Last updated on 2024-03-28 23:02:01 CET.
Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
---|---|---|---|---|---|---|
r-devel-linux-x86_64-debian-clang | 0.9-4 | 2.55 | 36.15 | 38.70 | OK | |
r-devel-linux-x86_64-debian-gcc | 0.9-4 | 2.10 | 27.82 | 29.92 | OK | |
r-devel-linux-x86_64-fedora-clang | 0.9-4 | 48.26 | OK | |||
r-devel-linux-x86_64-fedora-gcc | 0.9-4 | 59.60 | OK | |||
r-devel-windows-x86_64 | 0.9-4 | 3.00 | 204.00 | 207.00 | ERROR | |
r-patched-linux-x86_64 | 0.9-4 | 2.71 | 35.21 | 37.92 | OK | |
r-release-linux-x86_64 | 0.9-4 | 2.65 | 33.99 | 36.64 | OK | |
r-release-macos-arm64 | 0.9-4 | 25.00 | OK | |||
r-release-macos-x86_64 | 0.9-4 | 36.00 | OK | |||
r-release-windows-x86_64 | 0.9-4 | 4.00 | 59.00 | 63.00 | OK | |
r-oldrel-macos-arm64 | 0.9-4 | 20.00 | OK | |||
r-oldrel-windows-x86_64 | 0.9-4 | 7.00 | 65.00 | 72.00 | OK |
Version: 0.9-4
Check: tests
Result: ERROR
Running 'test-tsal.R' [155s]
Running the tests in 'tests/test-tsal.R' failed.
Complete output:
> library(tsallisqexp)
>
> # ?EPD
>
> #####
> # (1) density function
> x <- seq(-5, 5, length=101)
>
> cbind(x, y <- dtsal(x, 1/2, 1/4), dtsal(x, 1/2, 1/4, log=TRUE))
x
[1,] -5.0 0.00000000 -Inf
[2,] -4.9 0.00000000 -Inf
[3,] -4.8 0.00000000 -Inf
[4,] -4.7 0.00000000 -Inf
[5,] -4.6 0.00000000 -Inf
[6,] -4.5 0.00000000 -Inf
[7,] -4.4 0.00000000 -Inf
[8,] -4.3 0.00000000 -Inf
[9,] -4.2 0.00000000 -Inf
[10,] -4.1 0.00000000 -Inf
[11,] -4.0 0.00000000 -Inf
[12,] -3.9 0.00000000 -Inf
[13,] -3.8 0.00000000 -Inf
[14,] -3.7 0.00000000 -Inf
[15,] -3.6 0.00000000 -Inf
[16,] -3.5 0.00000000 -Inf
[17,] -3.4 0.00000000 -Inf
[18,] -3.3 0.00000000 -Inf
[19,] -3.2 0.00000000 -Inf
[20,] -3.1 0.00000000 -Inf
[21,] -3.0 0.00000000 -Inf
[22,] -2.9 0.00000000 -Inf
[23,] -2.8 0.00000000 -Inf
[24,] -2.7 0.00000000 -Inf
[25,] -2.6 0.00000000 -Inf
[26,] -2.5 0.00000000 -Inf
[27,] -2.4 0.00000000 -Inf
[28,] -2.3 0.00000000 -Inf
[29,] -2.2 0.00000000 -Inf
[30,] -2.1 0.00000000 -Inf
[31,] -2.0 0.00000000 -Inf
[32,] -1.9 0.00000000 -Inf
[33,] -1.8 0.00000000 -Inf
[34,] -1.7 0.00000000 -Inf
[35,] -1.6 0.00000000 -Inf
[36,] -1.5 0.00000000 -Inf
[37,] -1.4 0.00000000 -Inf
[38,] -1.3 0.00000000 -Inf
[39,] -1.2 0.00000000 -Inf
[40,] -1.1 0.00000000 -Inf
[41,] -1.0 0.00000000 -Inf
[42,] -0.9 0.00000000 -Inf
[43,] -0.8 0.00000000 -Inf
[44,] -0.7 0.00000000 -Inf
[45,] -0.6 0.00000000 -Inf
[46,] -0.5 0.00000000 -Inf
[47,] -0.4 0.00000000 -Inf
[48,] -0.3 0.00000000 -Inf
[49,] -0.2 22.36067977 3.1073040
[50,] -0.1 4.30331483 1.4593856
[51,] 0.0 2.00000000 0.6931472
[52,] 0.1 1.20736322 0.1884388
[53,] 0.2 0.82817332 -0.1885328
[54,] 0.3 0.61290897 -0.4895389
[55,] 0.4 0.47705667 -0.7401200
[56,] 0.5 0.38490018 -0.9547713
[57,] 0.6 0.31901538 -1.1425160
[58,] 0.7 0.26999430 -1.3093544
[59,] 0.8 0.23235716 -1.4594796
[60,] 0.9 0.20271844 -1.5959373
[61,] 1.0 0.17888544 -1.7210097
[62,] 1.1 0.15938203 -1.8364512
[63,] 1.2 0.14318186 -1.9436397
[64,] 1.3 0.12955150 -2.0436768
[65,] 1.4 0.11795439 -2.1374573
[66,] 1.5 0.10798985 -2.2257180
[67,] 1.6 0.09935333 -2.3090728
[68,] 1.7 0.09180960 -2.3880384
[69,] 1.8 0.08517443 -2.4630541
[70,] 1.9 0.07930167 -2.5344961
[71,] 2.0 0.07407407 -2.6026897
[72,] 2.1 0.06939660 -2.6679174
[73,] 2.2 0.06519149 -2.7304264
[74,] 2.3 0.06139454 -2.7904344
[75,] 2.4 0.05795237 -2.8481338
[76,] 2.5 0.05482024 -2.9036957
[77,] 2.6 0.05196043 -2.9572729
[78,] 2.7 0.04934089 -3.0090021
[79,] 2.8 0.04693429 -3.0590067
[80,] 2.9 0.04471716 -3.1073980
[81,] 3.0 0.04266925 -3.1542769
[82,] 3.1 0.04077301 -3.1997349
[83,] 3.2 0.03901318 -3.2438557
[84,] 3.3 0.03737640 -3.2867158
[85,] 3.4 0.03585095 -3.3283851
[86,] 3.5 0.03442652 -3.3689281
[87,] 3.6 0.03309397 -3.4084041
[88,] 3.7 0.03184523 -3.4468677
[89,] 3.8 0.03067309 -3.4843697
[90,] 3.9 0.02957113 -3.5209569
[91,] 4.0 0.02853360 -3.5566728
[92,] 4.1 0.02755536 -3.5915581
[93,] 4.2 0.02663177 -3.6256505
[94,] 4.3 0.02575864 -3.6589852
[95,] 4.4 0.02493220 -3.6915952
[96,] 4.5 0.02414902 -3.7235113
[97,] 4.6 0.02340601 -3.7547624
[98,] 4.7 0.02270033 -3.7853757
[99,] 4.8 0.02202941 -3.8153767
[100,] 4.9 0.02139091 -3.8447894
[101,] 5.0 0.02078266 -3.8736365
> # plot(x, y, type="l")
> cbind(x, y <- dtsal.tail(x, 1/2, 1/4, xmin=3), dtsal.tail(x, 1/2, 1/4, log=TRUE, xmin=3))
x
[1,] -5.0 0.00000000 -Inf
[2,] -4.9 0.00000000 -Inf
[3,] -4.8 0.00000000 -Inf
[4,] -4.7 0.00000000 -Inf
[5,] -4.6 0.00000000 -Inf
[6,] -4.5 0.00000000 -Inf
[7,] -4.4 0.00000000 -Inf
[8,] -4.3 0.00000000 -Inf
[9,] -4.2 0.00000000 -Inf
[10,] -4.1 0.00000000 -Inf
[11,] -4.0 0.00000000 -Inf
[12,] -3.9 0.00000000 -Inf
[13,] -3.8 0.00000000 -Inf
[14,] -3.7 0.00000000 -Inf
[15,] -3.6 0.00000000 -Inf
[16,] -3.5 0.00000000 -Inf
[17,] -3.4 0.00000000 -Inf
[18,] -3.3 0.00000000 -Inf
[19,] -3.2 0.00000000 -Inf
[20,] -3.1 0.00000000 -Inf
[21,] -3.0 0.00000000 -Inf
[22,] -2.9 0.00000000 -Inf
[23,] -2.8 0.00000000 -Inf
[24,] -2.7 0.00000000 -Inf
[25,] -2.6 0.00000000 -Inf
[26,] -2.5 0.00000000 -Inf
[27,] -2.4 0.00000000 -Inf
[28,] -2.3 0.00000000 -Inf
[29,] -2.2 0.00000000 -Inf
[30,] -2.1 0.00000000 -Inf
[31,] -2.0 0.00000000 -Inf
[32,] -1.9 0.00000000 -Inf
[33,] -1.8 0.00000000 -Inf
[34,] -1.7 0.00000000 -Inf
[35,] -1.6 0.00000000 -Inf
[36,] -1.5 0.00000000 -Inf
[37,] -1.4 0.00000000 -Inf
[38,] -1.3 0.00000000 -Inf
[39,] -1.2 0.00000000 -Inf
[40,] -1.1 0.00000000 -Inf
[41,] -1.0 0.00000000 -Inf
[42,] -0.9 0.00000000 -Inf
[43,] -0.8 0.00000000 -Inf
[44,] -0.7 0.00000000 -Inf
[45,] -0.6 0.00000000 -Inf
[46,] -0.5 0.00000000 -Inf
[47,] -0.4 0.00000000 -Inf
[48,] -0.3 0.00000000 -Inf
[49,] -0.2 0.00000000 -Inf
[50,] -0.1 0.00000000 -Inf
[51,] 0.0 0.00000000 -Inf
[52,] 0.1 0.00000000 -Inf
[53,] 0.2 0.00000000 -Inf
[54,] 0.3 0.00000000 -Inf
[55,] 0.4 0.00000000 -Inf
[56,] 0.5 0.00000000 -Inf
[57,] 0.6 0.00000000 -Inf
[58,] 0.7 0.00000000 -Inf
[59,] 0.8 0.00000000 -Inf
[60,] 0.9 0.00000000 -Inf
[61,] 1.0 0.00000000 -Inf
[62,] 1.1 0.00000000 -Inf
[63,] 1.2 0.00000000 -Inf
[64,] 1.3 0.00000000 -Inf
[65,] 1.4 0.00000000 -Inf
[66,] 1.5 0.00000000 -Inf
[67,] 1.6 0.00000000 -Inf
[68,] 1.7 0.00000000 -Inf
[69,] 1.8 0.00000000 -Inf
[70,] 1.9 0.00000000 -Inf
[71,] 2.0 0.00000000 -Inf
[72,] 2.1 0.00000000 -Inf
[73,] 2.2 0.00000000 -Inf
[74,] 2.3 0.00000000 -Inf
[75,] 2.4 0.00000000 -Inf
[76,] 2.5 0.00000000 -Inf
[77,] 2.6 0.00000000 -Inf
[78,] 2.7 0.00000000 -Inf
[79,] 2.8 0.00000000 -Inf
[80,] 2.9 0.00000000 -Inf
[81,] 3.0 0.15384615 -1.871802
[82,] 3.1 0.14700919 -1.917260
[83,] 3.2 0.14066403 -1.961381
[84,] 3.3 0.13476253 -2.004241
[85,] 3.4 0.12926245 -2.045910
[86,] 3.5 0.12412658 -2.086453
[87,] 3.6 0.11932202 -2.125929
[88,] 3.7 0.11481960 -2.164393
[89,] 3.8 0.11059338 -2.201895
[90,] 3.9 0.10662021 -2.238482
[91,] 4.0 0.10287937 -2.274198
[92,] 4.1 0.09935227 -2.309083
[93,] 4.2 0.09602220 -2.343176
[94,] 4.3 0.09287409 -2.376511
[95,] 4.4 0.08989432 -2.409121
[96,] 4.5 0.08707055 -2.441037
[97,] 4.6 0.08439157 -2.472288
[98,] 4.7 0.08184721 -2.502901
[99,] 4.8 0.07942818 -2.532902
[100,] 4.9 0.07712601 -2.562315
[101,] 5.0 0.07493293 -2.591162
>
> #####
> # (2) distribution function
>
> ptsal(x, 1/2, 1/4)
[1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[8] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[15] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[22] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[29] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[36] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[43] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
[50] 0.0000000 0.0000000 0.1548457 0.2546440 0.3258001 0.3798263 0.4226497
[57] 0.4576739 0.4870108 0.5120500 0.5337476 0.5527864 0.5696685 0.5847726
[64] 0.5983903 0.6107505 0.6220355 0.6323927 0.6419426 0.6507849 0.6590028
[71] 0.6666667 0.6738360 0.6805617 0.6868879 0.6928524 0.6984887 0.7038256
[78] 0.7088887 0.7137008 0.7182819 0.7226499 0.7268208 0.7308090 0.7346276
[85] 0.7382880 0.7418011 0.7451764 0.7484227 0.7515480 0.7545597 0.7574644
[92] 0.7602683 0.7629773 0.7655964 0.7681306 0.7705843 0.7729617 0.7752667
[99] 0.7775029 0.7796737 0.7817821
> ptsal(x, 1/2, 1/4, lower=FALSE)
[1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[22] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[50] 1.0000000 1.0000000 0.8451543 0.7453560 0.6741999 0.6201737 0.5773503
[57] 0.5423261 0.5129892 0.4879500 0.4662524 0.4472136 0.4303315 0.4152274
[64] 0.4016097 0.3892495 0.3779645 0.3676073 0.3580574 0.3492151 0.3409972
[71] 0.3333333 0.3261640 0.3194383 0.3131121 0.3071476 0.3015113 0.2961744
[78] 0.2911113 0.2862992 0.2817181 0.2773501 0.2731792 0.2691910 0.2653724
[85] 0.2617120 0.2581989 0.2548236 0.2515773 0.2484520 0.2454403 0.2425356
[92] 0.2397317 0.2370227 0.2344036 0.2318694 0.2294157 0.2270383 0.2247333
[99] 0.2224971 0.2203263 0.2182179
> ptsal(x, 1/2, 1/4, log=TRUE)
[1] NaN NaN NaN NaN NaN NaN
[7] NaN NaN NaN NaN NaN NaN
[13] NaN NaN NaN NaN NaN NaN
[19] NaN NaN NaN NaN NaN NaN
[25] NaN NaN NaN NaN NaN NaN
[31] NaN NaN NaN NaN NaN NaN
[37] NaN NaN NaN NaN NaN NaN
[43] NaN NaN NaN NaN NaN NaN
[49] NaN NaN -Inf -1.8653258 -1.3678888 -1.1214712
[55] -0.9680412 -0.8612115 -0.7815985 -0.7194689 -0.6693331 -0.6278322
[61] -0.5927836 -0.5627006 -0.5365322 -0.5135120 -0.4930667 -0.4747581
[67] -0.4582447 -0.4432564 -0.4295762 -0.4170274 -0.4054651 -0.3947686
[73] -0.3848368 -0.3755842 -0.3669382 -0.3588363 -0.3512247 -0.3440567
[79] -0.3372914 -0.3308931 -0.3248304 -0.3190753 -0.3136031 -0.3083916
[85] -0.3034212 -0.2986741 -0.2941343 -0.2897874 -0.2856202 -0.2816209
[91] -0.2777788 -0.2740838 -0.2705270 -0.2671002 -0.2637956 -0.2606063
[97] -0.2575258 -0.2545482 -0.2516679 -0.2488798 -0.2461792
Warning message:
In log(1 - (z^(-shape))) : NaNs produced
>
> ptsal(x, q=1/2, kappa=4)
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[19] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[31] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[37] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[43] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[49] 0.00000000 0.00000000 0.00000000 0.02484375 0.04937500 0.07359375
[55] 0.09750000 0.12109375 0.14437500 0.16734375 0.19000000 0.21234375
[61] 0.23437500 0.25609375 0.27750000 0.29859375 0.31937500 0.33984375
[67] 0.36000000 0.37984375 0.39937500 0.41859375 0.43750000 0.45609375
[73] 0.47437500 0.49234375 0.51000000 0.52734375 0.54437500 0.56109375
[79] 0.57750000 0.59359375 0.60937500 0.62484375 0.64000000 0.65484375
[85] 0.66937500 0.68359375 0.69750000 0.71109375 0.72437500 0.73734375
[91] 0.75000000 0.76234375 0.77437500 0.78609375 0.79750000 0.80859375
[97] 0.81937500 0.82984375 0.84000000 0.84984375 0.85937500
>
> ptsal.tail(x, 1/2, 1/4, xmin=3)
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[19] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[31] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[37] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[43] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[49] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[55] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[61] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[67] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[73] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[79] 0.00000000 0.00000000 0.00000000 0.01503845 0.02941822 0.04318604
[85] 0.05638410 0.06905066 0.08122046 0.09292514 0.10419358 0.11505224
[91] 0.12552537 0.13563524 0.14540239 0.15484575 0.16398282 0.17282981
[97] 0.18140175 0.18971261 0.19777537 0.20560214 0.21320421
> ptsal.tail(x, 1/2, 1/4, xmin=3, log=TRUE)
[1] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[8] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[15] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[22] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[29] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[36] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[43] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[50] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[57] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[64] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[71] -Inf -Inf -Inf -Inf -Inf -Inf -Inf
[78] -Inf -Inf -Inf -Inf -4.197145 -3.526141 -3.142238
[85] -2.875568 -2.672915 -2.510588 -2.375961 -2.261505 -2.162369 -2.075247
[92] -1.997786 -1.928250 -1.865326 -1.807994 -1.755448 -1.707041 -1.662245
[99] -1.620623 -1.581812 -1.545505
Warning message:
In log(1 - (C * z^(-shape))) : NaNs produced
> ptsal.tail(x, 1/2, 1/4, xmin=3, lower=FALSE)
[1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[22] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[50] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[57] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[64] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[71] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[78] 1.0000000 1.0000000 1.0000000 1.0000000 0.9849615 0.9705818 0.9568140
[85] 0.9436159 0.9309493 0.9187795 0.9070749 0.8958064 0.8849478 0.8744746
[92] 0.8643648 0.8545976 0.8451543 0.8360172 0.8271702 0.8185982 0.8102874
[99] 0.8022246 0.7943979 0.7867958
> ptsal.tail(x, 1/2, 1/4, xmin=3, lower=FALSE, log=TRUE)
[1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[19] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[31] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[37] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[43] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[49] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[55] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[61] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[67] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[73] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
[79] 0.00000000 0.00000000 0.00000000 -0.01515267 -0.02985962 -0.04414630
[85] -0.05803609 -0.07155042 -0.08470908 -0.09753029 -0.11003094 -0.12222667
[91] -0.13413199 -0.14576042 -0.15712455 -0.16823612 -0.17910611 -0.18974481
[97] -0.20016185 -0.21036629 -0.22036662 -0.23017086 -0.23978654
>
>
>
> #####
> # (3) quantile function
>
> qtsal(0:10/10, 3, 2)
[1] 0.00000000 0.07148834 0.15443469 0.25249576 0.37126220 0.51984210
[7] 0.71441762 0.98760316 1.41995189 2.30886938 Inf
> qtsal(log(0:10/10), 3, 2, log=TRUE)
[1] 0.00000000 0.07148834 0.15443469 0.25249576 0.37126220 0.51984210
[7] 0.71441762 0.98760316 1.41995189 2.30886938 Inf
>
> qtsal.tail(0:10/10, 3, 2, xmin=3)
[1] 3.000000 3.178721 3.386087 3.631239 3.928156 4.299605 4.786044 5.469008
[9] 6.549880 8.772173 Inf
> qtsal.tail(log(0:10/10), 3, 2, xmin=3, log=TRUE)
[1] 3.000000 3.178721 3.386087 3.631239 3.928156 4.299605 4.786044 5.469008
[9] 6.549880 8.772173 Inf
>
>
> #####
> # (4) random generation function
>
> rtsal(10, 3, 2)
[1] 0.50672430 0.17306287 0.68673217 0.05193965 0.45937181 0.19526519
[7] 0.78724509 0.11115452 0.53350668 0.82837696
> rtsal.tail(10, 3, 2, xmin=3)
[1] 7.976940 3.784501 8.181562 4.555309 11.355400 6.107752 7.317455
[8] 3.917363 4.117301 3.036674
>
> #####
> # (5) fit function
>
> set.seed(1234)
> x <- rtsal(10, 3, 2)
>
> tsal.fit(x, method="mle.equation")
$type
[1] "tsal"
$q
[1] 0.6145008
$kappa
[1] 0.9282705
$shape
[1] -2.594039
$scale
[1] -2.40797
$loglik
[1] -5.400687
$n
[1] 10
$xmin
[1] 0
$method
[1] "mle.equation"
> tsal.fit(x, method="mle.direct")
$type
[1] "tsal"
$q
[1] 0.6145008
$kappa
[1] 0.9282705
$shape
[1] -2.594039
$scale
[1] -2.40797
$loglik
[1] -5.400687
$n
[1] 10
$xmin
[1] 0
$method
[1] "mle.direct"
> tsal.fit(x, method="leastsquares")
$type
[1] "tsal"
$q
[1] 1
$kappa
[1] 0.6644425
$shape
[1] 11748774
$scale
[1] 7806385
$loglik
[1] -5.911932
$n
[1] 10
$xmin
[1] 0
$method
[1] "leastsquares"
>
>
>
> #####
> # (6) boot functions
>
> # ?tsal.boot
>
> tsal.bootstrap.errors(dist=NULL, reps=100, confidence=0.95, n=10)
$originals
shape scale q kappa
1 1 2 1
$bias
shape scale q kappa
-0.6808935 0.9614131 -0.3320685 1.0020521
$se
shape scale q kappa
11.792101 14.756864 1.345451 3.448985
$confidence.interval.lower
shape scale q kappa
-10.2051863 -15.6167973 -3.3790572 0.3052557
$confidence.interval.upper
shape scale q kappa
8.156044 8.542180 3.246330 13.988616
$sample.size
[1] 10
$bootrap.replicates
[1] 100
$confidence
[1] 0.95
$method
[1] "mle.equation"
$xmin
[1] 0
Warning message:
In log(shape/scale) : NaNs produced
>
> tsal.bootstrap.errors(dist=tsal.fit(x, method="mle.equation"), reps=100)
$originals
shape scale q kappa
-2.5940391 -2.4079699 0.6145008 0.9282705
$bias
shape scale q kappa
2.640742 1.321381 -Inf Inf
$se
shape scale q kappa
9.171589 4.546734 NaN NaN
$confidence.interval.lower
shape scale q kappa
-9.5448472 -6.1911554 -Inf 0.4337565
$confidence.interval.upper
shape scale q kappa
8.454331 3.906426 1.139538 Inf
$sample.size
[1] 10
$bootrap.replicates
[1] 100
$confidence
[1] 0.95
$method
[1] "mle.equation"
$xmin
[1] 0
There were 15 warnings (use warnings() to see them)
>
> tsal.total.magnitude(dist=NULL, n=10)
$magnitude.est
[1] Inf
$count.est
[1] 10
>
> tsal.total.magnitude(dist=tsal.fit(x, method="mle.equation"))
$magnitude.est
[1] 6.699899
$count.est
[1] 10
>
>
> #####
> # (7) test functions
>
> # ?tsal.test
>
> test.tsal.quantile.transform(from=0, to=1e6, shape=1, scale=1,
+ n=1e5, lwd=0.01, xmin=0)
>
> test.tsal.LR.distribution(n=10, reps=100, shape=2, scale=3/2,
+ xmin=0,method="mle.equation")
Exact one-sample Kolmogorov-Smirnov test
data: LR2
D = 0.15949, p-value = 0.01147
alternative hypothesis: two-sided
Warning message:
In log(shape/scale) : NaNs produced
> test.tsal.LR.distribution(n=1000, reps=100, shape=2, scale=3/2,
+ xmin=0,method="mle.equation")
Asymptotic one-sample Kolmogorov-Smirnov test
data: LR2
D = 0.094052, p-value = 0.3393
alternative hypothesis: two-sided
>
> proc.time()
user system elapsed
2.75 0.10 2.84
Flavor: r-devel-windows-x86_64