test.CM {gofIG} | R Documentation |
This function computes the goodness-of-fit test for the inverse Gaussian family in the spirit of Cramer and von Mises. Note that this tests the composite hypothesis of fit to the family of inverse Gaussian distributions, i.e. a bootstrap procedure is implemented to perform the test.
test.CM(data, B = 500)
data |
a vector of positive numbers. |
B |
number of bootstrap iterations used to obtain p value. |
The Cramer-von Mises test is computed as described in Allison et. al. (2019). The p value is obtained by a parametric bootstrap procedure.
a list containing the value of the name of the test statistic, the value of the test statistic, the bootstrap p value, the values of the estimators, and the number of bootstrap iterations:
$Test
the name of the used test statistic.
$T.value
the value of the test statistic.
$p.value
the approximated p value.
$par.est
the estimated parameters.
$boot.run
number of bootstrap iterations.
Allison, J.S., Betsch, S., Ebner, B., Visagie, I.J.H. (2019) "New weighted L^2
-type tests for the inverse Gaussian distribution", arXiv:1910.14119. LINK
test.CM(rmutil::rinvgauss(20,2,1),B=100)