Iterate a population model

Iterate a population model forward in time from an initial population vector. Parameters of the model can vary with time.

iterate(parms = NULL, N0 = NULL, popfun = NULL)

Arguments

parms	a data frame containing the parameters of the model. Must have one row for each time step.
N0	a numeric vector of the initial population size. If named, these names will be used in the returned tibble.
popfun	a function that steps the model by one time step. Must take at least N0 as an argument.

Value

A data frame containing the input parameters and a column of projected population size. The return value will have the same class as parms.

Details

Will fail if any of the three input arguments is NULL. If `parms` only has a single row, just column binds the initial population size. If `parms` has zero rows, the result also has zero rows.

Examples

# a simple exponential growth model
anexppop <- function(N0, b, d) {
  N1 <- N0 * (1 + b - d)
  return(N1)
}
# make the input dataframe
inputs <- data.frame(Year = 2018:2025, b = 0.2, d = 0.15)
iterate(inputs, 23, anexppop)
#>   Year   b    d        N
#> 1 2018 0.2 0.15 23.00000
#> 2 2019 0.2 0.15 24.15000
#> 3 2020 0.2 0.15 25.35750
#> 4 2021 0.2 0.15 26.62538
#> 5 2022 0.2 0.15 27.95664
#> 6 2023 0.2 0.15 29.35448
#> 7 2024 0.2 0.15 30.82220
#> 8 2025 0.2 0.15 32.36331
# time dependent model
inputs <- data.frame(Year = 2018:2025, b = seq(0.2, 0.1, length = 8), d = 0.15)
iterate(inputs, 23, anexppop)
#>   Year         b    d        N
#> 1 2018 0.2000000 0.15 23.00000
#> 2 2019 0.1857143 0.15 24.15000
#> 3 2020 0.1714286 0.15 25.01250
#> 4 2021 0.1571429 0.15 25.54848
#> 5 2022 0.1428571 0.15 25.73097
#> 6 2023 0.1285714 0.15 25.54718
#> 7 2024 0.1142857 0.15 24.99974
#> 8 2025 0.1000000 0.15 24.10689