Run exponential growth model

Run exponential growth model

Usage

run_exponential_model(time = 10, init = c(N1 = 1), params = c(r = 0.1))

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

intrinsic growth rate r, in a vector with name r

See also

run_logistic_model() for simulating the dynamics of a population with logistic growth to a carrying capacity, and run_discrete_exponential_model() for a simulating exponential growth in discrete time

Examples

run_exponential_model(time = 0:10, init = c(N1 = 1), params = c(r = .1))
#>    time       N1
#> 1     0 1.000000
#> 2     1 1.105171
#> 3     2 1.221403
#> 4     3 1.349861
#> 5     4 1.491827
#> 6     5 1.648723
#> 7     6 1.822121
#> 8     7 2.013755
#> 9     8 2.225544
#> 10    9 2.459607
#> 11   10 2.718286
run_exponential_model(time = 10, init = c(N1 = 1), params = c(r = .1))
#>     time       N1
#> 1    0.0 1.000000
#> 2    0.1 1.010050
#> 3    0.2 1.020202
#> 4    0.3 1.030455
#> 5    0.4 1.040811
#> 6    0.5 1.051271
#> 7    0.6 1.061837
#> 8    0.7 1.072508
#> 9    0.8 1.083287
#> 10   0.9 1.094175
#> 11   1.0 1.105171
#> 12   1.1 1.116278
#> 13   1.2 1.127497
#> 14   1.3 1.138829
#> 15   1.4 1.150274
#> 16   1.5 1.161834
#> 17   1.6 1.173511
#> 18   1.7 1.185305
#> 19   1.8 1.197218
#> 20   1.9 1.209250
#> 21   2.0 1.221403
#> 22   2.1 1.233678
#> 23   2.2 1.246077
#> 24   2.3 1.258600
#> 25   2.4 1.271249
#> 26   2.5 1.284026
#> 27   2.6 1.296930
#> 28   2.7 1.309965
#> 29   2.8 1.323130
#> 30   2.9 1.336428
#> 31   3.0 1.349859
#> 32   3.1 1.363425
#> 33   3.2 1.377128
#> 34   3.3 1.390968
#> 35   3.4 1.404948
#> 36   3.5 1.419068
#> 37   3.6 1.433330
#> 38   3.7 1.447735
#> 39   3.8 1.462285
#> 40   3.9 1.476981
#> 41   4.0 1.491825
#> 42   4.1 1.506818
#> 43   4.2 1.521962
#> 44   4.3 1.537258
#> 45   4.4 1.552708
#> 46   4.5 1.568313
#> 47   4.6 1.584074
#> 48   4.7 1.599995
#> 49   4.8 1.616075
#> 50   4.9 1.632317
#> 51   5.0 1.648722
#> 52   5.1 1.665292
#> 53   5.2 1.682028
#> 54   5.3 1.698933
#> 55   5.4 1.716007
#> 56   5.5 1.733253
#> 57   5.6 1.750673
#> 58   5.7 1.768267
#> 59   5.8 1.786039
#> 60   5.9 1.803989
#> 61   6.0 1.822119
#> 62   6.1 1.840432
#> 63   6.2 1.858928
#> 64   6.3 1.877611
#> 65   6.4 1.896481
#> 66   6.5 1.915541
#> 67   6.6 1.934793
#> 68   6.7 1.954238
#> 69   6.8 1.973878
#> 70   6.9 1.993716
#> 71   7.0 2.013753
#> 72   7.1 2.033992
#> 73   7.2 2.054434
#> 74   7.3 2.075081
#> 75   7.4 2.095936
#> 76   7.5 2.117000
#> 77   7.6 2.138277
#> 78   7.7 2.159767
#> 79   7.8 2.181473
#> 80   7.9 2.203397
#> 81   8.0 2.225541
#> 82   8.1 2.247908
#> 83   8.2 2.270500
#> 84   8.3 2.293319
#> 85   8.4 2.316367
#> 86   8.5 2.339647
#> 87   8.6 2.363161
#> 88   8.7 2.386911
#> 89   8.8 2.410900
#> 90   8.9 2.435130
#> 91   9.0 2.459604
#> 92   9.1 2.484323
#> 93   9.2 2.509291
#> 94   9.3 2.534510
#> 95   9.4 2.559982
#> 96   9.5 2.585710
#> 97   9.6 2.611697
#> 98   9.7 2.637945
#> 99   9.8 2.664457
#> 100  9.9 2.691235
#> 101 10.0 2.718282