MVcis: Calculate Hotelling's T2 Confidence Intervals

In mvdalab: Multivariate Data Analysis Laboratory

Calculate Hotelling's T2 Confidence Intervals

Description

Calculate joint confidence intervals (Hotelling's T2 Intervals).

Usage

MVcis(data, segments = 51, level = .95, Vars2Plot = c(1, 2), include.zero = F)

Arguments

data	a multivariable dataset to compare to means
segments	number of line-segments used to draw ellipse.
level	draw elliptical contours at these (normal) probability or confidence levels.
Vars2Plot	variables to plot
include.zero	add the zero axis to the graph output

Details

This function calculates the Hotelling's T2 Intervals for a mean vector.

Assumption:

Population is a random sample from a multivariate population.

If the confidence ellipse does not cover c(0, 0), we reject the NULL that the joint confidence region is equal to zero (at the stated alpha level).

Value

This function returns the Hotelling's T2 confidence intervals for the p-variates and its corresponding confidence ellipse at the stated confidence level.

Author(s)

Nelson Lee Afanador ([e-mail])

References

Johnson, R.A., Wichern, D.W. (2002) Applied Multivariate Statistical Analysis. Prentice Hall.

See Also

MVComp

Examples

data(College)
MVcis(College, Vars2Plot = c(1, 2), include.zero = TRUE)

mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.