Description Usage Arguments Value References Examples
Generate univariate or multivariate random sample for distribution with independent marginals such that D_1 \otimes D_2. D_1 \otimes D_2 denotes the distribution having independent marginal distributions D_1 and D_2. This function can generate multivariate random samples only from distribution D_1 or from both D_1 and D_2.
1 |
n |
number of rows (observations). |
p |
total number of columns (variables). |
q |
number of columns from distribution |
D1 |
random generation function for 1st distribution (e.g., |
D2 |
random generation function for 2nd distribution (e.g., |
D1.args |
a list of optional arguments passed to |
D2.args |
a list of optional arguments passed to |
Returns univariate (p=1) or multivariate (p>1) random sample matrix.
Zhou, M., & Shao, Y. (2014). A powerful test for multivariate normality. Journal of applied statistics, 41(2), 351-363.
Henze, N., & Zirkler, B. (1990). A class of invariant consistent tests for multivariate normality. Communications in statistics-Theory and Methods, 19(10), 3595-3617.
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## Generate 5X2 random sample matrix from IMMV(N(0,1),Beta(1,2)) ##
IMMV(n=5, p=2, q=1, D1=rbeta, D1.args=list(shape1=1,shape2=2), D2=rnorm)
## Power calculation against bivariate (p=2) IMMV(Gamma(5,1)) distribution ##
## at sample size n=50 at one-sided alpha = 0.05 ##
# Zhou-Shao's test #
power.mvnTest(a=0.05, n=50, p=2, B=100, FUN=IMMV, D1=rgamma, D1.args=list(shape=5, rate=1))
## Power calculation against bivariate (p=2) IMMV(N(0,1),Beta(1,2)) distribution ##
## at sample size n=50 at one-sided alpha = 0.05 ##
# Zhou-Shao's test #
power.mvnTest(a=0.05, n=50, p=2, B=100, FUN=IMMV, q=1, D1=rbeta, D1.args=list(shape1=1,shape2=2),
D2=rnorm)
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