Plot, Image and Histogram Method for deSolve Objects
Plot the output of numeric integration routines.
## 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, ...)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 R-function |
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 |
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.
Set mfrow equal to NULL to avoid the plotting function to
change user-defined 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 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 the second case, an x-y 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 x-axis.
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 x-axis.
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.
ode.2D, for an example of using subset with
arr = TRUE.
## =======================================================================
## Example 1. 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")
## Set user-defined mfrow
pm <- par (mfrow = c(2, 2))
## "mfrow=NULL" keeps user-defined 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 y-variables 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.
## =======================================================================
## 1-D 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 variablesPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.