# plot.coenocline: Plot species simulations along gradients In coenocliner: Coenocline Simulation

## Description

A simple S3 `plot` method for coenocline simulations.

## Usage

 ```1 2 3 4 5``` ```## S3 method for class 'coenocline' plot(x, type = "p", pch = 1, ...) ## S3 method for class 'coenocline' lines(x, lty = "solid", ...) ```

## Arguments

 `x` an object of class `"coenocline"`, the result of a call to `coenocline`. `type` character; the type of plot to produce. See `plot.default` for details. `pch` the plotting character to use. See `plot.default` for details. `...` additional arguments to `matplot`. `lty` the line type to use. See `plot.default` for details.

## Value

A plot is drawn on the current device.

Gavin L. Simpson

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```## Poisson counts along a single gradient, Gaussian response ## ========================================================= x <- seq(from = 4, to = 6, length = 100) opt <- c(3.75, 4, 4.55, 5, 5.5) + 0.5 tol <- rep(0.25, 5) h <- rep(20, 5) ## simulate set.seed(1) y <- coenocline(x, responseModel = "gaussian", params = cbind(opt = opt, tol = tol, h = h), countModel = "poisson") head(y) y <- coenocline(x, responseModel = "gaussian", params = cbind(opt = opt, tol = tol, h = h), countModel = "poisson", expectation = TRUE) plot(y, type = "l", lty = "solid") ```

### Example output

```This is coenocliner 0.2-2
[,1] [,2] [,3] [,4] [,5]
[1,]    9    3    0    0    0
[2,]   17    8    0    0    0
[3,]   18    4    0    0    0
[4,]   16    5    0    0    0
[5,]   21    5    0    0    0
[6,]   19    4    0    0    0
```

coenocliner documentation built on May 29, 2017, 3:57 p.m.