test.HV {mnt} | R Documentation |
The Henze-Visagie test of multivariate normality
Description
Computes the multivariate normality test of Henze and Visagie (2019).
Usage
test.HV(data, a = 5, MC.rep = 10000, alpha = 0.05)
Arguments
data |
a n x d matrix of d dimensional data vectors. |
a |
positive numeric number (tuning parameter). |
MC.rep |
number of repetitions for the Monte Carlo simulation of the critical value. |
alpha |
level of significance of the test. |
Details
This functions evaluates the teststatistic with the given data and the specified tuning parameter a
.
Each row of the data Matrix contains one of the n (multivariate) sample with dimension d. To ensure that the computation works properly
n \ge d+1
is needed. If that is not the case the test returns an error.
Note that a=Inf
returns the limiting test statistic with value 2*MSkew + MRSSkew
.
Value
a list containing the value of the test statistic, the approximated critical value and a test decision on the significance level alpha
:
$Test
name of the test.
$param
value tuning parameter.
$Test.value
the value of the test statistic.
$cv
the approximated critical value.
$Decision
the comparison of the critical value and the value of the test statistic.
References
Henze, N., Visagie, J. (2019) "Testing for normality in any dimension based on a partial differential equation involving the moment generating function", to appear in Ann. Inst. Stat. Math., DOI
See Also
Examples
test.HV(MASS::mvrnorm(50,c(0,1),diag(1,2)),a=5,MC.rep=500)
test.HV(MASS::mvrnorm(50,c(0,1),diag(1,2)),a=Inf,MC.rep=500)