asbio (version 0.2-1)

bv.boxplot: Bivariate boxplots

Description

Creates diagnostic bivariate boxplots using the method of Goldberg and Iglewicz (1992). The boxplots can be used to check assumptions of bivariate normality. The default robust=TRUE option relies on on a biweight estimator function written by Everitt (2004).

Usage

bv.boxplot(X, Y, robust = TRUE, D = 7, xlab = "X", ylab = "Y")

Arguments

X

First of two quantitative variables making up the bivariate distribution.

Y

Second of two quantitative variables making up the bivariate distribution.

robust

Logical. Robust estimators, i.e. robust = TRUE are recommended.

D

The default D = 7 lets the fence be equal to a 99 percent confidence interval for an individual observation.

xlab

Caption for X axis.

ylab

Caption for Y axis.

Value

• A diagnostic plot is returned

Details

Two ellipses are drawn. The inner is the "hinge" which contains 50 percent of the data. The outer is the "fence". Observations outside of the "fence" constitute possible troublesome outliers. The function bivariate from Everitt (2004) is used calculate the biweight M-estimators of location, scale and correlation if robust = TRUE (the default).

References

Everitt, B. 2005. An R and S-plus companion to multivariate analysis. Springer. Goldberg, K. M., and B. Ingelwicz. 1992. Bivariate extensions of the boxplot. Technometrics 34: 307-320.

See Also

boxplot

Examples

Y1<-rnorm(100,17,3)
Y2<-rnorm(100,13,2)
bv.boxplot(Y1,Y2)