0.746
#> alternative hypothesis: two.sided
#> sample estimates:
#> rate
#> 0.5
#>
#> Censoring Scheme: complete
#> Null Parameters: rate = 0.5Progressive Type-II censoring allows items to be removed at each failure time according to a pre-specified removal plan \(R = (R_1, \dots, R_m)\).
# Example data: insulating fluid breakdown times (n = 19, m = 18)
x_prog <- c(0.19, 0.78, 0.96, 1.31, 2.78, 3.16, 4.15, 4.67, 4.85, 6.50,
7.35, 8.01, 8.27, 12.06, 31.75, 32.52, 33.91, 36.71)
R_plan <- c(rep(0, 17), 1)
cd_prog <- cens_data(x = x_prog, scheme = "progtypeII", n = 19, R = R_plan)
# Test exponentiality using Spacings Test statistic T (Balakrishnan et al. 2002b)
test_T <- gof_test(cd_prog, distribution = dist_exp, statistic = "T", p.method = "asymptotic")
print(test_T)
#>
#> Asymptotic Normal Spacings Test (T for progtypeII scheme)
#>
#> data: cd_prog
#> T = 0.3855, n = 19, n_obs = 18, p-value = 0.102
#> alternative hypothesis: two.sided
#> sample estimates:
#> rate
#> 0.5
#>
#> Censoring Scheme: progtypeII
#> Null Parameters: rate = 0.5In Type-I hybrid censoring, the test terminates at \(T^* = \min(T_0, X_{r:n})\).
set.seed(456)
x_hyb1 <- c(0.25, 0.48, 0.81, 1.05, 1.32)
cd_hyb1 <- cens_data(x = x_hyb1, scheme = "hybridI", n = 10, r = 7, T0 = 1.5)
test_ksi <- gof_test(cd_hyb1, distribution = dist_exp, statistic = "KSI", p.method = "montecarlo", nsim = 499)
print(test_ksi)
#>
#> Monte Carlo Test (499 replicates) (KSI for hybridI scheme)
#>
#> data: cd_hyb1
#> KSI = 0.42066, n = 10, n_obs = 5, p-value = 0.902
#> alternative hypothesis: two.sided
#> sample estimates:
#> rate
#> 0.5
#>
#> Censoring Scheme: hybridI
#> Null Parameters: rate = 0.5In Type-II hybrid censoring, the test terminates at \(T^* = \max(T_0, X_{r:n})\).
set.seed(789)
x_hyb2 <- c(0.31, 0.55, 0.92, 1.15, 1.60, 2.10)
cd_hyb2 <- cens_data(x = x_hyb2, scheme = "hybridII", n = 10, r = 5, T0 = 1.0)
test_ksii <- gof_test(cd_hyb2, distribution = dist_exp, statistic = "KSII", p.method = "montecarlo", nsim = 499)
print(test_ksii)
#>
#> Monte Carlo Test (499 replicates) (KSII for hybridII scheme)
#>
#> data: cd_hyb2
#> KSII = 0.53353, n = 10, n_obs = 6, p-value = 0.774
#> alternative hypothesis: two.sided
#> sample estimates:
#> rate
#> 0.5
#>
#> Censoring Scheme: hybridII
#> Null Parameters: rate = 0.5These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.