2D Effect Plot
Description
Function to plot simple 2D graphics for univariate effects/functions, typically used for objects
of class "linear.bayesx"
and "sm.bayesx"
returned from function bayesx
and read.bayesx.output
.
Usage
1 2 3 4 5 
Arguments
x 
a matrix or data frame, containing the covariate for which the effect should be plotted
in the first column and at least a second column containing the effect, typically the structure
for univariate functions returned within 
residuals 
if set to 
rug 
add a 
jitter 
if set to 
col.residuals 
the color of the partial residuals. 
col.lines 
the color of the lines. 
col.polygons 
specify the background color of polygons, if 
col.rug 
specify the color of the rug representation. 
c.select 

fill.select 

data 
if 
sep 
the field separator character when 
month, year, step 
provide specific annotation for plotting estimation results for temporal
variables. 
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. 
... 
other graphical parameters, please see the details. 
Details
For 2D plots the following graphical parameters may be specified additionally:

cex
: specify the size of partial residuals, 
lty
: the line type for each column that is plotted, e.g.lty = c(1, 2)
, 
lwd
: the line width for each column that is plotted, e.g.lwd = c(1, 2)
, 
poly.lty
: the line type to be used for the polygons, 
poly.lwd
: the line width to be used for the polygons, 
density
angle
,border
: seepolygon
, 
...
: other graphical parameters, see functionplot
.
Author(s)
Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis.
See Also
plot.bayesx
, bayesx
, read.bayesx.output
,
fitted.bayesx
.
Examples
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  ## generate some data
set.seed(111)
n < 500
## regressor
dat < data.frame(x = runif(n,3,3))
## response
dat$y < with(dat, 10 + sin(x) + rnorm(n,sd=0.6))
## Not run:
## estimate model
b < bayesx(y ~ sx(x), data = dat)
summary(b)
## plot estimated effect
plot(b, which = 1)
plot(b, which = 1, rug = FALSE)
## extract fitted effects
f < fitted(b, term = "sx(x)")
## now use plot2d
plot2d(f)
plot2d(f, residuals = TRUE)
plot2d(f, residuals = TRUE, pch = 2, col.resid = "green3")
plot2d(f, col.poly = NA, lwd = 1, lty = 1)
plot2d(f, col.poly = NA, lwd = 1, lty = 1, col.lines = 4)
plot2d(f, col.poly = c(2, 3), lwd = 1, col.lines = 4, lty = 1)
plot2d(f, lwd = c(1, 3, 2, 2, 3), col.poly = NA, lty = 1)
plot2d(f, lwd = c(1, 3, 2, 2, 3), col.poly = NA, lty = 1, col.lines = 2:6)
plot2d(f, lwd = c(1, 3, 2, 2, 3), col.poly = NA, lty = 1, col.lines = 2:6,
resid = TRUE, pch = 4, col.resid = 7)
## End(Not run)
## another variation
plot2d(sin(x) ~ x, data = dat)
dat$f < with(dat, sin(dat$x))
plot2d(f ~ x, data = dat)
dat$f1 < with(dat, f + 0.1)
dat$f2 < with(dat, f  0.1)
plot2d(f1 + f2 ~ x, data = dat)
plot2d(f1 + f2 ~ x, data = dat, fill.select = c(0, 1, 1), lty = 0)
plot2d(f1 + f2 ~ x, data = dat, fill.select = c(0, 1, 1), lty = 0,
density = 20, poly.lty = 1, poly.lwd = 2)
plot2d(f1 + f + f2 ~ x, data = dat, fill.select = c(0, 1, 0, 1),
lty = c(0, 1, 0), density = 20, poly.lty = 1, poly.lwd = 2)
