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)
<!-- %% thpe: since 1.14 not anymore exported -->
<!-- %\method{matplot}{deSolve}(x, \dots, select = NULL, which = select, -->
<!-- %              obs = NULL, obspar = list(), subset = NULL, -->
<!-- %              legend = list(x = "topright")) -->
# 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 deSolve, as returned by the integrators, and to be plotted.

For plot.deSolve, it is allowed to pass several objects of class deSolve after x (unnamed) - see second example.

which

the name(s) or the index to the variables that should be plotted or selected. Default = all variables, except time. For use with matplot.0D and matplot.1D, which or select can be a list, with vectors, each referring to a separate y-axis.

select

which variable/columns to be selected. This is added for consistency with the R-function subset.

subset

either a logical expression indicating elements or rows to keep in select, or a vector of integers denoting the indices of the elements over which to loop. Missing values are taken as FALSE

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 par(ask) and dev.interactive.

add.contour

if TRUE, will add contours to the image plot.

method

the name of the plotting method to use, one of "image", "filled.contour", "persp", "contour".

grid

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

xyswap

if TRUE, then x-and y-values are swapped and the y-axis is from top to bottom. Useful for drawing vertical profiles.

vertical

if TRUE, then 1. x-and y-values are swapped, the y-axis is from top to bottom, the x-axis is on top, margin 3 and the main title gets the value of the x-axis. Useful for drawing vertical profiles; see example 2.

delay

adds a delay (in milliseconds) between consecutive plots of plot.1D to enable animations.

obs

a data.frame or matrix with "observed data" that will be added as points to the plots. obs can also be a list with multiple data.frames and/or matrices containing observed data.

By default the first column of an observed data set should contain the time-variable. The other columns contain the observed values and they should have names that are known in x.

If the first column of obs consists of factors or characters (strings), then it is assumed that the data are presented in long (database) format, where the first three columns contain (name, time, value).

If obs is not NULL and which is NULL, then the variables, common to both obs and x will be plotted.

obspar

additional graphics arguments passed to points, for plotting the observed data. If obs is a list containing multiple observed data sets, then the graphics arguments can be a vector or a list (e.g. for xlim, ylim), specifying each data set separately.

legend

if TRUE, a color legend will be drawn on the right of each image. For use with matplot.0D and matplot.1D: a list with arguments passed to R-function legend.

arr

if TRUE, and the output is from a 2-D or 3-D model, an array will be returned with dimension = c(dimension of selected variable, nrow(x)). When arr=TRUE then only one variable can be selected. When the output is from a 0-D or 1-D model, then this argument is ignored.

...

additional arguments.

The graphical arguments are passed to plot.default, image or hist

For plot.deSolve, and plot.1D, the dots may contain other objects of class deSolve, as returned by the integrators, and to be plotted on the same graphs as x - see second example. In this case, x and and these other objects should be compatible, i.e. the column names should be the same.

For plot.deSolve, the arguments after ... must be matched exactly.

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.

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. 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.

Examples

## =======================================================================
## 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
#>    name time value
#> 1 prey1   10  0.05
#> 2 prey1  100  0.04
#> 3 prey1  250  0.70
#> 4 prey2   10  0.50
#> 5 prey2  400  1.00
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")


if (FALSE) { # \dontrun{
  plot(out, ask = FALSE, mfrow = c(1, 1))
  plot.1D(out, ask = FALSE, type = "l", lwd = 2, xyswap = TRUE)
} # }

## see help file for ode.2D for images of 2D variables

Plot, Image and Histogram Method for deSolve Objects

Arguments

Value

Details

See also

Examples