deSolve (version 1.7)

plot.deSolve: Plot and Histogram Method for deSolve Objects

Description

Plot the output of numeric integration routines.

Usage

## S3 method for class 'deSolve':
plot(x, which = 1:(ncol(x)-1), ask = NULL, ...)
## S3 method for class 'deSolve':
hist(x, which = 1:(ncol(x)-1), ask = NULL, ...)
## S3 method for class 'deSolve':
image(x, which = NULL, ask = NULL, 
  add.contour = FALSE, grid = NULL, ...)

Arguments

x

an object of class deSolve, as returned by the integrators, and to be plotted.

which

the name(s) or the index to the variables that should be plotted. Default = all variables.

ask

logical; if TRUE, the user is asked before each plot, if NULL the user is only asked if more than one page of plots is necessary and the current graphics device is set interactive, see

add.contour

if TRUE, will add contours to the image plot.

grid

only for image plots: the 1-D grid as a vector (output generated with ode.1D, or the x- and y-grid, as a list for output generated with ode.2D.

...

additional graphics arguments passed to plot.default, image or hist

Details

The number of panels per page is automatically determined up to 3 x 3 (par(mfrow=c(3, 3))). This default can be overwritten by specifying user-defined settings for mfrow or mfcol.

Other graphical parameters can be passed as well. Parameters xlab and ylab are vectorized, so it is possible to assign specific axis labels to individual plots. Image plots will only work for 1-D and 2-D variables, as solved with ode.1D and ode.2D. In the first case, an image with times as x- and the grid as y-axis will be created. in second case, an x-y plot will be created, for all times. Unless ask=FALSE, the user will be asked to confirm page changes.

See Also

print.deSolve, ode, deSolve

Examples

## =======================================================================
## A Predator-Prey model with 4 species in matrix formulation
## =======================================================================

LVmatrix <- function(t, n, parms) {
  with(parms, {
    dn <- r * n + n * (A %*% n)
    return(list(c(dn)))
  })
}
parms <- list(
  r = c(r1 = 0.1, r2 = 0.1, r3 = -0.1, r4 = -0.1),
  A = matrix(c(0.0, 0.0, -0.2, 0.01,      # prey 1
               0.0, 0.0, 0.02, -0.1,      # prey 2
               0.2, 0.02, 0.0, 0.0,       # predator 1; prefers prey 1
               0.01, 0.1, 0.0, 0.0),      # predator 2; prefers prey 2
               nrow = 4, ncol = 4, byrow=TRUE)
)
times <- seq(from = 0, to = 500, by = 0.1)
y     <- c(prey1 = 1, prey2 = 1, pred1 = 2, pred2 = 2)

out <- ode(y, times, LVmatrix, parms)

## Basic line plot
plot(out, type = "l")

## User-specified axis labels
plot(out, type = "l", ylab = c("Prey 1", "Prey 2", "Pred 1", "Pred 2"),
  xlab = "Time (d)", main = "Time Series") 

hist(out, col="darkblue", breaks = 50)

## =======================================================================
## The Aphid model from Soetaert and Herman, 2009.
## A practical guide to ecological modelling.
## Using R as a simulation platform. Springer.
## =======================================================================

## 1-D diffusion model

## ================
## Model equations
## ================
Aphid <- function(t, APHIDS, parameters)
{
  deltax  <- c (0.5, rep(1, numboxes-1), 0.5)
  Flux    <- -D*diff(c(0, APHIDS, 0))/deltax
  dAPHIDS <- -diff(Flux)/delx + APHIDS*r

  list(dAPHIDS)   # the output
}
  
## ==================
## Model application
## ==================

## the model parameters:
D         <- 0.3    # m2/day  diffusion rate
r         <- 0.01   # /day    net growth rate
delx      <- 1      # m       thickness of boxes
numboxes  <- 60 

## distance of boxes on plant, m, 1 m intervals
Distance  <- seq(from = 0.5, by = delx, length.out = numboxes)

## Initial conditions, ind/m2
## aphids present only on two central boxes
APHIDS        <- rep(0, times = numboxes)
APHIDS[30:31] <- 1
state         <- c(APHIDS = APHIDS)      # initialise state variables 
                  
## RUNNING the model:
times <- seq(0, 200, by = 1)   # output wanted at these time intervals
out   <- ode.1D(state, times, Aphid, parms = 0, nspec = 1)

image(out, grid = Distance, main="Aphid model", ylab="distance, m")