| EpistemicTest {FuzzySimRes} | R Documentation |
Apply epistemic test for one or two fuzzy samples.
Description
'EpistemicTest' calculates the p-value for the given real-valued statistical test using one of the epistemic bootstrap approaches.
Usage
EpistemicTest(sample1, sample2, algorithm = "avs", ...)
Arguments
sample1 |
Sample of fuzzy numbers given in the form of a list or as a single number. |
sample2 |
Sample of fuzzy numbers given in the form of a list or as a single number (two-sample test case) or
|
algorithm |
Type of the epistemic bootstrap
algorithm used to calculate the output p-value (possible values: |
... |
Additional arguments passed to the epistemic test. |
Details
This is a general procedure that can be used to invoke other epistemic bootstrap tests: AverageStatisticEpistemicTest
(if the algorithm is set to "avs"), MultiStatisticEpistemicTest (if algorithm="ms"), and
ResamplingStatisticEpistemicTest (for algorithm="res").
For additional details about these procedures and their parameters, see the respective functions.
Value
The output is given in the form of a real number (the p-value) for the selected statistical test.
References
Grzegorzewski, P., Romaniuk, M. (2022) Bootstrap Methods for Epistemic Fuzzy Data. International Journal of Applied Mathematics and Computer Science, 32(2)
Grzegorzewski, P., Romaniuk, M. (2022) Bootstrapped Kolmogorov-Smirnov Test for Epistemic Fuzzy Data. Communications in Computer and Information Science, CCIS 1602, pp. 494-507, Springer
Gagolewski, M., Caha, J. (2021) FuzzyNumbers Package: Tools to deal with fuzzy numbers in R. R package version 0.4-7, https://cran.r-project.org/web/packages=FuzzyNumbers
See Also
MultiStatisticEpistemicTest for the epistemic bootstrap test related to multi-statistic approach,
ResamplingStatisticEpistemicTest for the epistemic bootstrap test related to resampling statistics,
AverageStatisticEpistemicTest for the epistemic bootstrap test related to averaging statistics,
Other epistemic bootstrap statistical test:
AverageStatisticEpistemicTest(),
MultiStatisticEpistemicTest(),
ResamplingStatisticEpistemicTest()
Examples
# seed PRNG
set.seed(1234)
# generate two independent initial fuzzy samples
list1<-SimulateSample(20,originalPD="rnorm",parOriginalPD=list(mean=0,sd=1),
incrCorePD="rexp", parIncrCorePD=list(rate=2),
suppLeftPD="runif",parSuppLeftPD=list(min=0,max=0.6),
suppRightPD="runif", parSuppRightPD=list(min=0,max=0.6),
type="trapezoidal")
list2<-SimulateSample(20,originalPD="rnorm",parOriginalPD=list(mean=0,sd=1),
incrCorePD="rexp", parIncrCorePD=list(rate=2),
suppLeftPD="runif",parSuppLeftPD=list(min=0,max=0.6),
suppRightPD="runif", parSuppRightPD=list(min=0,max=0.6),
type="trapezoidal")
# apply the Kolmogorov-Smirnov two sample test for two different samples
# with the average statistics approach
EpistemicTest(list1$value,list2$value,cutsNumber = 30)
# apply the Kolmogorov-Smirnov two sample test for two different samples
# with the multi-statistic and antithetic approach
EpistemicTest(list1$value,list2$value,algorithm = "ms",bootstrapMethod = "anti")