mcGraph: Illustrate multicollinearity in regression, part 1.

In rockchalk: Regression Estimation and Presentation

Illustrate multicollinearity in regression, part 1.

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).

mcGraph1 draws the 3D regression space, but all of the points are illustrated "in" the flat plane of x1-x2 variables.

Usage

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

mcGraph2(x1, x2, y, rescaley = 1, drawArrows = TRUE, x1lab, x2lab, ylab, ...)

mcGraph3(
  x1,
  x2,
  y,
  interaction = FALSE,
  drawArrows = TRUE,
  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
rescaley	a single scalar value or a vector of the same length as y.
drawArrows	TRUE or FALSE, do you want arrows from the plane to observed y?
interaction	a TRUE or FALSE request for inclusion of the x1-x2 interaction in the regression calculation

Details

These functions are specialized to a particular purpose. If you just want to draw 3D regressions, look at plotPlane.

Value

mcGraph1 and mcGraph2 return only the perspective matrix from persp. It is returned so that users can add additional embellishments on the 3D plot (can be used with trans3d)

mcGraph3 returns a list of 2 objects. 1) the fitted regression model2) the perspective matrix used with persp to draw the image.

Author(s)

Paul E. Johnson [e-mail]

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)

set.seed(12345)
## Create data with x1 and x2 correlated at 0.10
dat <- genCorrelatedData(rho=.1, stde=7)
## This will "grow" the "cloud" of points up from the
## x1-x2 axis
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.0, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.1, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.2, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.3, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.4, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.5, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.6, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.7, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.8, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 0.9, theta = 0)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 1, theta = 0)

##rotate this
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 1, theta = 20)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 1, theta = 40)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 1, theta = 60)
mcGraph2(dat$x1, dat$x2, dat$y, rescaley = 1, theta = 80)

## once they reach the top, make them glitter a while
for(i in 1:20){
  mcGraph2(dat$x1, dat$x2, dat$y, rescaley = runif(length(dat$x1), .9,1.1), theta = 0)
}

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

mcGraph3(dat$x1, dat$x2, dat$y, theta = 0)

dat2 <- genCorrelatedData(rho = 0, stde = 7)

mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = 0, phi = 10)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = 30, phi = 10)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30, phi = 10)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30, phi = -10)
mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30, phi = -15)

## Run regressions with not-strongly correlated data
modset1 <- list()
for(i in 1:20){
  dat2 <- genCorrelatedData(rho = .1, stde = 7)
  summary(lm( y ~ x1 + x2 , data = dat2))
  modset1[[i]] <- mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30)
}


## Run regressions with strongly correlated data
modset2 <- list()
for(i in 1:20){
  dat2 <- genCorrelatedData(rho = .981, stde = 7)
  summary(lm( y ~ x1 + x2 , data = dat2))
  modset2[[i]] <- mcGraph3(dat2$x1, dat2$x2, dat2$y, theta = -30)
}

dat3 <- genCorrelatedData(rho = .981, stde = 100, beta=c(0.1, 0.2, 0.3, -0.1))
mcGraph3(dat3$x1, dat3$x2, dat3$y, theta=-10, interaction = TRUE)

rockchalk documentation built on Aug. 6, 2022, 5:05 p.m.