# Plot methods for 'Projection' objects 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. ```r 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 ## Examples ```r ### 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") ``` ## See Also `project` `Projection-class`

Projection-plots function