MVcis: Calculate Hotelling's T2 Confidence Intervals In mvdalab: Multivariate Data Analysis Laboratory

Description

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

Usage

 `1` ```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.

References

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

`MVComp`

Examples

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

Example output

```             [,1]      [,2]
Social  502.99940 550.17302
Verbal   51.21484  58.16447
Science  23.63855  26.61432
```

mvdalab documentation built on Nov. 17, 2017, 6 a.m.