regr2.plot

3D plot of z against x and y, with regression plane fit and display of squared residuals.

3D plot of z against x and y, with regression plane fit and display of squared residuals.

Usage

regr2.plot(x, y, z,
           main.in="put a useful title here",
           resid.plot=FALSE,
           plot.base.plane=TRUE,
           plot.back.planes=TRUE,
           plot.base.points=FALSE,
           eye=NULL,                   ## S-Plus
           theta=0, phi=15, r=sqrt(3), ticktype="detailed", ## R
           ...)

Note

This plot is designed as a pedagogical example for introductory courses. When resid.plot=="square", then we actually see the set of squares for which the sum of their areas is minimized by the method of "least squares". The demo called in the examples section shows the geometry of regression coefficients, the change in predicted y when x1 is changed one unit holding all other x variables constant.

References

Heiberger, Richard M. and Holland, Burt (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. Springer-Verlag, New York. https://www.springer.com/us/book/9781493921218

Smith, W. and Gonick, L. (1993). The Cartoon Guide to Statistics. HarperCollins.

See Also

resid.squares, regr1.plot, persp

Aliases

• regr2.plot

Examples

# NOT RUN {
data(fat)
regr2.plot(fat[,"abdomin"], xlab="abdomin",
           fat[,"biceps"],  ylab="biceps",
           fat[,"bodyfat"], zlab="bodyfat",
           resid.plot="square",
           eye=c(335.5, 115.65, 171.9),   ## used only in S-Plus
           theta=140, phi=35, r=sqrt(15), ## used only in R
           box=is.R(),
           plot.back.planes=FALSE,
           main="Least-squares with two X-variables")

# }
# NOT RUN {
  demo("regr2", package="HH", ask=FALSE)
  ## run the file manually to see the individual steps.
# }