plot.deSolve {deSolve}  R Documentation 
Plot, Image and Histogram Method for deSolve Objects
Description
Plot the output of numeric integration routines.
Usage
## S3 method for class 'deSolve'
plot(x, ..., select = NULL, which = select, ask = NULL,
obs = NULL, obspar = list(), subset = NULL)
## S3 method for class 'deSolve'
hist(x, select = 1:(ncol(x)1), which = select, ask = NULL,
subset = NULL, ...)
## S3 method for class 'deSolve'
image(x, select = NULL, which = select, ask = NULL,
add.contour = FALSE, grid = NULL,
method = "image", legend = FALSE, subset = NULL, ...)
## S3 method for class 'deSolve'
subset(x, subset = NULL, select = NULL,
which = select, arr = FALSE, ...)
plot.1D (x, ..., select = NULL, which = select, ask = NULL,
obs = NULL, obspar = list(), grid = NULL,
xyswap = FALSE, delay = 0, vertical = FALSE, subset = NULL)
matplot.0D(x, ..., select = NULL, which = select,
obs = NULL, obspar = list(), subset = NULL,
legend = list(x = "topright"))
matplot.1D(x, select = NULL, which = select, ask = NULL,
obs = NULL, obspar = list(), grid = NULL,
xyswap = FALSE, vertical = FALSE, subset = NULL, ...)
Arguments
x 
an object of class For 
which 
the name(s) or the index to the variables that should be
plotted or selected. Default = all variables, except 
select 
which variable/columns to be selected. This is added for
consistency with the Rfunction 
subset 
either a logical expression indicating elements or rows to keep in

ask 
logical; if 
add.contour 
if 
method 
the name of the plotting method to use, one of "image", "filled.contour", "persp", "contour". 
grid 
only for 
xyswap 
if 
vertical 
if 
delay 
adds a delay (in milliseconds) between consecutive plots
of 
obs 
a By default the first column of an observed data set should contain
the If the first column of If 
obspar 
additional graphics arguments passed to 
legend 
if 
arr 
if 
... 
additional arguments. The graphical arguments are passed to
For For 
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 userdefined settings for mfrow
or mfcol
.
Set mfrow
equal to NULL
to avoid the plotting function to
change userdefined mfrow
or mfcol
settings.
Other graphical parameters can be passed as well. Parameters are
vectorized, either according to the number of plots (xlab
,
ylab
, main
, sub
, xlim
, ylim
,
log
, asp
, ann
, axes
, frame.plot
,
panel.first
, panel.last
, cex.lab
,
cex.axis
, cex.main
) or according to the number of lines
within one plot (other parameters e.g. col
, lty
,
lwd
etc.) so it is possible to assign specific axis labels to
individual plots, resp. different plotting style. Plotting parameter
ylim
, or xlim
can also be a list to assign different
axis limits to individual plots.
Similarly, the graphical parameters for observed data, as passed by
obspar
can be vectorized, according to the number of observed
data sets.
Image plots will only work for 1D and 2D variables, as solved with
ode.1D
and ode.2D
. In the first case, an
image with times
as x and the grid
as yaxis will be
created. In the second case, an xy plot will be created, for all
times. Unless ask = FALSE
, the user will be asked to confirm
page changes. Via argument mtext
, it is possible to label each
page in case of 2D output.
For images, it is possible to pass an argument
method
which can take the values "image" (default),
"filled.contour", "contour" or "persp", in order to use the respective
plotting method.
plot
and matplot.0D
will always have times
on the xaxis.
For problems solved with ode.1D
, it may be more useful to use
plot.1D
or matplot.1D
which will plot how spatial variables change with time. These plots will
have the grid
on the xaxis.
Value
Function subset
called with arr = FALSE
will return a
matrix with up to as many rows as selected by subset
and as
many columns as selected variables.
When arr = TRUE
then an array will be outputted with dimensions
equal to the dimension of the selected variable, augmented with the number
of rows selected by subset
. This means that the last dimension points
to times
.
Function subset
also has an attribute that contains the times
selected.
See Also
hist
image
matplot
,
plot.default
for the underlying functions from package graphics,
ode.2D
, for an example of using subset
with
arr = TRUE
.
Examples
## =======================================================================
## Example 1. A PredatorPrey 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")
## Userspecified axis labels
plot(out, type = "l", ylab = c("Prey 1", "Prey 2", "Pred 1", "Pred 2"),
xlab = "Time (d)", main = "Time Series")
## Set userdefined mfrow
pm < par (mfrow = c(2, 2))
## "mfrow=NULL" keeps userdefined mfrow
plot(out, which = c("prey1", "pred2"), mfrow = NULL, type = "l", lwd = 2)
plot(out[,"prey1"], out[,"pred1"], xlab="prey1",
ylab = "pred1", type = "l", lwd = 2)
plot(out[,"prey2"], out[,"pred2"], xlab = "prey2",
ylab = "pred2", type = "l",lwd = 2)
## restore graphics parameters
par ("mfrow" = pm)
## Plot all in one figure, using matplot
matplot.0D(out, lwd = 2)
## Split yvariables in two groups
matplot.0D(out, which = list(c(1,3), c(2,4)),
lty = c(1,2,1,2), col=c(4,4,5,5),
ylab = c("prey1,pred1", "prey2,pred2"))
## =======================================================================
## Example 2. Add second and third output, and observations
## =======================================================================
# New runs with different parameter settings
parms2 < parms
parms2$r[1] < 0.2
out2 < ode(y, times, LVmatrix, parms2)
# New runs with different parameter settings
parms3 < parms
parms3$r[1] < 0.05
out3 < ode(y, times, LVmatrix, parms3)
# plot all three outputs
plot(out, out2, out3, type = "l",
ylab = c("Prey 1", "Prey 2", "Pred 1", "Pred 2"),
xlab = "Time (d)", main = c("Prey 1", "Prey 2", "Pred 1", "Pred 2"),
col = c("red", "blue", "darkred"))
## 'observed' data
obs < as.data.frame(out[out[,1] %in% seq(10, 500, by = 30), ])
plot(out, which = "prey1", type = "l", obs = obs,
obspar = list(pch = 18, cex = 2))
plot(out, type = "l", obs = obs, col = "red")
matplot.0D(out, which = c("prey1", "pred1"), type = "l", obs = obs)
## second set of 'observed' data and two outputs
obs2 < as.data.frame(out2[out2[,1] %in% seq(10, 500, by = 50), ])
## manual xlim, log
plot(out, out2, type = "l", obs = list(obs, obs2), col = c("red", "blue"),
obspar = list(pch = 18:19, cex = 2, col = c("red", "blue")),
log = c("y", ""), which = c("prey1", "prey1"),
xlim = list(c(100, 500), c(0, 400)))
## data in 'long' format
OBS < data.frame(name = c(rep("prey1", 3), rep("prey2", 2)),
time = c(10, 100, 250, 10, 400),
value = c(0.05, 0.04, 0.7, 0.5, 1))
OBS
plot(out, obs = OBS, obspar = c(pch = 18, cex = 2))
# a subset only:
plot(out, subset = prey1 < 0.5, type = "p")
# Simple histogram
hist(out, col = "darkblue", breaks = 50)
hist(out, col = "darkblue", breaks = 50, subset = prey1<1 & prey2 < 1)
# different parameters per plot
hist(out, col = c("darkblue", "red", "orange", "black"),
breaks = c(10,50))
## =======================================================================
## The Aphid model from Soetaert and Herman, 2009.
## A practical guide to ecological modelling.
## Using R as a simulation platform. Springer.
## =======================================================================
## 1D diffusion model
## ================
## Model equations
## ================
Aphid < function(t, APHIDS, parameters) {
deltax < c (0.5*delx, rep(delx, numboxes  1), 0.5*delx)
Flux < D * diff(c(0, APHIDS, 0))/deltax
dAPHIDS < diff(Flux)/delx + APHIDS * r
list(dAPHIDS, Flux = Flux)
}
## ==================
## 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, names = "Aphid")
image(out, grid = Distance, main = "Aphid model", ylab = "distance, m",
legend = TRUE)
## restricting time
image(out, grid = Distance, main = "Aphid model", ylab = "distance, m",
legend = TRUE, subset = time < 100)
image(out, grid = Distance, main = "Aphid model", ylab = "distance, m",
method = "persp", border = NA, theta = 30)
FluxAphid < subset(out, select = "Flux", subset = time < 50)
matplot.1D(out, type = "l", lwd = 2, xyswap = TRUE, lty = 1)
matplot.1D(out, type = "l", lwd = 2, xyswap = TRUE, lty = 1,
subset = time < 50)
matplot.1D(out, type = "l", lwd = 2, xyswap = TRUE, lty = 1,
subset = time %in% seq(0, 200, by = 10), col = "grey")
## Not run:
plot(out, ask = FALSE, mfrow = c(1, 1))
plot.1D(out, ask = FALSE, type = "l", lwd = 2, xyswap = TRUE)
## End(Not run)
## see help file for ode.2D for images of 2D variables