MVcis {mvdalab} | R Documentation |
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 (nelson.afanador@mvdalab.com)
References
Johnson, R.A., Wichern, D.W. (2002) Applied Multivariate Statistical Analysis. Prentice Hall.
See Also
Examples
data(College)
MVcis(College, Vars2Plot = c(1, 2), include.zero = TRUE)