plotPlane: Draw a 3-D regression plot for two predictors from any linear...

In rockchalk: Regression Estimation and Presentation

Draw a 3-D regression plot for two predictors from any linear or nonlinear lm or glm object

Description

This allows user to fit a regression model with many variables and then plot 2 of its predictors and the output plane for those predictors with other variables set at mean or mode (numeric or factor). This is a front-end (wrapper) for R's persp function. Persp does all of the hard work, this function reorganizes the information for the user in a more readily understood way. It intended as a convenience for students (or others) who do not want to fight their way through the details needed to use persp to plot a regression plane. The fitted model can have any number of input variables, this will display only two of them. And, at least for the moment, I insist these predictors must be numeric variables. They can be transformed in any of the usual ways, such as poly, log, and so forth.

Usage

plotPlane(
  model = NULL,
  plotx1 = NULL,
  plotx2 = NULL,
  drawArrows = FALSE,
  plotPoints = TRUE,
  npp = 20,
  x1lab,
  x2lab,
  ylab,
  x1lim,
  x2lim,
  x1floor = 5,
  x2floor = 5,
  pch = 1,
  pcol = "blue",
  plwd = 0.5,
  pcex = 1,
  llwd = 0.3,
  lcol = 1,
  llty = 1,
  acol = "red",
  alty = 4,
  alwd = 0.3,
  alength = 0.1,
  linesFrom,
  lflwd = 3,
  envir = environment(formula(model)),
  ...
)

## Default S3 method:
plotPlane(
  model = NULL,
  plotx1 = NULL,
  plotx2 = NULL,
  drawArrows = FALSE,
  plotPoints = TRUE,
  npp = 20,
  x1lab,
  x2lab,
  ylab,
  x1lim,
  x2lim,
  x1floor = 5,
  x2floor = 5,
  pch = 1,
  pcol = "blue",
  plwd = 0.5,
  pcex = 1,
  llwd = 0.3,
  lcol = 1,
  llty = 1,
  acol = "red",
  alty = 4,
  alwd = 0.3,
  alength = 0.1,
  linesFrom,
  lflwd = 3,
  envir = environment(formula(model)),
  ...
)

Arguments

model	an lm or glm fitted model object
plotx1	name of one variable to be used on the x1 axis
plotx2	name of one variable to be used on the x2 axis
drawArrows	draw red arrows from prediction plane toward observed values TRUE or FALSE
plotPoints	Should the plot include scatter of observed scores?
npp	number of points at which to calculate prediction
x1lab	optional label
x2lab	optional label
ylab	optional label
x1lim	optional lower and upper bounds for x1, as vector like c(0,1)
x2lim	optional lower and upper bounds for x2, as vector like c(0,1)
x1floor	Default=5. Number of "floor" lines to be drawn for variable x1
x2floor	Default=5. Number of "floor" lines to be drawn for variable x2
pch	plot character, passed on to the "points" function
pcol	color for points, col passed to "points" function
plwd	line width, lwd passed to "points" function
pcex	character expansion, cex passed to "points" function
llwd	line width, lwd passed to the "lines" function
lcol	line color, col passed to the "lines" function
llty	line type, lty passed to the "lines" function
acol	color for arrows, col passed to "arrows" function
alty	arrow line type, lty passed to the "arrows" function
alwd	arrow line width, lwd passed to the "arrows" function
alength	arrow head length, length passed to "arrows" function
linesFrom	object with information about "highlight" lines to be added to the 3d plane (output from plotCurves or plotSlopes)
lflwd	line widths for linesFrom highlight lines
envir	environment from whence to grab data
...	additional parameters that will go to persp

Details

Besides a fitted model object, plotPlane requires two additional arguments, plotx1 and plotx2. These are the names of the plotting variables. Please note, that if the term in the regression is something like poly(fish,2) or log(fish), then the argument to plotx1 should be the quoted name of the variable "fish". plotPlane will handle the work of re-organizing the information so that R's predict functions can generate the desired information. This might be thought of as a 3D version of "termplot", with a significant exception. The calculation of predicted values depends on predictors besides plotx1 and plotx2 in a different ways. The sample averages are used for numeric variables, but for factors the modal value is used.

This function creates an empty 3D drawing and then fills in the pieces. It uses the R functions lines, points, and arrows. To allow customization, several parameters are introduced for the users to choose colors and such. These options are prefixed by "l" for the lines that draw the plane, "p" for the points, and "a" for the arrows. Of course, if plotPoints=FALSE or drawArrows=FALSE, then these options are irrelevant.

Value

The main point is the plot that is drawn, but for record keeping the return object is a list including 1) res: the transformation matrix that was created by persp 2) the call that was issued, 3) x1seq, the "plot sequence" for the x1 dimension, 4) x2seq, the "plot sequence" for the x2 dimension, 5) zplane, the values of the plane corresponding to locations x1seq and x2seq.

Author(s)

Paul E. Johnson [e-mail]

See Also

persp, scatterplot3d, regr2.plot

Examples

library(rockchalk)


set.seed(12345)
x1 <- rnorm(100)
x2 <- rnorm(100)
x3 <- rnorm(100)
x4 <- rnorm(100)
y <- rnorm(100)
y2 <- 0.03 + 0.1*x1 + 0.1*x2 + 0.25*x1*x2 + 0.4*x3 -0.1*x4 + 1*rnorm(100)
dat <- data.frame(x1,x2,x3,x4,y, y2)
rm(x1, x2, x3, x4, y, y2)

## linear ordinary regression
m1 <- lm(y ~ x1 + x2 +x3 + x4, data = dat)

plotPlane(m1, plotx1 = "x3", plotx2 = "x4")

plotPlane(m1, plotx1 = "x3", plotx2 = "x4", drawArrows = TRUE)

plotPlane(m1, plotx1 = "x1", plotx2 = "x4", drawArrows = TRUE)


plotPlane(m1, plotx1 = "x1", plotx2 = "x2", drawArrows = TRUE, npp = 10)
plotPlane(m1, plotx1 = "x3", plotx2 = "x2", drawArrows = TRUE, npp = 40)

plotPlane(m1, plotx1 = "x3", plotx2 = "x2", drawArrows = FALSE,
          npp = 5, ticktype = "detailed")


## regression with interaction
m2 <- lm(y ~ x1  * x2 +x3 + x4, data = dat)

plotPlane(m2, plotx1 = "x1", plotx2 = "x2", drawArrows = TRUE)


plotPlane(m2, plotx1 = "x1", plotx2 = "x4", drawArrows = TRUE)
plotPlane(m2, plotx1 = "x1", plotx2 = "x3", drawArrows = TRUE)

plotPlane(m2, plotx1 = "x1", plotx2 = "x2", drawArrows = TRUE,
          phi = 10, theta = 30)



## regression with quadratic;
## Required some fancy footwork in plotPlane, so be happy
dat$y3 <- 0 + 1 * dat$x1 + 2 * dat$x1^2 + 1 * dat$x2 +
    0.4*dat$x3 + 8 * rnorm(100)
m3 <- lm(y3 ~ poly(x1,2) + x2 +x3 + x4, data = dat)
summary(m3)

plotPlane(m3, plotx1 = "x1", plotx2 = "x2", drawArrows = TRUE,
          x1lab = "my great predictor", x2lab = "a so-so predictor",
          ylab = "Most awesomest DV ever")

plotPlane(m3, plotx1 = "x1", plotx2 = "x2", drawArrows = TRUE,
          x1lab = "my great predictor", x2lab = "a so-so predictor",
          ylab = "Most awesomest DV ever", phi = -20)

plotPlane(m3, plotx1 = "x1", plotx2 = "x2", drawArrows = TRUE,
          phi = 10, theta = 30)

plotPlane(m3, plotx1 = "x1", plotx2 = "x4", drawArrows = TRUE,
          ticktype = "detailed")
plotPlane(m3, plotx1 = "x1", plotx2 = "x3", drawArrows = TRUE)

plotPlane(m3, plotx1 = "x1", plotx2 = "x2", drawArrows = TRUE,
          phi = 10, theta = 30)

m4 <- lm(y ~ sin(x1) + x2*x3 +x3 + x4, data = dat)
summary(m4)


plotPlane(m4, plotx1 = "x1", plotx2 = "x2", drawArrows = TRUE)
plotPlane(m4, plotx1 = "x1", plotx2 = "x3", drawArrows = TRUE)



eta3 <- 1.1 + .9*dat$x1 - .6*dat$x2 + .5*dat$x3
dat$y4 <- rbinom(100, size = 1, prob = exp( eta3)/(1+exp(eta3)))
gm1 <- glm(y4 ~ x1 + x2 + x3, data = dat, family = binomial(logit))
summary(gm1)
plotPlane(gm1, plotx1 = "x1", plotx2 = "x2")
plotPlane(gm1, plotx1 = "x1", plotx2 = "x2", phi = -10)

plotPlane(gm1, plotx1 = "x1", plotx2 = "x2", ticktype = "detailed")
plotPlane(gm1, plotx1 = "x1", plotx2 = "x2", ticktype = "detailed",
          npp = 30, theta = 30)
plotPlane(gm1, plotx1 = "x1", plotx2 = "x3", ticktype = "detailed",
          npp = 70, theta = 60)

plotPlane(gm1, plotx1 = "x1", plotx2 = "x2", ticktype = c("detailed"),
          npp = 50, theta = 40)

dat$x2 <- 5 * dat$x2
dat$x4 <- 10 * dat$x4
eta4 <- 0.1 + .15*dat$x1 - 0.1*dat$x2 + .25*dat$x3 + 0.1*dat$x4
dat$y4 <- rbinom(100, size = 1, prob = exp( eta4)/(1+exp(eta4)))
gm2 <- glm(y4 ~ x1 + x2 + x3 + x4, data = dat, family = binomial(logit))
summary(gm2)
plotPlane(gm2, plotx1 = "x1", plotx2 = "x2")
plotPlane(gm2, plotx1 = "x2", plotx2 = "x1")
plotPlane(gm2, plotx1 = "x1", plotx2 = "x2", phi = -10)
plotPlane(gm2, plotx1 = "x1", plotx2 = "x2", phi = 5, theta = 70, npp = 40)

plotPlane(gm2, plotx1 = "x1", plotx2 = "x2", ticktype = "detailed")
plotPlane(gm2, plotx1 = "x1", plotx2 = "x2", ticktype = "detailed",
          npp = 30, theta = -30)
plotPlane(gm2, plotx1 = "x1", plotx2 = "x3", ticktype = "detailed",
          npp = 70, theta = 60)

plotPlane(gm2, plotx1 = "x4", plotx2 = "x3", ticktype = "detailed",
          npp = 50, theta = 10)

plotPlane(gm2, plotx1 = "x1", plotx2 = "x2", ticktype = c("detailed"))

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