Convert percent per year trend to cumulative trend
Value
A numeric vector of the same length as x that contains the
cumulative trend resulting from n_years years of compounding annual
trend.
Examples
ppy_trend <- runif(100, min = -100, 100)
cumulative_trend <- convert_ppy_to_cumulative(ppy_trend, n_years = 5)
cbind(ppy_trend, cumulative_trend)
#> ppy_trend cumulative_trend
#> [1,] 37.0308294 383.160512
#> [2,] 99.9613629 3096.910224
#> [3,] -60.5870956 -99.048974
#> [4,] -65.7462530 -99.528436
#> [5,] -66.6408817 -99.586883
#> [6,] 93.1104965 2585.526253
#> [7,] -27.6598451 -80.189421
#> [8,] -49.0065226 -96.551953
#> [9,] -72.5135942 -99.843112
#> [10,] -62.3086964 -99.239313
#> [11,] 67.5481140 1220.376291
#> [12,] -98.5543832 -100.000000
#> [13,] -21.6235981 -70.424874
#> [14,] 49.5139800 647.152082
#> [15,] 78.0171083 1687.757918
#> [16,] -37.4275029 -90.407817
#> [17,] -76.0853987 -99.921780
#> [18,] 16.0109404 110.133230
#> [19,] 4.9255232 27.176163
#> [20,] -31.6596431 -85.093139
#> [21,] -98.7014870 -100.000000
#> [22,] 52.0246697 712.026762
#> [23,] 23.2525141 184.432313
#> [24,] 28.6719997 252.712013
#> [25,] 82.5191530 1925.546527
#> [26,] -82.3117551 -99.982685
#> [27,] -28.0494563 -80.717187
#> [28,] -47.2478580 -95.914921
#> [29,] 18.3742505 132.426805
#> [30,] -97.3313568 -99.999999
#> [31,] 24.4785105 198.862837
#> [32,] -59.1507802 -98.862585
#> [33,] 3.2270633 17.210862
#> [34,] 88.5309670 2281.844887
#> [35,] 86.9456285 2183.371470
#> [36,] -18.6704147 -64.416981
#> [37,] -12.7653876 -49.482229
#> [38,] -70.8498831 -99.789525
#> [39,] -33.4829047 -86.978339
#> [40,] -20.7052394 -68.651089
#> [41,] -69.0053591 -99.713956
#> [42,] 92.0461348 2512.328892
#> [43,] 82.8205821 1942.327742
#> [44,] -50.1079920 -96.908602
#> [45,] -51.3973860 -97.287947
#> [46,] 82.6365235 1932.067636
#> [47,] 79.8070486 1779.462055
#> [48,] -37.4815181 -90.449148
#> [49,] 82.5406853 1926.741607
#> [50,] -39.6010438 -91.962015
#> [51,] -63.6699866 -99.367111
#> [52,] 61.6571397 1004.013670
#> [53,] -50.4581128 -97.015561
#> [54,] 75.0888617 1545.479955
#> [55,] 31.6975001 296.175538
#> [56,] -24.0338038 -74.701085
#> [57,] -81.7176180 -99.979575
#> [58,] 26.9031846 229.126312
#> [59,] -4.8496712 -22.007747
#> [60,] -53.2877808 -97.775909
#> [61,] -65.6208901 -99.519744
#> [62,] 71.7607693 1394.926645
#> [63,] -47.6182770 -96.056345
#> [64,] 64.2411353 1095.114984
#> [65,] -35.0734280 -88.462483
#> [66,] -85.2128339 -99.992930
#> [67,] 14.4770744 96.604118
#> [68,] 33.2304805 319.776353
#> [69,] 72.6926422 1435.922035
#> [70,] -91.9113623 -99.999654
#> [71,] 23.6590130 189.153784
#> [72,] -59.7943409 -98.949404
#> [73,] -77.2165910 -99.938611
#> [74,] -45.6508961 -95.257996
#> [75,] 57.0508700 855.436291
#> [76,] 27.5961604 238.211234
#> [77,] -6.0898502 -26.959680
#> [78,] 65.3054437 1134.342770
#> [79,] -1.3583505 -6.609730
#> [80,] 55.0320627 795.586683
#> [81,] 40.7493845 452.373101
#> [82,] -81.9888145 -99.981046
#> [83,] -3.7408039 -17.356034
#> [84,] -83.0425453 -99.985978
#> [85,] -65.6136450 -99.519237
#> [86,] -33.6547709 -87.145698
#> [87,] -85.5264190 -99.993648
#> [88,] 99.3374145 3047.343215
#> [89,] -73.3879390 -99.866527
#> [90,] 0.8804244 4.480322
#> [91,] -58.4314961 -98.758857
#> [92,] 98.8942169 3012.510161
#> [93,] 28.6094997 251.856229
#> [94,] 2.3137241 12.116483
#> [95,] -35.4352674 -88.780415
#> [96,] -92.2750663 -99.999725
#> [97,] -92.3839119 -99.999744
#> [98,] 19.5461488 144.161930
#> [99,] 32.5084474 308.524236
#> [100,] -75.9664418 -99.919816