sim_kulan_data.RdSimulate a dataset
sim_kulan_data(n = 100, ma = 25, p0 = 0.5, ml = 20, w = 0.2)
| n | number of transects |
|---|---|
| ma | mean number of animals counted where they are counted |
| p0 | proportion of the transects that will be empty |
| ml | mean length of the transects |
| w | width of the transects (assuming a constant width) |
simulated data in a dataframe
sim_kulan_data(n=100,ma=25, p0=0.5, ml=20, w=0.2)#> sp_count Trans_length Trans_area sp_area #> 1 24 24 4.8 5.000000 #> 2 30 18 3.6 8.333333 #> 3 0 19 3.8 0.000000 #> 4 21 20 4.0 5.250000 #> 5 0 20 4.0 0.000000 #> 6 20 15 3.0 6.666667 #> 7 29 20 4.0 7.250000 #> 8 21 17 3.4 6.176471 #> 9 27 23 4.6 5.869565 #> 10 21 13 2.6 8.076923 #> 11 26 17 3.4 7.647059 #> 12 0 23 4.6 0.000000 #> 13 27 13 2.6 10.384615 #> 14 25 17 3.4 7.352941 #> 15 0 16 3.2 0.000000 #> 16 0 17 3.4 0.000000 #> 17 0 28 5.6 0.000000 #> 18 26 10 2.0 13.000000 #> 19 0 13 2.6 0.000000 #> 20 0 24 4.8 0.000000 #> 21 35 21 4.2 8.333333 #> 22 21 25 5.0 4.200000 #> 23 0 18 3.6 0.000000 #> 24 18 16 3.2 5.625000 #> 25 0 17 3.4 0.000000 #> 26 32 22 4.4 7.272727 #> 27 0 15 3.0 0.000000 #> 28 0 16 3.2 0.000000 #> 29 31 19 3.8 8.157895 #> 30 33 14 2.8 11.785714 #> 31 24 26 5.2 4.615385 #> 32 29 20 4.0 7.250000 #> 33 34 13 2.6 13.076923 #> 34 0 17 3.4 0.000000 #> 35 0 27 5.4 0.000000 #> 36 22 12 2.4 9.166667 #> 37 37 21 4.2 8.809524 #> 38 0 20 4.0 0.000000 #> 39 28 10 2.0 14.000000 #> 40 18 29 5.8 3.103448 #> 41 28 18 3.6 7.777778 #> 42 16 13 2.6 6.153846 #> 43 28 29 5.8 4.827586 #> 44 28 23 4.6 6.086957 #> 45 0 23 4.6 0.000000 #> 46 0 18 3.6 0.000000 #> 47 0 24 4.8 0.000000 #> 48 31 15 3.0 10.333333 #> 49 0 11 2.2 0.000000 #> 50 28 21 4.2 6.666667 #> 51 32 25 5.0 6.400000 #> 52 0 15 3.0 0.000000 #> 53 19 24 4.8 3.958333 #> 54 0 22 4.4 0.000000 #> 55 22 9 1.8 12.222222 #> 56 24 19 3.8 6.315789 #> 57 28 15 3.0 9.333333 #> 58 0 20 4.0 0.000000 #> 59 29 24 4.8 6.041667 #> 60 27 23 4.6 5.869565 #> 61 24 15 3.0 8.000000 #> 62 0 22 4.4 0.000000 #> 63 0 29 5.8 0.000000 #> 64 25 18 3.6 6.944444 #> 65 30 15 3.0 10.000000 #> 66 0 24 4.8 0.000000 #> 67 0 16 3.2 0.000000 #> 68 0 18 3.6 0.000000 #> 69 0 22 4.4 0.000000 #> 70 0 28 5.6 0.000000 #> 71 0 28 5.6 0.000000 #> 72 0 12 2.4 0.000000 #> 73 17 21 4.2 4.047619 #> 74 27 21 4.2 6.428571 #> 75 0 21 4.2 0.000000 #> 76 0 21 4.2 0.000000 #> 77 0 19 3.8 0.000000 #> 78 0 22 4.4 0.000000 #> 79 0 22 4.4 0.000000 #> 80 0 19 3.8 0.000000 #> 81 38 19 3.8 10.000000 #> 82 31 13 2.6 11.923077 #> 83 0 21 4.2 0.000000 #> 84 0 26 5.2 0.000000 #> 85 0 20 4.0 0.000000 #> 86 0 29 5.8 0.000000 #> 87 26 13 2.6 10.000000 #> 88 0 17 3.4 0.000000 #> 89 0 21 4.2 0.000000 #> 90 31 22 4.4 7.045455 #> 91 24 24 4.8 5.000000 #> 92 13 11 2.2 5.909091 #> 93 17 21 4.2 4.047619 #> 94 0 26 5.2 0.000000 #> 95 24 19 3.8 6.315789 #> 96 33 23 4.6 7.173913 #> 97 23 20 4.0 5.750000 #> 98 26 20 4.0 6.500000 #> 99 0 16 3.2 0.000000 #> 100 29 18 3.6 8.055556