Projection-plots {popdemo}

Plot methods for 'Projection' objects

Description

This page describes print and plot methods for Projection-class. Example code is below, or worked examples using these methods are available in the "Deterministic population dynamics" and "Stochastic population dynamics" vignettes.

Usage

plot(x, y, ...)

## S4 method for signature 'Projection,missing'
plot(
  x,
  y,
  bounds = FALSE,
  bounds.args = NULL,
  labs = TRUE,
  plottype = "lines",
  ybreaks = 20,
  shadelevels = 100,
  ...
)

Arguments

x

an object of class "Projection" generated using project

y

not used

...

arguments to be passed to methods: see par and plot.

bounds

logical: indicates whether to plot the bounds on population density.

bounds.args

A list of graphical parameters for plotting the bounds if bounds=T. The name of each list element indicates the name of the argument. Could include, e.g. list(lwd=2,lty=3,col="darkred").

labs

logical: if TRUE, the plot includes more than one projection and plottype="lines", then lines are automatically labelled according to the names contained in the 'projection' object.

plottype

for projections generated from dirichlet draws (see project), plottype has two options. "lines" will plot each projection as a separate line. "shady" will plot shaded contours showing the probabilities of population densities over over time, calculated across the set of projections from dirichlet draws. By default this shaded plot is a gradient of black to white (with black representing higher probabilities), but this can be overridden by using the 'col' argument (see examples).

ybreaks

if plottype="shady", gives the number of breaks on the y axis for generating the grid for the shade plot. A larger number of breaks means a finer resolution grid for the shading.

shadelevels

if plottype="shady" and a palette of colours is not specified using 'col', then shadelevels gives the number of colour/shading levels to use when generating the black and white shade plot. A larger number of levels means a finer resolution on the shade plot of population density (see examples).

Details

plot

plot a Projection object

See Also

project Projection-class

Examples

  ### Desert tortoise matrix
  data(Tort)

  # Create an initial stage structure
  Tortvec1 <- c(8, 7, 6, 5, 4, 3, 2, 1)
  
  # Create a projection over 30 time intervals
  ( Tortp1 <- project(Tort, vector = Tortvec1, time = 10) )

  # plot p1
  plot(Tortp1)
  plot(Tortp1, bounds = TRUE) #with bounds
 
  # new display parameters
  plot(Tortp1, bounds = TRUE, col = "red", bty = "n", log = "y", 
       ylab = "Number of individuals (log scale)",
       bounds.args = list(lty = 2, lwd = 2) )

  # multiple vectors
  Tortvec2 <- cbind(Tortvec1, c(1, 2, 3, 4, 5, 6, 7, 8))
  plot(project(Tort, vector = Tortvec2), log = "y")
  plot(project(Tort, vector = Tortvec2), log = "y", labs = FALSE) #no labels

  # dirichlet distribution 
  # darker shading indicates more likely population size
  Tortshade <- project(Tort, time = 30, vector = "diri", standard.A = TRUE,
               draws = 500, alpha.draws = "unif")
  plot(Tortshade, plottype = "shady", bounds = TRUE)
  
  ### STOCHASTIC PROJECTIONS
  # load polar bear data
  data(Pbear)
  
  # project over 50 years with uniform matrix selection
  Pbearvec <- c(0.106, 0.068, 0.106, 0.461, 0.151, 0.108)
  p2 <- project(Pbear, Pbearvec, time = 50, Aseq = "unif")

  # stochastic projection information
  Aseq(p2)
  projtype(p2)
  nmat(p2)
  
  # plot
  plot(p2, log = "y")
  
  ### USING PROJECTION OBJECTS

  # Create a 3x3 PPM
  ( A <- matrix(c(0,1,2,0.5,0.1,0,0,0.6,0.6), byrow=TRUE, ncol=3) )

  # Project stage-biased dynamics of A over 70 intervals
  ( pr <- project(A, vector="n", time=70) )
  plot(pr)

  # Access other slots
  vec(pr)  #time sequence of population vectors
  bounds(pr)  #bounds on population dynamics
  mat(pr)  #matrix used to create projection
  Aseq(pr)  #sequence of matrices (more useful for stochastic projections)
  projtype(pr)  #type of projection
  vectype(pr)  #type of vector(s) initiating projection

  # Extra information on the projection
  nproj(pr)  #number of projections
  nmat(pr)  #number of matrices (more usefulk for stochastic projections)
  ntime(pr)  #number of time intervals
  
  # Select the projection of stage 2 bias
  pr[,2]

  # Project stage-biased dynamics of standardised A over 30 intervals
  ( pr2 <- project(A, vector="n", time=30, standard.A=TRUE) )
  plot(pr2)

  #Select the projection of stage 2 bias
  pr2[,2]

  # Select the density of stage 3 in bias 2 at time 10
  vec(pr2)[11,3,2]

  # Select the time series of densities of stage 2 in bias 1
  vec(pr2)[,2,1]

  #Select the matrix of population vectors for bias 2
  vec(pr2)[,,2]

  # Create an initial stage structure
  ( initial <- c(1,3,2) )

  # Project A over 50 intervals using a specified population structure
  ( pr3 <- project(A, vector=initial, time=50) )
  plot(pr3)

  # Project standardised dynamics of A over 10 intervals using 
  # standardised initial structure and return demographic vectors
  ( pr4 <- project(A, vector=initial, time=10, standard.vec=TRUE, 
                   standard.A=TRUE, return.vec=TRUE) )
  plot(pr4)

  # Select the time series for stage 1
  vec(pr4)[,1]

  ### DETERMINISTIC PROJECTIONS

  # Load the desert Tortoise matrix
  data(Tort)

  # Create an initial stage structure
  Tortvec1 <- c(8, 7, 6, 5, 4, 3, 2, 1)
  
  # Create a projection over 30 time intervals
  ( Tortp1 <- project(Tort, vector = Tortvec1, time = 10) )

  # plot p1
  plot(Tortp1)
  plot(Tortp1, bounds = TRUE) #with bounds
 
  # new display parameters
  plot(Tortp1, bounds = TRUE, col = "red", bty = "n", log = "y", 
       ylab = "Number of individuals (log scale)",
       bounds.args = list(lty = 2, lwd = 2) )

  # multiple vectors
  Tortvec2 <- cbind(Tortvec1, c(1, 2, 3, 4, 5, 6, 7, 8))
  plot(project(Tort, vector = Tortvec2), log = "y")
  plot(project(Tort, vector = Tortvec2), log = "y", labs = FALSE) #no labels

  # dirichlet distribution 
  # darker shading indicates more likely population size
  Tortshade <- project(Tort, time = 30, vector = "diri", standard.A = TRUE,
               draws = 500, alpha.draws = "unif")
  plot(Tortshade, plottype = "shady", bounds = TRUE)
  
  ### STOCHASTIC PROJECTIONS
  # load polar bear data
  data(Pbear)
  
  # project over 50 years with uniform matrix selection
  Pbearvec <- c(0.106, 0.068, 0.106, 0.461, 0.151, 0.108)
  p2 <- project(Pbear, Pbearvec, time = 50, Aseq = "unif")

  # stochastic projection information
  Aseq(p2)
  projtype(p2)
  nmat(p2)
  
  # plot
  plot(p2, log = "y")