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Last data update: 2014.03.03

R: Plot Slices of Bivariate Functions

sliceplot

R Documentation

Plot Slices of Bivariate Functions

Description

This function plots slices from user defined values of bivariate surfaces.

Usage

sliceplot(x, y = NULL, z = NULL, view = 1, c.select = NULL,
  values = NULL, probs = c(0.1, 0.5, 0.9), grid = 100,
  legend = TRUE, pos = "topright", digits = 2, data = NULL,
  rawdata = FALSE, type = "akima", linear = FALSE,
  extrap = FALSE, k = 40, rug = TRUE, rug.col = NULL,
  jitter = TRUE, ...)

Arguments

`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 `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. for simple plotting of bivariate surfaces `z ~ x + y`, also see the example.
`y`	if `x` is a vector the argument `y` and `z` must also be supplied as vectors.
`z`	if `x` is a vector the argument `y` and `z` must also be supplied as vectors, `z` defines the surface given by z = f(x, y).
`view`	which variable should be used for the x-axis of the plot, the other variable will be used to compute the slices. May also be a `character` with the name of the corresponding variable.
`c.select`	`integer`, selects the column that is used in the resulting matrix to be used as the `z` argument.
`values`	the values of the `x` or `y` variable that should be used for computing the slices, if set to `NULL`, slices will be constructed according to the quantiles, see also argument `probs`.
`probs`	numeric vector of probabilities with values in [0,1] to be used within function `quantile` to compute the `values` for plotting the slices.
`grid`	the grid size of the surface where the slices are generated from.
`legend`	if set to `TRUE`, a legend with the `values` that where used for slicing will be added.
`pos`	the position of the legend, see also function `legend`.
`digits`	the decimal place the legend values should be rounded.
`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 `plot3d` is called.
`rawdata`	if set to `TRUE`, the data will not be interpolated, only raw data will be used. This is useful when displaying data on a regular grid.
`type`	character. Which type of interpolation metjod should be used. The default is `type = "akima"`, see function `interp`. The two other options are `type = "mba"`, which calls function `mba.surf` of package MBA, or `type = "mgcv"`, which uses a spatial smoother withing package mgcv for interpolation. The last option is definitely the slowest, since a full regression model needs to be estimated.
`linear`	logical. Should linear interpolation be used withing function `interp`?
`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 `type = "mgcv"`.
`rug`	add a `rug` to the plot.
`jitter`	if set to `TRUE` a `jitter`ed `rug` plot is added.
`rug.col`	specify the color of the rug representation.
`...`	parameters passed to `matplot` and `legend`.

Details

Similar to function plot3d, this function first applies bivariate interpolation on a regular grid, afterwards the slices are computed from the resulting surface.

Note

Function sliceplot 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 non-commercial usage, see

http://www.acm.org/publications/policies/softwarecrnotice

Function sliceplot prints a note refering to the ACM licence. This note can be supressed by setting

options("use.akima" = TRUE)

Author(s)

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

Examples

## 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)", sliceplot = TRUE)
plot(b, term = "sx(z,w)", sliceplot = TRUE, view = 2)
plot(b, term = "sx(z,w)", sliceplot = TRUE, view = "w")
plot(b, term = "sx(z,w)", sliceplot = TRUE, c.select = 4)
plot(b, term = "sx(z,w)", sliceplot = TRUE, c.select = 6)
plot(b, term = "sx(z,w)", sliceplot = TRUE, probs = seq(0, 1, length = 10))

## End(Not run)

## another variation
dat$f1 <- with(dat, sin(z) * cos(w))
sliceplot(cbind(z = dat$z, w = dat$w, f1 = dat$f1))

## same with formula 
sliceplot(sin(z) * cos(w) ~ z + w, ylab = "f(z)", data = dat)

## compare with plot3d()
plot3d(sin(z) * 1.5 * w ~ z + w, zlab = "f(z,w)", data = dat)
sliceplot(sin(z) * 1.5 * w ~ z + w, ylab = "f(z)", data = dat)
sliceplot(sin(z) * 1.5 * w ~ z + w, view = 2, ylab = "f(z)", data = dat)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(R2BayesX)
Loading required package: BayesXsrc
Loading required package: colorspace
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-12. For overview type 'help("mgcv-package")'.
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/R2BayesX/sliceplot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sliceplot
> ### Title: Plot Slices of Bivariate Functions
> ### Aliases: sliceplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> ## generate some data
> set.seed(111)
> n <- 500
> 
> ## regressors
> dat <- data.frame(z = runif(n, -3, 3), w = runif(n, 0, 6))
> 
> ## response
> dat$y <- with(dat, 1.5 + cos(z) * sin(w) + rnorm(n, sd = 0.6))
> 
> ## Not run: 
> ##D ## estimate model
> ##D b <- bayesx(y ~ sx(z, w, bs = "te", knots = 5), data = dat, method = "REML")
> ##D summary(b)
> ##D 
> ##D ## plot estimated effect
> ##D plot(b, term = "sx(z,w)", sliceplot = TRUE)
> ##D plot(b, term = "sx(z,w)", sliceplot = TRUE, view = 2)
> ##D plot(b, term = "sx(z,w)", sliceplot = TRUE, view = "w")
> ##D plot(b, term = "sx(z,w)", sliceplot = TRUE, c.select = 4)
> ##D plot(b, term = "sx(z,w)", sliceplot = TRUE, c.select = 6)
> ##D plot(b, term = "sx(z,w)", sliceplot = TRUE, probs = seq(0, 1, length = 10))
> ## End(Not run)
> 
> ## another variation
> dat$f1 <- with(dat, sin(z) * cos(w))
> sliceplot(cbind(z = dat$z, w = dat$w, f1 = dat$f1))
Loading required namespace: akima
NOTE: Package 'akima' has an ACM license that restricts applications to non-commercial usage.
> 
> ## same with formula 
> sliceplot(sin(z) * cos(w) ~ z + w, ylab = "f(z)", data = dat)
NOTE: Package 'akima' has an ACM license that restricts applications to non-commercial usage.
> 
> ## compare with plot3d()
> plot3d(sin(z) * 1.5 * w ~ z + w, zlab = "f(z,w)", data = dat)
NOTE: Package 'akima' has an ACM license that restricts applications to non-commercial usage.
> sliceplot(sin(z) * 1.5 * w ~ z + w, ylab = "f(z)", data = dat)
NOTE: Package 'akima' has an ACM license that restricts applications to non-commercial usage.
> sliceplot(sin(z) * 1.5 * w ~ z + w, view = 2, ylab = "f(z)", data = dat)
NOTE: Package 'akima' has an ACM license that restricts applications to non-commercial usage.
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>