Description Usage Arguments Details Note Author(s) See Also Examples
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
.
1 2 3 4 5 6 7 8  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 = "akima", 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, typically
the structure for bivariate functions returned within 
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 

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 metjod 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
uses per default 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
http://www.acm.org/publications/policies/softwarecrnotice
Function plot3d
prints a note refering to the ACM licence. This note can be supressed by
setting
options("use.akima" = TRUE)
Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.
plot.bayesx
, bayesx
, read.bayesx.output
,
fitted.bayesx
, colorlegend
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75  ## 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)

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