| gamma_test {goft} | R Documentation |
Test for the Gamma distribution
Description
Test of fit for the Gamma distribution with unknown shape and scale parameters based on the ratio of two variance estimators (Villasenor and Gonzalez-Estrada, 2015).
Usage
gamma_test(x)
Arguments
x |
a numeric data vector containing a random sample of positive real numbers. |
Details
The test statistic is the ratio of two variance estimators, namely, the sample variance and the moments estimator obtained by Villasenor and Gonzalez-Estrada (2015), which is the product of the sample mean of X and the sample covariance of X and log(X).
The asymptotic null distribution of the test statistic is used to approximate p-values.
NOTE: the unbiased sample covariance estimator is used to compute the test statistic.
Value
A list with class "htest" containing the following components.
statistic |
the calculated value of the test statistic. |
p.value |
the approximated p-value of the test. |
method |
the character string "Test of fit for the Gamma distribution". |
data.name |
a character string giving the name of the data set. |
Author(s)
Elizabeth Gonzalez-Estrada egonzalez@colpos.mx, Jose A. Villasenor-Alva
References
Villasenor, J.A. and Gonzalez-Estrada, E. (2015). A variance ratio test of fit for Gamma distributions. Statistics and Probability Letters, 96 1, 281-286. http://dx.doi.org/10.1016/j.spl.2014.10.001
See Also
gamma_fit for fitting a Gamma distribution to data.
Examples
# Testing the gamma distribution hypothesis on the logarithm of variable Loss
# of the danishuni data set
library(fitdistrplus)
data(danishuni)
logLoss <- log(danishuni$Loss) # logarithm of Loss variable
logLoss <- logLoss[logLoss > 0] # observations > 0
gamma_test(logLoss)