simulate objectR/summarise.R
get_cdf.RdCalculate the cumulative distribution function
of a summary statistic across all iterations of a
simulate object
get_cdf(sims, subset = NULL, times = NULL, n = 100, fn = min, ...)an object returned from simulate
integer vector denoting the population classes
to include in calculation of population abundance. Defaults to
all classes
integer vector specifying generations to
include in calculation of extinction risk. Defaults to all
simulated generations
integer specifying number of threshold values
to use in default case when threshold is not specified.
Defaults to 100
function to apply to each iteration. Defaults to min
additional arguments passed to fn
get_cdf is a faster and more
general alternative to the risk_curve function.
get_cdf can be used to calculate the cumulative
distribution of any summary statistic. For example, the
cumulative distribution of the minimum population size
is equivalent to a risk curve. Summary statistics for
get_cdf are extracted from a simulate
object and represent the cumulative distribution of that
statistic over all replicate trajectories at any time step
within a set period. Abundances can be specified for all
population classes or for a subset of classes.
# 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 distribution of minimum population sizes (default)
get_cdf(sims)
#> prob value
#> 1 0.00 16.57627
#> 2 0.01 22.76214
#> 3 0.02 23.53699
#> 4 0.03 24.70575
#> 5 0.04 25.44184
#> 6 0.05 26.25621
#> 7 0.06 26.80685
#> 8 0.07 27.21967
#> 9 0.08 27.63838
#> 10 0.09 28.01883
#> 11 0.10 28.21132
#> 12 0.11 28.52113
#> 13 0.12 28.80478
#> 14 0.13 29.01816
#> 15 0.14 29.27747
#> 16 0.15 29.49167
#> 17 0.16 29.75466
#> 18 0.17 29.93450
#> 19 0.18 30.17543
#> 20 0.19 30.36014
#> 21 0.20 30.52386
#> 22 0.21 30.73327
#> 23 0.22 30.94783
#> 24 0.23 31.21974
#> 25 0.24 31.38598
#> 26 0.25 31.62794
#> 27 0.26 31.87343
#> 28 0.27 32.04050
#> 29 0.28 32.11920
#> 30 0.29 32.33426
#> 31 0.30 32.45162
#> 32 0.31 32.67995
#> 33 0.32 32.78106
#> 34 0.33 32.95388
#> 35 0.34 33.09542
#> 36 0.35 33.23107
#> 37 0.36 33.41848
#> 38 0.37 33.62761
#> 39 0.38 33.81373
#> 40 0.39 34.00703
#> 41 0.40 34.10330
#> 42 0.41 34.29785
#> 43 0.42 34.44988
#> 44 0.43 34.63895
#> 45 0.44 34.76482
#> 46 0.45 34.91542
#> 47 0.46 35.00000
#> 48 0.47 35.04430
#> 49 0.48 35.14422
#> 50 0.49 35.34647
#> 51 0.50 35.52478
#> 52 0.51 35.65412
#> 53 0.52 35.80061
#> 54 0.53 35.93372
#> 55 0.54 36.05007
#> 56 0.55 36.20076
#> 57 0.56 36.40055
#> 58 0.57 36.60039
#> 59 0.58 36.80431
#> 60 0.59 37.01074
#> 61 0.60 37.17714
#> 62 0.61 37.29078
#> 63 0.62 37.57234
#> 64 0.63 37.78301
#> 65 0.64 37.92597
#> 66 0.65 38.02424
#> 67 0.66 38.18515
#> 68 0.67 38.31945
#> 69 0.68 38.48259
#> 70 0.69 38.57785
#> 71 0.70 38.71078
#> 72 0.71 38.96248
#> 73 0.72 39.02102
#> 74 0.73 39.23670
#> 75 0.74 39.42163
#> 76 0.75 39.58612
#> 77 0.76 39.85937
#> 78 0.77 40.00568
#> 79 0.78 40.28316
#> 80 0.79 40.43740
#> 81 0.80 40.60948
#> 82 0.81 40.75497
#> 83 0.82 41.00000
#> 84 0.83 41.09616
#> 85 0.84 41.34031
#> 86 0.85 41.55361
#> 87 0.86 41.80701
#> 88 0.87 41.95882
#> 89 0.88 42.32450
#> 90 0.89 42.87538
#> 91 0.90 43.30201
#> 92 0.91 43.78990
#> 93 0.92 44.00104
#> 94 0.93 44.19003
#> 95 0.94 44.75662
#> 96 0.95 45.43513
#> 97 0.96 46.00255
#> 98 0.97 47.17404
#> 99 0.98 48.00000
#> 100 0.99 48.65680
#> 101 1.00 54.00000
# calculate distribution of maximum population sizes
get_cdf(sims, fn = max)
#> prob value
#> 1 0.00 120.2500
#> 2 0.01 173.6498
#> 3 0.02 189.7890
#> 4 0.03 195.5790
#> 5 0.04 204.2440
#> 6 0.05 209.2612
#> 7 0.06 215.2085
#> 8 0.07 224.6230
#> 9 0.08 226.1500
#> 10 0.09 230.8210
#> 11 0.10 233.3450
#> 12 0.11 235.3445
#> 13 0.12 236.9900
#> 14 0.13 241.5545
#> 15 0.14 244.9650
#> 16 0.15 246.2638
#> 17 0.16 250.2910
#> 18 0.17 254.1330
#> 19 0.18 256.0615
#> 20 0.19 258.1000
#> 21 0.20 261.8350
#> 22 0.21 265.1290
#> 23 0.22 266.5280
#> 24 0.23 267.6097
#> 25 0.24 271.7460
#> 26 0.25 272.5250
#> 27 0.26 274.6720
#> 28 0.27 276.0865
#> 29 0.28 276.8000
#> 30 0.29 277.7033
#> 31 0.30 281.8325
#> 32 0.31 283.3345
#> 33 0.32 284.4040
#> 34 0.33 285.4675
#> 35 0.34 286.7490
#> 36 0.35 287.8000
#> 37 0.36 290.7840
#> 38 0.37 293.7760
#> 39 0.38 295.3500
#> 40 0.39 296.4550
#> 41 0.40 297.7800
#> 42 0.41 298.8885
#> 43 0.42 302.6190
#> 44 0.43 303.9070
#> 45 0.44 305.3500
#> 46 0.45 306.5000
#> 47 0.46 307.3080
#> 48 0.47 309.4858
#> 49 0.48 313.4680
#> 50 0.49 314.4510
#> 51 0.50 315.2750
#> 52 0.51 316.9980
#> 53 0.52 317.9980
#> 54 0.53 319.1350
#> 55 0.54 323.6075
#> 56 0.55 325.3000
#> 57 0.56 326.4660
#> 58 0.57 327.6715
#> 59 0.58 328.7565
#> 60 0.59 330.3870
#> 61 0.60 335.2350
#> 62 0.61 336.4585
#> 63 0.62 337.6570
#> 64 0.63 338.6035
#> 65 0.64 339.9860
#> 66 0.65 343.0150
#> 67 0.66 344.8010
#> 68 0.67 348.2000
#> 69 0.68 349.7960
#> 70 0.69 353.5155
#> 71 0.70 355.4000
#> 72 0.71 356.6660
#> 73 0.72 358.1640
#> 74 0.73 359.2445
#> 75 0.74 362.5910
#> 76 0.75 364.8813
#> 77 0.76 366.0600
#> 78 0.77 367.7615
#> 79 0.78 369.2500
#> 80 0.79 371.1710
#> 81 0.80 374.1000
#> 82 0.81 376.6285
#> 83 0.82 377.9500
#> 84 0.83 381.6350
#> 85 0.84 387.2400
#> 86 0.85 389.0575
#> 87 0.86 394.6920
#> 88 0.87 398.0260
#> 89 0.88 404.5620
#> 90 0.89 407.8885
#> 91 0.90 412.2600
#> 92 0.91 416.2000
#> 93 0.92 418.4040
#> 94 0.93 422.8560
#> 95 0.94 428.2140
#> 96 0.95 438.5600
#> 97 0.96 447.4720
#> 98 0.97 456.1255
#> 99 0.98 461.0510
#> 100 0.99 487.4690
#> 101 1.00 593.1500
# calculate distribution of the 90th percentile of
# population sizes
get_cdf(sims, fn = quantile, prob = 0.9)
#> prob value
#> 1 0.00 76.98023
#> 2 0.01 102.49990
#> 3 0.02 109.84399
#> 4 0.03 114.02712
#> 5 0.04 118.38532
#> 6 0.05 122.02219
#> 7 0.06 123.52929
#> 8 0.07 125.63030
#> 9 0.08 126.64000
#> 10 0.09 129.37145
#> 11 0.10 131.01680
#> 12 0.11 132.17844
#> 13 0.12 133.31749
#> 14 0.13 135.12942
#> 15 0.14 136.48753
#> 16 0.15 137.89821
#> 17 0.16 139.01827
#> 18 0.17 140.45710
#> 19 0.18 141.38502
#> 20 0.19 142.26075
#> 21 0.20 143.13278
#> 22 0.21 143.59812
#> 23 0.22 144.45184
#> 24 0.23 145.22565
#> 25 0.24 146.05740
#> 26 0.25 146.72162
#> 27 0.26 147.15972
#> 28 0.27 147.95998
#> 29 0.28 148.64738
#> 30 0.29 149.34416
#> 31 0.30 150.44060
#> 32 0.31 151.20954
#> 33 0.32 152.78267
#> 34 0.33 153.74902
#> 35 0.34 154.58036
#> 36 0.35 155.11948
#> 37 0.36 156.27845
#> 38 0.37 157.06037
#> 39 0.38 157.55732
#> 40 0.39 158.03724
#> 41 0.40 159.20053
#> 42 0.41 159.93501
#> 43 0.42 160.53031
#> 44 0.43 161.31254
#> 45 0.44 161.92491
#> 46 0.45 162.64418
#> 47 0.46 163.28153
#> 48 0.47 163.94852
#> 49 0.48 164.24063
#> 50 0.49 164.90263
#> 51 0.50 165.47777
#> 52 0.51 166.08699
#> 53 0.52 166.86590
#> 54 0.53 168.41017
#> 55 0.54 169.52043
#> 56 0.55 170.28281
#> 57 0.56 171.35084
#> 58 0.57 172.00788
#> 59 0.58 173.01916
#> 60 0.59 173.68737
#> 61 0.60 174.60170
#> 62 0.61 175.60320
#> 63 0.62 176.20011
#> 64 0.63 177.46607
#> 65 0.64 178.22055
#> 66 0.65 179.31283
#> 67 0.66 179.81465
#> 68 0.67 180.46916
#> 69 0.68 181.40481
#> 70 0.69 182.66460
#> 71 0.70 183.85959
#> 72 0.71 184.45540
#> 73 0.72 185.07142
#> 74 0.73 186.19832
#> 75 0.74 186.65070
#> 76 0.75 187.13092
#> 77 0.76 187.86243
#> 78 0.77 189.12423
#> 79 0.78 190.17429
#> 80 0.79 191.24000
#> 81 0.80 191.97720
#> 82 0.81 193.35646
#> 83 0.82 194.09119
#> 84 0.83 195.35651
#> 85 0.84 196.70510
#> 86 0.85 197.75283
#> 87 0.86 199.02672
#> 88 0.87 200.35235
#> 89 0.88 201.52974
#> 90 0.89 202.76375
#> 91 0.90 204.45930
#> 92 0.91 207.19144
#> 93 0.92 210.20370
#> 94 0.93 212.15702
#> 95 0.94 216.70593
#> 96 0.95 220.67927
#> 97 0.96 223.86260
#> 98 0.97 227.07772
#> 99 0.98 233.16718
#> 100 0.99 240.15184
#> 101 1.00 298.02023
# calculate distribution of minimum population sizes
# but ignore first 10 years
get_cdf(sims, fn = max, times = 11:51)
#> prob value
#> 1 0.00 76.98023
#> 2 0.01 110.76862
#> 3 0.02 121.18732
#> 4 0.03 125.23802
#> 5 0.04 132.00945
#> 6 0.05 135.67082
#> 7 0.06 137.54360
#> 8 0.07 139.57583
#> 9 0.08 142.16263
#> 10 0.09 143.35866
#> 11 0.10 145.29109
#> 12 0.11 147.10626
#> 13 0.12 149.31802
#> 14 0.13 150.62007
#> 15 0.14 151.72098
#> 16 0.15 152.79263
#> 17 0.16 155.34258
#> 18 0.17 157.23199
#> 19 0.18 158.00231
#> 20 0.19 159.99816
#> 21 0.20 160.90076
#> 22 0.21 161.62467
#> 23 0.22 162.89199
#> 24 0.23 163.69255
#> 25 0.24 164.64765
#> 26 0.25 165.43308
#> 27 0.26 166.27613
#> 28 0.27 167.06386
#> 29 0.28 168.00876
#> 30 0.29 169.29768
#> 31 0.30 170.08799
#> 32 0.31 171.70649
#> 33 0.32 172.69784
#> 34 0.33 173.96883
#> 35 0.34 175.84834
#> 36 0.35 176.84356
#> 37 0.36 177.67196
#> 38 0.37 179.26154
#> 39 0.38 180.59065
#> 40 0.39 181.19841
#> 41 0.40 181.66738
#> 42 0.41 182.43189
#> 43 0.42 183.65343
#> 44 0.43 184.36777
#> 45 0.44 185.06306
#> 46 0.45 186.44617
#> 47 0.46 187.05054
#> 48 0.47 188.30648
#> 49 0.48 189.12173
#> 50 0.49 190.02921
#> 51 0.50 190.59325
#> 52 0.51 191.22220
#> 53 0.52 191.70171
#> 54 0.53 192.66907
#> 55 0.54 193.78567
#> 56 0.55 194.35298
#> 57 0.56 196.18237
#> 58 0.57 197.43916
#> 59 0.58 197.99194
#> 60 0.59 199.02538
#> 61 0.60 200.57673
#> 62 0.61 201.49634
#> 63 0.62 202.05108
#> 64 0.63 203.03897
#> 65 0.64 204.01362
#> 66 0.65 204.57874
#> 67 0.66 205.75188
#> 68 0.67 206.58421
#> 69 0.68 207.25865
#> 70 0.69 208.29693
#> 71 0.70 209.22692
#> 72 0.71 210.91813
#> 73 0.72 212.33493
#> 74 0.73 213.18168
#> 75 0.74 213.84866
#> 76 0.75 215.06762
#> 77 0.76 216.10726
#> 78 0.77 217.19395
#> 79 0.78 217.95693
#> 80 0.79 219.02752
#> 81 0.80 220.70312
#> 82 0.81 222.72153
#> 83 0.82 224.34445
#> 84 0.83 225.88376
#> 85 0.84 227.21535
#> 86 0.85 228.71166
#> 87 0.86 230.62884
#> 88 0.87 232.17310
#> 89 0.88 233.92418
#> 90 0.89 235.85527
#> 91 0.90 237.22303
#> 92 0.91 239.40521
#> 93 0.92 242.17571
#> 94 0.93 245.47500
#> 95 0.94 248.91567
#> 96 0.95 253.61895
#> 97 0.96 259.76756
#> 98 0.97 264.40355
#> 99 0.98 270.32208
#> 100 0.99 281.66846
#> 101 1.00 355.75692