knitr::opts_chunk$set(fig.width=6, fig.height=6)

The ContourFunctions R package provides functions that make it easier to make contour plots. The function `cf`

is a quick function that can take in grid data, a function, or any data, and give a contour plot showing the function or data.
By default, plots are made using base graphics,
but they can also be done using ggplot2.

`cf_grid`

`cf_grid`

creates a contour plot from a grid of points.

Below `a`

and `b`

create a grid of points at which `r`

is calculated. `cf_grid`

is used to create the contour plot.
Note that the only indication of the relationship between the colors and the `r`

values is in the title of plot, which says that the darkest blue point is the minimum of -0.613, and the darkest pink point is the maximum of 1. (Note that this is not a good representation of the surface because there aren't enough points in the grid, the contours are actually concentric circles as shown below.)

library(ContourFunctions) a <- b <- seq(-4*pi, 4*pi, len = 27) r <- sqrt(outer(a^2, b^2, "+")) cf_grid(a, b, cos(r^2)*exp(-r/(2*pi)))

To add a bar that shows how the colors relate to the output, simply set `bar=TRUE`

, as shown below.

cf_grid(a, b, cos(r^2)*exp(-r/(2*pi)), bar=TRUE)

Other parameters specifying details of the plot can be passed as well, see the documentation for those options.

`cf_func`

For the above we had to create the grid of points and give it in to `cf_grid`

. To make this easier, `cf_func`

allows you to simply pass in a function. It will then evaluate the function at a grid of points and pass these to `cf_grid`

to make the contour plot.

f1 <- function(r) cos(r[1]^2 + r[2]^2)*exp(-sqrt(r[1]^2 + r[2]^2)/(2*pi)) cf_func(f1, xlim = c(-4*pi, 4*pi), ylim = c(-4*pi, 4*pi))

If you give a function that can more efficient evaluate a bunch of points at a time, instead of one at a time, use the `batchmax`

to have it pass points as a matrix to the given function.

The argument `n`

controls how many points along each dimension are used. We see below that if we go back to `n=27`

, then we get the same plot as above.

cf_func(f1, xlim = c(-4*pi, 4*pi), ylim = c(-4*pi, 4*pi), n=27)

`cf_data`

Often one has data and wants to get an idea of what the surface looks like that fits the data. The `cf_data`

allows the user to pass in the data to get such a plot. A Gaussian process model is fit to the data, by default using the R package laGP to do so. The model is then used to make predictions at the grid of points to make the contour plot. The model prediction function is passed to `cf_func`

to create the contour plot.
Note that this relies heavily on the model being somewhat accurate, and may not truly represent the data if the model is a poor fit.

Below a random sample of 20 points are taken from a function (a Gaussian peak centered at (0.5, 0.5)), and `cf_data`

is used to plot the data. The black dots show the data points used to create the model.

set.seed(0) x <- runif(20) y <- runif(20) z <- exp(-(x-.5)^2-5*(y-.5)^2)# + rnorm(20,0,.05) # cf_data(x,y,z) cf_data(x,y,z, bar=T)

`afterplotfunc`

The contour plots are created using the `split.screen`

function.
This causes the plot to not add additional items, such as points or lines, after making the plot. The plot below shows how when trying to add a point to the plot using `points`

, a point that should be placed at the center ends up in the bottom right corner.

cf_func(f1, xlim = c(-4*pi, 4*pi), ylim = c(-4*pi, 4*pi)) points(c(0,0), pch=19)

If you just want to add points, you can use the parameter `pts`

to do so. Below we see that the point ends up correctly in the center of the plot.

cf_func(f1, xlim = c(-4*pi, 4*pi), ylim = c(-4*pi, 4*pi), pts=c(0,0))

Another option, that gives you more capability, is to use the parameter `afterplotfunc`

to pass in a function that takes no arguments. After the plot is made this function will be called. You can put anything inside this function that you would normally do to a plot, including `points`

, `text`

, `legend`

, and `abline`

.

cf_func(f1, xlim = c(-4*pi, 4*pi), ylim = c(-4*pi, 4*pi), afterplotfunc=function() { points(5, 5, pch=19) text(-5,5,"Text here") legend('bottomright', legend=c(1,2,3), fill=c(1,2,3)) abline(a=0, b=1, col=2) } )

`cf`

To make using the above `cf_func`

and `cf_data`

slightly easier, the same inputs can be passed to the function `cf`

. It detects whether the first parameter is a function, in which case it passes everything to `cf_func`

or numeric, in which case it passes everything to `cf_data`

.

The following two plots demonstrate how `cf`

is used. Really the only benefit is that is saves you typing `_func`

or `_grid`

.

cf(f1, xlim = c(-4*pi, 4*pi), ylim = c(-4*pi, 4*pi))

cf(x,y,z, bar=T)

`cf_highdim`

For higher dimensional functions, `cf_highdim`

makes a contour plot
of two-dimensional slices of the given function.
The dimensions not being shown can be set to a default value
or averaged out.

friedman <- function(x) { 10*sin(pi*x[1]*x[2]) + 20*(x[3]-.5)^2 + 10*x[4] + 5*x[5] } cf_highdim(friedman, 5, color.palette=topo.colors)

`cf_4dim`

Functions with four input dimensions can be displayed
using a grid of contour plots with the function `cf_4dim`

.
Two of the dimensions are shown on each plot,
while the other two are set to a specific value
for the given plot.

cf_4dim(function(x) {x[1] + x[2]^2 + sin(2*pi*x[3])})

All of the above plots used R base graphics.
Similar functions for `cf`

, `cf_func`

, `cf_data`

, and `cf_grid`

that use ggplot2 are also available as
`gcf`

, `gcf_func`

, `gcf_data`

, and `gcf_grid`

f2 <- function(x) {exp(x[1]) * sin(2*pi*x[2])} gcf(f2)

By default, the contour plots are made using filled colors.
To add lines on top of the color fill, use `with_lines=TRUE`

.
To make a contour plot with only lines, use `lines_only=TRUE`

.

cf(f2, with_lines=TRUE)

gcf(f2, lines_only=TRUE, bar=T)

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