meanTest.single {MNormTest}R Documentation

Single Mean Vector Hypothesis Testing

Description

Test whether the mean vector of a single multivariate normal population is equal to a certain value when the population covariance matrix is known or unknown. The null hypothesis is that "H0: mu = mu0".

Usage

meanTest.single(data, mu0, Sigma0 = FALSE, alpha = 0.05, verbose = TRUE)

Arguments

data

The data matrix which is a matrix or data frame.

mu0

The mean vector when the null hypothesis is true.

Sigma0

The population covariance matrix. Default is FALSE which means the covariance matrix is unknown.

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.

Df

The degree of freedom.

Author(s)

Xifeng Zhang

References

Huixuan, Gao. Applied Multivariate Statistical Analysis. Peking University Press, 2005: pp.66-68.

Examples

data(iris)
X <- iris[, 1:4]
mu0 <- c(5.8, 3.0, 4.3, 1.3)
# carry out the test
test1 <- meanTest.single(X, mu0)
test2 <- meanTest.single(X, mu0, Sigma0 = diag(1, 4))
test3 <- meanTest.single(X, mu0, verbose = FALSE)
# get the elements
test1$Stat
test1$SampMean
test1$SampA
test1$Df

[Package MNormTest version 1.1.0 Index]