SingleCovComp: Test of hypothesis over a single covariance matrix

In andresfpc/AMUN: Analisis Multivariado Universidad Nacional de Colombia (AMUN)

Description

The following function provides a useful computation to contrast a single covariance matrix against to an already set covariance matrix as in Simar(2007).

Usage

SingleCovComp(Sigma_0, Sigma, n, alpha = 0.05)

Arguments

Sigma_0	a positive define matrix which contains the variances and covariances under the null hypothesis
Sigma	A positive define matrix which contains the variances and covariances that is being use to contrast
n	number of observatios
alpha	level of significance

Value

LStatistic	a value of the likelihood ratio estimator
df	total number of degrees of freedom for the likelihood ratio
chi2	the value of the Chi Square statistic for the likelihod radio
pValue	the p value of the Chi square statistic

Author(s)

Jesus Gonzalez <[e-mail]>, Andres Palacios <[e-mail]>, Campo Elias Pardo <[e-mail]>

References

Hardle, W. & Simar, L. (2007), Applied Multivariate Statistical Analysis, Springer, New York.

Examples

Sigma_0 <- matrix(c( 4, 3, 2,
                     3, 6, 5,
                     2, 5, 10), nrow = 3, byrow = TRUE)

Sigma <- matrix(c( 3.42, 2.60, 1.89,
                     2.60, 8.00, 6.51,
                     1.89, 6.51, 9.62), nrow = 3, byrow = TRUE)

n <- 20

SingleCovComp(Sigma_0 = Sigma_0, Sigma = Sigma, n = n)

andresfpc/AMUN documentation built on May 12, 2019, 3:36 a.m.