plot3d: 3D Effect Plot
In R2BayesX: Estimate Structured Additive Regression Models with 'BayesX'

plot3d

R Documentation

3D Effect Plot

Description

Function to plot 3D graphics or image and/or contour plots for bivariate effects/functions, typically used for objects of class "sm.bayesx" and "geo.bayesx" returned from function bayesx and read.bayesx.output.

Usage

plot3d(x, residuals = FALSE, col.surface = NULL, 
  ncol = 99L, swap = FALSE, col.residuals = NULL, col.contour = NULL, 
  c.select = NULL, grid = 30L, image = FALSE, contour = FALSE, 
  legend = TRUE, cex.legend = 1, breaks = NULL, range = NULL, 
  digits = 2L, d.persp = 1L, r.persp = sqrt(3), outscale = 0,
  data = NULL, sep = "", shift = NULL, trans = NULL,
  type = "interp", linear = FALSE, extrap = FALSE,
  k = 40, ...)

Arguments

`x`	a matrix or data frame, containing the covariates for which the effect should be plotted in the first and second column and at least a third column containing the effect, typically the structure for bivariate functions returned within `bayesx` and `read.bayesx.output` model term objects is used, also see `fitted.bayesx`. Another possibility is to specify the plot via a `formula`, e.g. for simple plotting of bivariate surfaces `z ~ x + y`, also see the example. `x` may also be a character file path to the data to be used for plotting.
`residuals`	if set to `TRUE`, partial residuals may also be plotted if available.
`col.surface`	the color of the surface, may also be a function, e.g. `col.surface = heat.colors`.
`ncol`	the number of different colors that should be generated, if `col.surface` is a function.
`swap`	if set to `TRUE` colors will be represented in reverse order.
`col.residuals`	the color of the partial residuals, or if `contour = TRUE` the color of the contour lines.
`col.contour`	the color of the contour lines.
`c.select`	`integer` vector of maximum length of columns of `x`, selects the columns of the resulting data matrix that should be used for plotting. E.g. if `x` has 5 columns, then `c.select = c(1, 2, 5)` will select column 1, 2 and 5 for plotting. If `c.select = 95` or `c.select = 80`, function `plot3d` will search for the corresponding columns to plot a 95`\%` or 80`\%` confidence surfaces respectively. Note that if e.g. `c.select = c(1, 2)`, `plot3d` will use columns 1 + 2 and 2 + 2 for plotting.
`grid`	the grid size of the surface(s).
`image`	if set to `TRUE`, an `image.plot` is drawn.
`contour`	if set to `TRUE`, a `contour` plot is drawn.
`legend`	if `image = TRUE` an additional legend may be added to the plot.
`cex.legend`	the expansion factor for the legend text, see `text`.
`breaks`	a set of breakpoints for the colors: must give one more breakpoint than `ncol`.
`range`	specifies a certain range values should be plotted for.
`digits`	specifies the legend decimal places.
`d.persp`	see argument `d` in function `persp`.
`r.persp`	see argument `r` in function `persp`.
`outscale`	scales the outer ranges of `x` and `z` limits used for interpolation.
`data`	if `x` is a `formula`, a `data.frame` or `list`. By default the variables are taken from `environment(x)`: typically the environment from which `plot3d` is called. Note that `data` may also be a character file path to the data.
`sep`	the field separator character when `x` or `data` is a character, see function `read.table`.
`shift`	numeric. Constant to be added to the smooth before plotting.
`trans`	function to be applied to the smooth before plotting, e.g., to transform the plot to the response scale.
`type`	character. Which type of interpolation metjod should be used. The default is `type = "interp"`, see function `interp`. The two other options are `type = "mba"`, which calls function `mba.surf` of package MBA, or `type = "mgcv"`, which uses a spatial smoother withing package mgcv for interpolation. The last option is definitely the slowest, since a full regression model needs to be estimated.
`linear`	logical. Should linear interpolation be used withing function `interp`?
`extrap`	logical. Should interpolations be computed outside the observation area (i.e., extrapolated)?
`k`	integer. The number of basis functions to be used to compute the interpolated surface when `type = "mgcv"`.
`...`	parameters passed to `colorlegend` if an image plot with legend is drawn, also other graphical parameters, please see the details.

Details

For 3D plots the following graphical parameters may be specified additionally:

cex: specify the size of partial residuals,
col: it is possible to specify the color for the surfaces if se > 0, then e.g. col = c("green", "black", "red"),
pch: the plotting character of the partial residuals,
...: other graphical parameters passed functions persp, image.plot and contour.

Author(s)

Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.

Examples

## generate some data
set.seed(111)
n <- 500

## regressors
dat <- data.frame(z = runif(n, -3, 3), w = runif(n, 0, 6))

## response
dat$y <- with(dat, 1.5 + cos(z) * sin(w) + rnorm(n, sd = 0.6))

## Not run: 
## estimate model
b <- bayesx(y ~ sx(z, w, bs = "te", knots = 5), data = dat, method = "REML")
summary(b)

## plot estimated effect
plot(b, term = "sx(z,w)")

## extract fitted effects
f <- fitted(b, term = "sx(z,w)")

## now use plot3d
plot3d(f)
plot3d(f, swap = TRUE)
plot3d(f, residuals = TRUE)
plot3d(f, resid = TRUE, cex.resid = 0.1)
plot3d(f, resid = TRUE, pch = 2, col.resid = "green3")
plot3d(f, resid = TRUE, c.select = 95, cex.resid = 0.1)
plot3d(f, resid = TRUE, c.select = 80, cex.resid = 0.1)
plot3d(f, grid = 100, border = NA)
plot3d(f, c.select = 95, border = c("red", NA, "green"),
  col.surface = c(1, NA, 1), resid = TRUE, cex.resid = 0.2)

## now some image and contour
plot3d(f, image = TRUE, legend = FALSE)
plot3d(f, image = TRUE, legend = TRUE)
plot3d(f, image = TRUE, contour = TRUE)
plot3d(f, image = TRUE, contour = TRUE, swap = TRUE)
plot3d(f, image = TRUE, contour = TRUE, col.contour = "white")
plot3d(f, contour = TRUE)
op <- par(no.readonly = TRUE)
par(mfrow = c(1, 3))
plot3d(f, image = TRUE, contour = TRUE, c.select = 3)
plot3d(f, image = TRUE, contour = TRUE, c.select = "Estimate")
plot3d(f, image = TRUE, contour = TRUE, c.select = "97.5
par(op)

## End(Not run)

## another variation
dat$f1 <- with(dat, sin(z) * cos(w))
with(dat, plot3d(cbind(z, w, f1)))

## same with formula 
plot3d(sin(z) * cos(w) ~ z + w, zlab = "f(z,w)", data = dat)
plot3d(sin(z) * cos(w) ~ z + w, zlab = "f(z,w)", data = dat, 
  ticktype = "detailed")

## play with palettes
plot3d(sin(z) * cos(w) ~ z + w, col.surface = heat.colors, data = dat)
plot3d(sin(z) * cos(w) ~ z + w, col.surface = topo.colors, data = dat)
plot3d(sin(z) * cos(w) ~ z + w, col.surface = cm.colors, data = dat)
plot3d(sin(z) * cos(w) ~ z + w, col.surface = rainbow, data = dat)
plot3d(sin(z) * cos(w) ~ z + w, col.surface = terrain.colors, data = dat)

plot3d(sin(z) * cos(w) ~ z + w, col.surface = rainbow_hcl, data = dat)
plot3d(sin(z) * cos(w) ~ z + w, col.surface = diverge_hcl, data = dat)
plot3d(sin(z) * cos(w) ~ z + w, col.surface = sequential_hcl, data = dat)

plot3d(sin(z) * cos(w) ~ z + w, 
  col.surface = rainbow_hcl(n = 99, c = 300, l = 80, start = 0, end = 100), 
  data = dat)
plot3d(sin(z) * cos(w) ~ z + w, 
  col.surface = rainbow_hcl(n = 99, c = 300, l = 80, start = 0, end = 100), 
  image = TRUE, grid = 200, data = dat)

R2BayesX documentation built on Oct. 20, 2023, 9:11 a.m.