rockchalk (version 1.1)

mcGraph3: mcGraph3 draws a 3-dimensional scatter and a regression plane

Description

The observations are scattered in 3-dimensions, the fitted values are represented by a mesh, and their shadows in the x1-x2 plane are also represented.

Usage

mcGraph3(x1, x2, y, interaction = FALSE, ...)

Arguments

x1

a predictor vector

x2

a predictor vector

y

the dependent variable

interaction

a TRUE or FALSE request for inclusion of the x1-x2 interaction in the regression calculation

...

optional arguments passed to persp

Value

• a list of 2 objects, the fitted regression model and the perspective matrix used with persp to draw the image.

Examples

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)