indTest.multi {MNormTest} | R Documentation |
Multivariate Normal Independence Test
Description
Test whether a set of multivariate normal random vectors are independent. The null hypothesis is "H0: The random vectors are independent of each other".
Usage
indTest.multi(data, subdim = FALSE, alpha = 0.05, verbose = TRUE)
Arguments
data |
The data matrix which is a matrix or data frame. Each column represents a random variable. |
subdim |
The dimensions of submatrices. The default is FALSE, which means the independence of all components of the random vector will be tested. |
alpha |
The significance level. Default is 0.05. |
verbose |
A boolean value. Default is TRUE. If TRUE, the null hypothesis will be displayed. If FALSE, the test will be carried out silently. |
Value
An object of class "testResult", which is a list with the following elements:
Conclusion |
The conclusion of the test. |
Stat |
A data frame containing the statistics, p value and critical value. |
SampMean |
The sample mean. |
SampA |
The sample deviation. |
SampAii |
The sample deviation of submatrices. |
Df |
The degree of freedom. |
b |
The Modified factor of the statistic. |
Author(s)
Xifeng Zhang
References
Huixuan, Gao. Applied Multivariate Statistical Analysis. Peking University Press, 2005: pp.92-94.
Examples
data(iris)
chart <- iris[, 1:4]
# carry out the test
test1 <- indTest.multi(chart)
test2 <- indTest.multi(chart, subdim = c(2, 1, 1))
test3 <- indTest.multi(chart, verbose = FALSE)
# get the elements
test1$Stat
test1$SampMean
test2$SampAii