rockchalk (version 1.4)

mcGraph1: Illustrate the effect of multicollinearity in regression.

Description

This is a set of functions that faciliates the examination of multicollinearity. Suppose the "true" relationship is y[i] = 0.2 * x1[i] + 0.2 * x2[i] + e where e is Normal(0, stde^2).

Usage

mcGraph1(x1, x2, y, x1lab, x2lab, ylab, ...)

Arguments

x1

a predictor vector

x2

a predictor vector

y

the dependent variable

x1lab

label for the x1 axis, (the one called "xlab" inside persp)

x2lab

label for the x2 axis, (the one called "ylab" inside persp)

ylab

label for the y (vertical) axis (the one called "zlab" inside persp)

...

additional parameters passed to persp

Examples

set.seed(12345)
## Create data with x1 and x2 correlated at 0.10
dat <- genCorrelatedData(rho=.1, stde=7)

mcGraph1(dat$x1, dat$x2, dat$y, theta=20, phi=8, ticktype="detailed", nticks=10)