plot2d: 2D Effect Plot
In R2BayesX: Estimate Structured Additive Regression Models with 'BayesX'

plot2d

R Documentation

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 `jitter`ed `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.

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.

Author(s)

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

Examples

## 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)

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