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Calculate expected population size for a simulate object based on generic functions (ExPS)

Source: R/summarise.R
exps.Rd

Calculate expected population size for a simulate object based on generic functions (ExPS)

Usage

exps(
  sims,
  subset = NULL,
  times = NULL,
  fun_within = mean,
  fun_among = mean,
  ...
)

Arguments

sims

an object returned from simulate

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

fun_within

function used to summarise a single trajectory. Must return a single value. Defaults to mean

fun_among

function used to summarise over all replicate trajectories. Defaults to mean. Alternatives might include median or min

...

additional arguments passed to fun_within and fun_among. If these conflict, a wrapper function could be used to define expected arguments for each function

Value

a single value representing the expected statistic applied to the population sizes generated with simulate

Details

Expected population size (ExPS) is a highly flexible generalisation of emps and represents a two-level summary that first summarises individual population trajectories and then summarises these values over all replicates. Abundances can be specified for all population classes or for a subset of classes.

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 = 1000)

# calculate expected population size
exps(sims)
#> [1] 96.072

# calculate expected population size for 4 and 5 year
#   olds only
exps(sims, subset = 4:5)
#> [1] 4.860821

# calculate expected population size but ignore first 10 years
exps(sims, times = 11:51)
#> [1] 79.90515

# calculate expected population size based on median
exps(sims, fun_among = median)
#> [1] 95.3747

# calculate expected maximum population size based on median
exps(sims, fun_within = max, fun_among = median)
#> [1] 322.7

# calculate exps with conflicting quantile functions, handling
#   conflicting arguments with wrapper functions
quant1 <- function(x, p1, ...) {
  quantile(x, prob = p1)
}
quant2 <- function(x, p2, ...) {
  quantile(x, prob = p2)
}
exps(
  sims,
  fun_within = quant1, fun_among = quant2, p1 = 0.25, p2 = 0.75
)
#>     75% 
#> 60.7253