aae.pop (version 0.2.0)

risk_curve: Calculate (quasi-)extinction risk at multiple thresholds for a simulate object

Description

Calculate (quasi-)extinction risk at multiple thresholds for a simulate object

Usage

risk_curve(sims, threshold = NULL, subset = NULL, times = NULL, n = 100)

Value

a named vector containing the threshold values (names) and the probability the population will fall below these threshold values

Arguments

sims

an object returned from simulate

threshold

integer or numeric vector denoting the set of threshold population sizes used to define the risk curve. Defaults to n evenly spaced values from 0 to the maximum observed abundance

subset

integer vector denoting the population classes to include in calculation of population abundance. Defaults to all classes

times

integer vector specifying generations to include in calculation of extinction risk. Defaults to all simulated generations

n

integer specifying number of threshold values to use in default case when threshold is not specified. Defaults to 100

Details

Risk curves represent pr_extinct at multiple threshold population sizes simultaneously. This gives an expression of risk of population declines below a range of values. Risk curves are extracted from a simulate object as the proportion of replicate trajectories that fall below each threshold value at any time step within a set period. Abundances can be specified for all population classes or for a subset of classes.

The get_cdf function is a much faster way to generate risk curves for almost all use cases. The exception is when the threshold argument is used to specify threshold values that are not evenly spaced.

Examples

# define a basic population
nstage <- 5
popmat <- matrix(0, nrow = nstage, ncol = nstage)
popmat[reproduction(popmat, dims = 4:5)] <- c(10, 20)
popmat[transition(popmat)] <- c(0.25, 0.3, 0.5, 0.65)

# define a dynamics object
dyn <- dynamics(popmat)

# simulate with the default updater
sims <- simulate(dyn, nsim = 100)

# calculate risk curve
risk_curve(sims, n = 10)