persp.coenocline {coenocliner}

Perspective Plot of Species Simulations Along Gradients

Description

A simple S3 persp method for coenocline simulations.

Usage

## S3 method for class 'coenocline'
persp(x, species = NULL, theta = 45, phi = 30, ...)

Arguments

x

an object of class "coenocline", the result of a call to coenocline.

species

vector indicating which species to plot. This can be any vector that you can use to subset a matrix, but numeric or logical vectors would be mostly commonly used.

theta, phi

angles defining the viewing direction. theta gives the azimuthal direction and phi the colatitude. See persp for further details.

...

additional arguments to persp.

Value

A plot is drawn on the current device.

Author(s)

Gavin L. Simpson

Examples

## Poisson counts along two correlated gradients, Gaussian response
## ================================================================

set.seed(1)
N <-  40
x1 <- seq(from = 4, to = 6, length = N)
opt1 <- seq(4, 6, length = 5)
tol1 <- rep(0.25, 5)
x2 <- seq(from = 2, to = 20, length = N)
opt2 <- seq(2, 20, length = 5)
tol2 <- rep(1, 5)
h <- rep(30, 5)
xy <- expand.grid(x = x1, y = x2)

set.seed(1)
params <- list(px = list(opt = opt1, tol = tol1, h = h),
               py = list(opt = opt2, tol = tol2))
y <- coenocline(xy,
                responseModel = "gaussian",
                params = params,
                extraParams = list(corr = 0.5),
                countModel = "poisson")

## perspective plot(s) of simulated counts
layout(matrix(1:6, ncol = 3))
op <- par(mar = rep(1, 4))
persp(y)
par(op)
layout(1)

## as before but now just expectations
y <- coenocline(xy,
                responseModel = "gaussian",
                params = params,
                extraParams = list(corr = 0.5),
                countModel = "poisson",
                expectation = TRUE)

## perspective plots of response curves
layout(matrix(1:6, ncol = 3))
op <- par(mar = rep(1, 4))
persp(y)
par(op)
layout(1)

## Same plots generated using the `plot` method
layout(matrix(1:6, ncol = 3))
op <- par(mar = rep(1, 4))
persp(y)
par(op)
layout(1)