mvdalab (version 1.4)

MVcis: 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

Value

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

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).

References

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

See Also

MVComp

Examples

# NOT RUN {
data(College)
MVcis(College, Vars2Plot = c(1, 2), include.zero = TRUE)
# }