gks.srm.test.mc2: Generalized KS test for software reliability models with...

In SwReliab/gof4srm: Goodness-of-fit tests for NHPP-based software reliability models

Description

Perform the generalized KS test for the estimated software reliability models with Monte-Carlo testing

Usage

gks.srm.test.mc2(
  obj,
  alternative = c("two.sided", "less", "greater"),
  b = NA,
  alpha = 0.01,
  cv = 0.1,
  lhc = FALSE,
  seed = NA
)

Arguments

obj	An object of an estimated result by Rsrat
alternative	A string indicates the alternative hypothesis and must be one of "two.sided" (default), "less", or "greater".
b	An integer for the number of resamples. If it is NULL, the number of resamples is determined from the significant level and the coefficient variation.
alpha	A value of the significant level which is used to determine the number of resamples. The default is 0.01. If b is not NULL, this is ignored.
cv	A value of the coefficient variation which is used to determine the number of resamples. The default is 0.1. If b is not NULL, this is ignored.
lhc	A logical indicates resamples are drawn by Latin hypercude.
seed	An integer for the seed of random numbers. If it is NULL, the seed is not set.

Value

A list with components;

statistic	A value of the test statistic.
p.value	A value of the p-value of the test.
alternative	A string of the alternative hypothesis.
b	An integer for the number of resamples.
alpha	A value of the significant level.
cv	A value of the coefficient variation.
lhc	A logical indicates resamples are drawn by Latin hypercude.
seed	An integer for the seed of random numbers.

Examples

data(dacs)
result <- fit.srm.nhpp(fault=sys1g[1:30], srm.name=c("exp"))
gks.srm.test.mc2(result)

SwReliab/gof4srm documentation built on Dec. 18, 2021, 3:05 p.m.