plot.regvec3d: Plot method for regvec3d objects

In matlib: Matrix Functions for Teaching and Learning Linear Algebra and Multivariate Statistics

Plot method for regvec3d objects

Description

The plot method for regvec3d objects uses the low-level graphics tools in this package to draw 3D and 3D vector diagrams reflecting the partial and marginal relations of y to x1 and x2 in a bivariate multiple linear regression model, lm(y ~ x1 + x2).

The summary method prints the vectors and their vector lengths, followed by the summary for the model.

Usage

## S3 method for class 'regvec3d'
plot(
  x,
  y,
  dimension = 3,
  col = c("black", "red", "blue", "brown", "lightgray"),
  col.plane = "gray",
  cex.lab = 1.2,
  show.base = 2,
  show.marginal = FALSE,
  show.hplane = TRUE,
  show.angles = TRUE,
  error.sphere = c("none", "e", "y.hat"),
  scale.error.sphere = x$scale,
  level.error.sphere = 0.95,
  grid = FALSE,
  add = FALSE,
  ...
)

## S3 method for class 'regvec3d'
summary(object, ...)

## S3 method for class 'regvec3d'
print(x, ...)

Arguments

x	A “regvec3d” object
y	Ignored; only included for compatibility with the S3 generic
dimension	Number of dimensions to plot: 3 (default) or 2
col	A vector of 5 colors. col[1] is used for the y and residual (e) vectors, and for x1 and x2; col[2] is used for the vectors y -> yhat and y -> e; col[3] is used for the vectors yhat -> b1 and yhat -> b2;
col.plane	Color of the base plane in a 3D plot or axes in a 2D plot
cex.lab	character expansion applied to vector labels. May be a number or numeric vector corresponding to the the rows of X, recycled as necessary.
show.base	If show.base > 0, draws the base plane in a 3D plot; if show.base > 1, the plane is drawn thicker
show.marginal	If TRUE also draws lines showing the marginal relations of y on x1 and on x2
show.hplane	If TRUE, draws the plane defined by y, yhat and the origin in the 3D
show.angles	If TRUE, draw and label the angle between the x1 and x2 and between y and yhat, corresponding respectively to the correlation between the xs and the multiple correlation
error.sphere	Plot a sphere (or in 2D, a circle) of radius proportional to the length of the residual vector, centered either at the origin ("e") or at the fitted-values vector ("y.hat"; the default is "none".)
scale.error.sphere	Whether to scale the error sphere if error.sphere="y.hat"; defaults to TRUE if the vectors representing the variables are scaled, in which case the oblique projections of the error spheres can represent confidence intervals for the coefficients; otherwise defaults to FALSE.
level.error.sphere	The confidence level for the error sphere, applied if scale.error.sphere=TRUE.
grid	If TRUE, draws a light grid on the base plane
add	If TRUE, add to the current plot; otherwise start a new rgl or plot window
...	Parameters passed down to functions [unused now]
object	A regvec3d object for the summary method

Details

A 3D diagram shows the vector y and the plane formed by the predictors, x1 and x2, where all variables are represented in deviation form, so that the intercept need not be included.

A 2D diagram, using the first two columns of the result, can be used to show the projection of the space in the x1, x2 plane.

The drawing functions vectors and link{vectors3d} used by the plot.regvec3d method only work reasonably well if the variables are shown on commensurate scales, i.e., with either scale=TRUE or normalize=TRUE.

Value

None

References

Fox, J. (2016). Applied Regression Analysis and Generalized Linear Models, 3rd ed., Sage, Chapter 10.

See Also

regvec3d, vectors3d, vectors

Other vector diagrams: Proj(), arc(), arrows3d(), circle3d(), corner(), pointOnLine(), regvec3d(), vectors(), vectors3d()

Examples

if (require(carData)) {
   data("Duncan", package="carData")
   dunc.reg <- regvec3d(prestige ~ income + education, data=Duncan)
   plot(dunc.reg)
   plot(dunc.reg, dimension=2)
   plot(dunc.reg, error.sphere="e")
   summary(dunc.reg)

   # Example showing Simpson's paradox
   data("States", package="carData")
   states.vec <- regvec3d(SATM ~ pay + percent, data=States, scale=TRUE)
   plot(states.vec, show.marginal=TRUE)
   plot(states.vec, show.marginal=TRUE, dimension=2)
   summary(states.vec)
}

matlib documentation built on Oct. 3, 2024, 1:09 a.m.