plot3d {bamlss}  R Documentation 
Function to plot 3D graphics or image and/or contour plots for bivariate effects/functions.
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 = "mba", linear = FALSE, extrap = FALSE, k = 40, ...)
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. Another
possibility is to specify the plot via a 
residuals 
If set to 
col.surface 
The color of the surface, may also be a function, e.g.

ncol 
the number of different colors that should be generated, if 
swap 
If set to 
col.residuals 
The color of the partial residuals, or if 
col.contour 
The color of the contour lines. 
c.select 
Integer vector of maximum length of columns of 
grid 
The grid size of the surface(s). 
image 
If set to 
contour 
If set to 
legend 
If 
cex.legend 
The expansion factor for the legend text, see 
breaks 
A set of breakpoints for the colors: must give one more breakpoint than

range 
Specifies a certain range values should be plotted for. 
digits 
Specifies the legend decimal places. 
d.persp 
See argument 
r.persp 
See argument 
outscale 
Scales the outer ranges of 
data 
If 
sep 
The field separator character when 
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 method should be used. The default is

linear 
Logical, should linear interpolation be used withing function

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 
... 
Parameters passed to 
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
.
Function plot3d
can use the akima package to construct smooth interpolated
surfaces, therefore, package akima needs to be installed. The akima package has an ACM
license that restricts applications to noncommercial usage, see
https://www.acm.org/publications/policies/softwarecopyrightnotice
Function plot3d
prints a note referring to the ACM license. This note can be suppressed by
setting
options("use.akima" = TRUE)
colorlegend
, plot2d
, plotmap
,
plotblock
, sliceplot
.
## Generate some data. set.seed(111) n < 500 ## Regressors. d < data.frame(z = runif(n, 3, 3), w = runif(n, 0, 6)) ## Response. d$y < with(d, 1.5 + cos(z) * sin(w) + rnorm(n, sd = 0.6)) ## Not run: ## Estimate model. b < bamlss(y ~ s(z,w), data = d) summary(b) ## Plot estimated effect. plot(b, model = "mu", term = "s(z,w)") ## Extract fitted values. f < fitted(b, model = "mu", term = "s(z,w)", intercept = FALSE) f < cbind(d[, c("z", "w")], f) ## Now use plot3d(). plot3d(f) plot3d(f, swap = TRUE) plot3d(f, grid = 100, border = NA) ## Only works if columns are named with ## '2.5 plot3d(f, c.select = 95, border = c("red", NA, "green"), col.surface = c(1, NA, 1), resid = TRUE, cex.resid = 0.2) ## Now some image and contour. # plot3d(f, image = TRUE, legend = FALSE) # plot3d(f, image = TRUE, legend = TRUE) # plot3d(f, image = TRUE, contour = TRUE) # plot3d(f, image = TRUE, contour = TRUE, swap = TRUE) # plot3d(f, image = TRUE, contour = TRUE, col.contour = "white") # plot3d(f, contour = TRUE) # plot3d(f, image = TRUE, contour = TRUE, c.select = 3) # plot3d(f, image = TRUE, contour = TRUE, c.select = "Mean") # plot3d(f, image = TRUE, contour = TRUE, c.select = "97.5 ## End(Not run) ## Variations. d$f1 < with(d, sin(z) * cos(w)) with(d, plot3d(cbind(z, w, f1))) ## Same with formula. plot3d(sin(z) * cos(w) ~ z + w, zlab = "f(z,w)", data = d) plot3d(sin(z) * cos(w) ~ z + w, zlab = "f(z,w)", data = d, ticktype = "detailed") ## Play with palettes. plot3d(sin(z) * cos(w) ~ z + w, col.surface = heat.colors, data = d) plot3d(sin(z) * cos(w) ~ z + w, col.surface = topo.colors, data = d) plot3d(sin(z) * cos(w) ~ z + w, col.surface = cm.colors, data = d) plot3d(sin(z) * cos(w) ~ z + w, col.surface = rainbow, data = d) plot3d(sin(z) * cos(w) ~ z + w, col.surface = terrain.colors, data = d) plot3d(sin(z) * cos(w) ~ z + w, col.surface = rainbow_hcl, data = d) plot3d(sin(z) * cos(w) ~ z + w, col.surface = diverge_hcl, data = d) plot3d(sin(z) * cos(w) ~ z + w, col.surface = sequential_hcl, data = d) plot3d(sin(z) * cos(w) ~ z + w, col.surface = rainbow_hcl(n = 99, c = 300, l = 80, start = 0, end = 100), data = d) # 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 = d)