plot2d: 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

plot2d(x, residuals = FALSE, rug = TRUE, jitter = TRUE, 
  col.residuals = NULL, col.lines = NULL, col.polygons = NULL, 
  col.rug = NULL, c.select = NULL, fill.select = NULL, 
  data = NULL, sep = "", month = NULL, year = NULL,
  step = 12, shift = NULL, trans = NULL, ...)

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 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. y ~ x, 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.
rug: add a rug to the plot.
jitter: if set to TRUE a jittered rug plot is added.
col.residuals: the color of the partial residuals.
col.lines: the color of the lines.
col.polygons: specify the background color of polygons, if x has at least 3 columns, i.e. column 2 and 3 can form one polygon.
col.rug: specify the color of the rug representation.
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. Note that first element of c.select should always be the column that holds the variable for the x-axis.
fill.select: integer vector, select pairwise the columns of the resulting data matrix that should form one polygon with a certain background color specified in argument col. E.g. x has three columns, or is specified with formula f1 + f2 ~ x, then setting fill.select = c(0, 1, 1) will draw a polygon with f1 and f2 as boundaries. If x has five columns or the formula is e.g. f1 + f2 + f3 + f4 ~ x, then setting fill.select = c(0, 1, 1, 2, 2), the pairs f1, f2 and f3, f4 are selected to form two polygons.
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 plot2d 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.
month, year, step: provide specific annotation for plotting estimation results for temporal variables. month and year define the minimum time point whereas step specifies the type of temporal data with step = 4, step = 2 and step = 1 corresponding to quartely, half yearly and yearly data.
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.

Author

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

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: see polygon,
...: other graphical parameters, see function plot.

Examples

Run this code

## 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))

if (FALSE) {
## 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)
}

## 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)