ComparisonOneSampleCTest {FuzzyResampling} | R Documentation |
Comparison of the resampling approaches based on the power for the one-sample test for the mean.
Description
ComparisonOneSampleCTest
returns the percentage of rejections for the one-sample C-test when different resampling methods
are used.
Usage
ComparisonOneSampleCTest(
generator,
mu_0,
shift = 0,
sampleSize = 10,
numberOfSamples = 10,
initialSamples = 100,
theta = 1/3,
significance = 0.05,
...
)
Arguments
generator |
Name of the generator for sampling initial samples.
For the possible names check the values of |
mu_0 |
Triangular or trapezoidal fuzzy number which is used for the null hypothesis of the C-test. |
shift |
Deterministic shift added to all initial samples. |
sampleSize |
Size of the single initial sample. |
numberOfSamples |
Number of the bootstrapped samples used to estimate the p-value. |
initialSamples |
Number of the generated initial samples. More than one value can be given in the form of matrix. |
theta |
The weighting parameter for the mid/spread distance applied in the C-test. |
significance |
The significance value used to accept/reject the hypothesis for the one-sample C-test. |
... |
Parameters which are passed to |
Details
The function generates a sequence of initial samples (their number is given in initialSamples
,
the size is determined by sampleSize
) for fuzzy numbers of the type specified by generator
.
Then some deterministic shift of the size shift
is added to each fuzzy observation in these samples.
Next, function OneSampleCTest
is executed to calculate the p-value for each combination of the initial sample and
resampling method. Then, by comparing the p-value with the assumed significance level
significance
we make a decision whether to reject the null hypothesis for the one-sample C-test for the mean
(see Lubiano et al. (2016)) or not.
The output of this procedure is the percentage of rejections in the sequence of experiments.
Value
This function returns a vector of percentage of rejections for the one-sample C-test for the mean.
References
Lubiano, M.A., Montenegro M., Sinova, B., de Saa, S.R., Gil, M.A. (2016) Hypothesis testing for means in connection with fuzzy rating scale-based data: algorithms and applications European Journal of Operational Research, 251, pp. 918-929
See Also
ComparisonSEMean
for the comparison of resampling methods based on SE/MSE for the mean,
ComparePowerOneSampleCTest
for the comparison of resampling methods based on power for the one-sample C-test
for the mean.
Other comparison of resampling methods:
ComparePowerOneSampleCTest()
,
ComparisonSEMean()
Examples
## Not run:
# seed PRNG
set.seed(1234)
# compare the resampling methods for the synthetic data generated using GeneratorNU function
ComparisonOneSampleCTest(generator="GeneratorNU",mu_0 = c(-0.4,-0.1,0.1,0.4),
sampleSize = 10,numberOfSamples = 100, initialSamples = 100,mu = 0, sigma = 1,a = 0.2,b = 0.6)
## End(Not run)