Run logistic growth model

Run logistic growth model

Usage

run_logistic_model(
  time = 0:10,
  init = c(N1 = 1),
  params = c(r = 0.15, K = 1000)
)

Arguments

time

vector of time units over which to run model, starting from 0. time can also be supplied as just the total length of the simulation (i.e. tmax)

init

initial population size of population, in a vector with name N1

params

vector of intrinsic growth rate, carrying capacity and, if simulating a lagged-logistic model, tau in a vector with names r, K, and tau (if applicable)

See also

run_exponential_model() for simulating the dynamics of a population with no carrying capacity, and run_discrete_logistic_model(), run_beverton_holt_model(), and run_ricker_model() for discrete-time models of population growth with population regulation

Examples

run_logistic_model(time = 0:10, init = c(N1 = 1), params = c(r = .15, K = 1000))
#>    time       N1
#> 1     0 1.000000
#> 2     1 1.161646
#> 3     2 1.349388
#> 4     3 1.567423
#> 5     4 1.820624
#> 6     5 2.114641
#> 7     6 2.456023
#> 8     7 2.852358
#> 9     8 3.312437
#> 10    9 3.846442
#> 11   10 4.466148
run_logistic_model(time = 10, init = c(N1 = 1), params = c(r = .15, K = 1000))
#>     time       N1
#> 1    0.0 1.000000
#> 2    0.1 1.015098
#> 3    0.2 1.030424
#> 4    0.3 1.045981
#> 5    0.4 1.061772
#> 6    0.5 1.077801
#> 7    0.6 1.094072
#> 8    0.7 1.110588
#> 9    0.8 1.127354
#> 10   0.9 1.144372
#> 11   1.0 1.161647
#> 12   1.1 1.179182
#> 13   1.2 1.196982
#> 14   1.3 1.215050
#> 15   1.4 1.233391
#> 16   1.5 1.252008
#> 17   1.6 1.270905
#> 18   1.7 1.290088
#> 19   1.8 1.309559
#> 20   1.9 1.329325
#> 21   2.0 1.349388
#> 22   2.1 1.369753
#> 23   2.2 1.390426
#> 24   2.3 1.411409
#> 25   2.4 1.432710
#> 26   2.5 1.454331
#> 27   2.6 1.476278
#> 28   2.7 1.498555
#> 29   2.8 1.521169
#> 30   2.9 1.544123
#> 31   3.0 1.567423
#> 32   3.1 1.591073
#> 33   3.2 1.615081
#> 34   3.3 1.639449
#> 35   3.4 1.664185
#> 36   3.5 1.689294
#> 37   3.6 1.714780
#> 38   3.7 1.740651
#> 39   3.8 1.766911
#> 40   3.9 1.793566
#> 41   4.0 1.820623
#> 42   4.1 1.848088
#> 43   4.2 1.875966
#> 44   4.3 1.904263
#> 45   4.4 1.932987
#> 46   4.5 1.962143
#> 47   4.6 1.991738
#> 48   4.7 2.021778
#> 49   4.8 2.052271
#> 50   4.9 2.083222
#> 51   5.0 2.114639
#> 52   5.1 2.146530
#> 53   5.2 2.178899
#> 54   5.3 2.211757
#> 55   5.4 2.245108
#> 56   5.5 2.278961
#> 57   5.6 2.313323
#> 58   5.7 2.348203
#> 59   5.8 2.383607
#> 60   5.9 2.419543
#> 61   6.0 2.456020
#> 62   6.1 2.493045
#> 63   6.2 2.530628
#> 64   6.3 2.568775
#> 65   6.4 2.607496
#> 66   6.5 2.646799
#> 67   6.6 2.686693
#> 68   6.7 2.727186
#> 69   6.8 2.768288
#> 70   6.9 2.810008
#> 71   7.0 2.852354
#> 72   7.1 2.895337
#> 73   7.2 2.938966
#> 74   7.3 2.983251
#> 75   7.4 3.028200
#> 76   7.5 3.073825
#> 77   7.6 3.120135
#> 78   7.7 3.167140
#> 79   7.8 3.214852
#> 80   7.9 3.263279
#> 81   8.0 3.312434
#> 82   8.1 3.362327
#> 83   8.2 3.412968
#> 84   8.3 3.464370
#> 85   8.4 3.516543
#> 86   8.5 3.569499
#> 87   8.6 3.623250
#> 88   8.7 3.677807
#> 89   8.8 3.733182
#> 90   8.9 3.789388
#> 91   9.0 3.846437
#> 92   9.1 3.904342
#> 93   9.2 3.963115
#> 94   9.3 4.022768
#> 95   9.4 4.083317
#> 96   9.5 4.144772
#> 97   9.6 4.207149
#> 98   9.7 4.270460
#> 99   9.8 4.334720
#> 100  9.9 4.399943
#> 101 10.0 4.466143
run_logistic_model(time = 0:10, init = c(N1 = 1), params = c(r = .15, K = 1000, tau = 2.1))
#>    time       N1
#> 1     0 1.000000
#> 2     1 1.161646
#> 3     2 1.349387
#> 4     3 1.567494
#> 5     4 1.820831
#> 6     5 2.115049
#> 7     6 2.456722
#> 8     7 2.853476
#> 9     8 3.314148
#> 10    9 3.848983
#> 11   10 4.469847