Description Usage Arguments Value References See Also Examples
A simple and powerful test for multivariate normality with a combination of multivariate kurtosis (MK) and Shapiro-Wilk which was proposed by Zhou and Shao (2014). The p-value of the test statistic (T_n) is computed based on a simulated null distribution of T_n. Details see Zhou and Shao (2014).
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X |
an n*p data matrix or data frame, where n is number of rows (observations) and p is number of columns (variables) and n>p. |
B |
number of Monte Carlo simulations for null distribution, default is 1000 (increase B to increase the precision of p-value). |
pct |
percentiles of MK to get c_1 and c_2 described in the reference paper, default is (0.01, 0.99). |
Returns a list with two objects:
mv.test
results of the Zhou-Shao's test for multivariate normality , i.e., test statistic T_n, p-value (under H0, i.e. multivariate normal, that T_n is at least as extreme as the observed value), and multivariate normality summary (YES, if p-value>0.05).
uv.shapiro
a dataframe with p rows detailing univariate Shapiro-Wilk tests. Columns in the dataframe contain test statistics W, p-value,and univariate normality summary (YES, if p-value>0.05).
Zhou, M., & Shao, Y. (2014). A powerful test for multivariate normality. Journal of applied statistics, 41(2), 351-363.
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611.
power.mvnTest
, msk
, mardia
, msw
, faTest
, mhz
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