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Webapp for finding out how large your penis really is
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library(mvtnorm)
# Girth and Length are now set by values determined in either:
# Hungfun's charts (http://imgur.com/a/3r5sH)
# Herbenick et al (http://onlinelibrary.wiley.com/doi/10.1111/jsm.12244/abstract)
percentile = function (l_mean, l_sd, g_mean, g_sd, correlation) {
covariance = correlation * l_sd * g_sd # correlation coefficient * sd(length) * sd(girth)
sigma = matrix(c(l_sd^2, covariance, covariance, g_sd^2),nrow = 2, byrow=T)
grange = seq(2,8, by=.1)
lrange = seq(2,10, by=.1)
result = c()
for(l in lrange) {
for (g in grange) {
p = 1-pmvnorm(c(l,g), mean=c(l_mean, g_mean), sigma=sigma)[1]
result = cbind(result, c(l, g, p))
}
}
result = t(result)
colnames(result) = c("length", "girth", "percentile")
result
}
vol_sim = function(l_mean, l_sd, g_mean, g_sd, correlation, n=1000000) {
covariance = correlation * l_sd * g_sd # correlation coefficient * sd(length) * sd(girth)
sigma = matrix(c(l_sd^2, covariance, covariance, g_sd^2),nrow = 2, byrow=T)
# simulate volume
sim = rmvnorm(n, mean=c(l_mean, g_mean), sigma=sigma)
vol = sim[,1] * (sim[,2]/2/pi)^2*pi
vol_cdf = ecdf(vol)
vol_range = seq(0,35,by=0.01)
vol_quant = vol_cdf(vol_range)
vol_mat = cbind(vol_range, vol_quant)
colnames(vol_mat) = c("volume", "percentile")
vol_mat
}
hungfun = percentile (5.76, 0.83, 4.67, 0.54, .46)
write.csv(hungfun, "hungfun.csv", row.names = F)
herbenick = percentile(5.57, 1.04, 4.81, 0.88, .46)
write.csv(herbenick, "herbenick.csv", row.names = F)
hungfun_vol = vol_sim(5.76, 0.83, 4.67, 0.54, .46)
write.csv(hungfun_vol, "hungfun_vol.csv", row.names=FALSE)
herbenick_vol = vol_sim(5.57, 1.04, 4.81, 0.88, .46)
write.csv(herbenick_vol, "herbenick_vol.csv", row.names=FALSE)
length girth percentile
2 2 0.000988247214201543
2 2.1 0.00131710929337192
2 2.2 0.00178442675681245
2 2.3 0.00243982933835662
2 2.4 0.00334705031454485
2 2.5 0.00458652252706937
2 2.6 0.00625795331753487
2 2.7 0.0084826775486333
2 2.8 0.0114055351951707
2 2.9 0.0151959750221835
2 3 0.0200480565653749
2 3.1 0.0261790175209994
2 3.2 0.0338261008096689
2 3.3 0.043241401307439
2 3.4 0.0546845996580672
2 3.5 0.0684135983030968
2 3.6 0.0846732560185607
2 3.7 0.103682619102868
2 3.8 0.125621251786494
2 3.9 0.150615453259676
2 4 0.178725289996118
2 4.1 0.209933447034524
2 4.2 0.244136892299969
2 4.3 0.281142243283821
2 4.4 0.320665524961185
2 4.5 0.36233672265674
2 4.6 0.405709185855598
2 4.7 0.450273560251153
2 4.8 0.495475553177655
2 4.9 0.540736511049253
2 5 0.585475542108519
2 5.1 0.62913178107816
2 5.2 0.671185379699432
2 5.3 0.711175920070354
2 5.4 0.748717173356786
2 5.5 0.783507439548463
2 5.6 0.815335070146496
2 5.7 0.84407915622001
2 5.8 0.869705720911306
2 5.9 0.892260055131242
2 6 0.911856053618308
2 6.1 0.92866353248882
2 6.2 0.942894537172927
2 6.3 0.954789590006248
2 6.4 0.964604696739906
2 6.5 0.972599752998081
2 6.6 0.979028789235687
2 6.7 0.984132288774556
2 6.8 0.988131627103806
2 6.9 0.991225525675153
2 7 0.993588297917538
2 7.1 0.995369591527985
2 7.2 0.996695296914966
2 7.3 0.997669291162501
2 7.4 0.998375712195136
2 7.5 0.998881500411546
2 7.6 0.999238996790811
2 7.7 0.999488440433657
2 7.8 0.999660259392344
2 7.9 0.999777092942101
2 8 0.999855519274495
2.1 2 0.00110949554170847
2.1 2.1 0.00143704322290172
2.1 2.2 0.00190272400495461
2.1 2.3 0.00255612165642971
2.1 2.4 0.0034609264367238
2.1 2.5 0.00469753405652418
2.1 2.6 0.00636562373171901
2.1 2.7 0.00858651426507939
2.1 2.8 0.0115050443614183
2.1 2.9 0.015290678404975
2.1 3 0.0201375096481456
2.1 3.1 0.0262628277098644
2.1 3.2 0.0339039445281357
2.1 3.3 0.0433130387520159
2.1 3.4 0.0547498861009682
2.1 3.5 0.0684724910591777
2.1 3.6 0.0847258165205387
2.1 3.7 0.103729009917585
2.1 3.8 0.125661728812447
2.1 3.9 0.150650353726577
2.1 4 0.178755017199017
2.1 4.1 0.20995845294592
2.1 4.2 0.244157659295224
2.1 4.3 0.28115926618573
2.1 4.4 0.320679294471488
2.1 4.5 0.362347711020684
2.1 4.6 0.405717835332902
2.1 4.7 0.450280274677866
2.1 4.8 0.495480692630711
2.1 4.9 0.54074038939489
2.1 5 0.585478427043811
2.1 5.1 0.629133896168824
2.1 5.2 0.671186907874983
2.1 5.3 0.711177008050708
2.1 5.4 0.748717936539325
2.1 5.5 0.783507966966351
2.1 5.6 0.815335429203166
2.1 5.7 0.844079396998888
2.1 5.8 0.869705879945008
2.1 5.9 0.892260158584909
2.1 6 0.911856119895068
2.1 6.1 0.928663574301693
2.1 6.2 0.94289456314871
2.1 6.3 0.954789605895935
2.1 6.4 0.9646047063103
2.1 6.5 0.972599758673439
2.1 6.6 0.979028792549194
2.1 6.7 0.984132290679127
2.1 6.8 0.988131628181527
2.1 6.9 0.991225526275497
2.1 7 0.993588298246742
2.1 7.1 0.995369591705686
2.1 7.2 0.996695297009385
2.1 7.3 0.997669291211883
2.1 7.4 0.998375712220557
2.1 7.5 0.998881500424427
2.1 7.6 0.999238996797235
2.1 7.7 0.99948844043681
2.1 7.8 0.999660259393867
2.1 7.9 0.999777092942825
2.1 8 0.999855519274834
2.2 2 0.00127716415194634
2.2 2.1 0.00160305609595068
2.2 2.2 0.00206666200783134
2.2 2.3 0.00271750173154872
2.2 2.4 0.00361920421430395
2.2 2.5 0.00485211034520749
2.2 2.6 0.00651585524545695
2.2 2.7 0.00873172864189209
2.2 2.8 0.011644559163424
2.2 2.9 0.0154238233922567
2.2 3 0.0202636512346196
2.2 3.1 0.026381394185286
2.2 3.2 0.0340144504128834
2.2 3.3 0.0434151065774531
2.2 3.4 0.0548432639995218
2.2 3.5 0.068557064806095
2.2 3.6 0.0848016150730159
2.2 3.7 0.103796204005997
2.2 3.8 0.125720622697289
2.2 3.9 0.150701370780855
2.2 4 0.178798680448402
2.2 4.1 0.209995362047796
2.2 4.2 0.244188465629722
2.2 4.3 0.281184647755275
2.2 4.4 0.320699932170064
2.2 4.5 0.36236426756848
2.2 4.6 0.405730937817814
2.2 4.7 0.450290501250305
2.2 4.8 0.495488563508966
2.2 4.9 0.540746361994453
2.2 5 0.585482894773842
2.2 5.1 0.629137190257142
2.2 5.2 0.671189301491685
2.2 5.3 0.711178721990805
2.2 5.4 0.74871914578046
2.2 5.5 0.783508807518392
2.2 5.6 0.815336004791765
2.2 5.7 0.844079785256594
2.2 5.8 0.869706137906852
2.2 5.9 0.892260327390952
2.2 6 0.911856228685206
2.2 6.1 0.92866364334698
2.2 6.2 0.942894606300356
2.2 6.3 0.954789632451594
2.2 6.4 0.964604722401643
2.2 6.5 0.972599768273711
2.2 6.6 0.979028798188333
2.2 6.7 0.98413229394022
2.2 6.8 0.988131630038129
2.2 6.9 0.991225527316052
2.2 7 0.993588298820843
2.2 7.1 0.995369592017485
2.2 7.2 0.996695297176077
2.2 7.3 0.997669291299601
2.2 7.4 0.998375712265993
2.2 7.5 0.998881500447591
2.2 7.6 0.999238996808859
2.2 7.7 0.999488440442551
2.2 7.8 0.999660259396658
2.2 7.9 0.99977709294416
2.2 8 0.999855519275463
2.3 2 0.00150681663482521
2.3 2.1 0.00183064714508141
2.3 2.2 0.00229165330946857
2.3 2.3 0.00293926757974838
2.3 2.4 0.00383703321845841
2.3 2.5 0.00506521215484301
2.3 2.6 0.00672337291163405
2.3 2.7 0.00893275686399697
2.3 2.8 0.0118381681995714
2.3 2.9 0.0156090878796455
2.3 3 0.0204396826271819
2.3 3.1 0.0265473750336986
2.3 3.2 0.0341696683515864
2.3 3.3 0.0435589857013867
2.3 3.4 0.0549753913484663
2.3 3.5 0.0686772099397792
2.3 3.6 0.0849097411990749
2.3 3.7 0.103892469769991
2.3 3.8 0.125805374490425
2.3 3.9 0.150775126183674
2.3 4 0.178862104122173
2.3 4.1 0.210049235934039
2.3 4.2 0.244233655667268
2.3 4.3 0.281222069445706
2.3 4.4 0.320730517218603
2.3 4.5 0.362388933527987
2.3 4.6 0.40575056221962
2.3 4.7 0.450305901180093
2.3 4.8 0.495500481044349
2.3 4.9 0.540755455421877
2.3 5 0.585489735090987
2.3 5.1 0.629142262170586
2.3 5.2 0.671193007947624
2.3 5.3 0.711181391222245
2.3 5.4 0.748721039898684
2.3 5.5 0.783510131793495
2.3 5.6 0.815336916928289
2.3 5.7 0.844080404149203
2.3 5.8 0.869706551535585
2.3 5.9 0.892260599670335
2.3 6 0.911856405207073
2.3 6.1 0.928663756049894
2.3 6.2 0.942894677160132
2.3 6.3 0.954789676321763
2.3 6.4 0.964604749145392
2.3 6.5 0.972599784326066
2.3 6.6 0.979028807674703
2.3 6.7 0.984132299459582
2.3 6.8 0.988131633199598
2.3 6.9 0.99122552909878
2.3 7 0.993588299810451
2.3 7.1 0.995369592558253
2.3 7.2 0.996695297466957
2.3 7.3 0.997669291453616
2.3 7.4 0.998375712346261
2.3 7.5 0.998881500488768
2.3 7.6 0.99923899682965
2.3 7.7 0.999488440452883
2.3 7.8 0.999660259401712
2.3 7.9 0.999777092946593
2.3 8 0.999855519276616
2.4 2 0.00181837870595902
2.4 2.1 0.0021396725646412
2.4 2.2 0.00259745925543886
2.4 2.3 0.00324105370451522
2.4 2.4 0.00413388162354333
2.4 2.5 0.00535609372596568
2.4 2.6 0.00700716100346332
2.4 2.7 0.00920824888424732
2.4 2.8 0.0121041150532671
2.4 2.9 0.0158642305394766
2.4 3 0.0206827943607594
2.4 3.1 0.0267773071256474
2.4 3.2 0.0343853965967277
2.4 3.3 0.0437596545974502
2.4 3.4 0.0551603528459459
2.4 3.5 0.0688460538447403
2.4 3.6 0.0850623148653902
2.4 3.7 0.104028885513914
2.4 3.8 0.125926004130484
2.4 3.9 0.150880583145899
2.4 4 0.178953214538283
2.4 4.1 0.210127000989299
2.4 4.2 0.244299208038444
2.4 4.3 0.281276626955411
2.4 4.4 0.320775336807551
2.4 4.5 0.362425268337768
2.4 4.6 0.405779624044579
2.4 4.7 0.450328829868322
2.4 4.8 0.495518321693511
2.4 4.9 0.54076914350977
2.4 5 0.5855000890723
2.4 5.1 0.629149982607029
2.4 5.2 0.671198681940486
2.4 5.3 0.711185500783308
2.4 5.4 0.748723972907289
2.4 5.5 0.783512194312169
2.4 5.6 0.81533834584867
2.4 5.7 0.844081379376633
2.4 5.8 0.869707207162225
2.4 5.9 0.892261033808347
2.4 6 0.911856688339578
2.4 6.1 0.928663937900485
2.4 6.2 0.942894792181137
2.4 6.3 0.954789747961385
2.4 6.4 0.964604793081443
2.4 6.5 0.972599810857445
2.4 6.6 0.979028823449021
2.4 6.7 0.984132308693275
2.4 6.8 0.988131638520899
2.4 6.9 0.991225532117773
2.4 7 0.993588301496592
2.4 7.1 0.995369593485294
2.4 7.2 0.996695297968682
2.4 7.3 0.997669291720905
2.4 7.4 0.998375712486425
2.4 7.5 0.998881500561114
2.4 7.6 0.999238996866404
2.4 7.7 0.999488440471262
2.4 7.8 0.999660259410757
2.4 7.9 0.999777092950975
2.4 8 0.999855519278704
2.5 2 0.00223705875466529
2.5 2.1 0.00255526753843971
2.5 2.2 0.00300911372295409
2.5 2.3 0.00364775667919126
2.5 2.4 0.00453446365839683
2.5 2.5 0.0057492319060567
2.5 2.6 0.00739139333097694
2.5 2.7 0.00958199904789192
2.5 2.8 0.0124657278572216
2.5 2.9 0.0162120173821354
2.5 3 0.0210150871770864
2.5 3.1 0.0270925182422679
2.5 3.2 0.0346820811136742
2.5 3.3 0.0440365713209561
2.5 3.4 0.0554165195496881
2.5 3.5 0.0690807927931489
2.5 3.6 0.0852752851041334
2.5 3.7 0.104220099377478
2.5 3.8 0.126095826641983
2.5 3.9 0.151029714357524
2.5 4 0.179082656228646
2.5 4.1 0.210238010343608
2.5 4.2 0.244393241858823
2.5 4.3 0.281355280492154
2.5 4.4 0.320840281429323
2.5 4.5 0.36247819198561
2.5 4.6 0.405822177804562
2.5 4.7 0.450362583149651
2.5 4.8 0.495544727529664
2.5 4.9 0.540789514527079
2.5 5 0.585515583794944
2.5 5.1 0.629161601064666
2.5 5.2 0.671207269057132
2.5 5.3 0.711191755763638
2.5 5.4 0.748728462781282
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6.7 3.5 0.8623640790212
6.7 3.6 0.862766745020623
6.7 3.7 0.863306089898123
6.7 3.8 0.864017082033641
6.7 3.9 0.864939537423103
6.7 4 0.866117453125191
6.7 4.1 0.867597847674396
6.7 4.2 0.869429072154542
6.7 4.3 0.871658601586693
6.7 4.4 0.874330372987437
6.7 4.5 0.877481798709437
6.7 4.6 0.881140644204049
6.7 4.7 0.885322009540058
6.7 4.8 0.890025685126162
6.7 4.9 0.895234156671825
6.7 5 0.900911507798149
6.7 5.1 0.907003410149501
6.7 5.2 0.91343830423399
6.7 5.3 0.920129767923243
6.7 5.4 0.926979955562539
6.7 5.5 0.933883882962319
6.7 5.6 0.940734246047658
6.7 5.7 0.947426405242612
6.7 5.8 0.95386315121476
6.7 5.9 0.959958892484661
6.7 6 0.965642968007319
6.7 6.1 0.970861879494319
6.7 6.2 0.975580346702459
6.7 6.3 0.979781200296845
6.7 6.4 0.983464227872046
6.7 6.5 0.986644168406321
6.7 6.6 0.989348101768798
6.7 6.7 0.991612500276711
6.7 6.8 0.993480200384793
6.7 6.9 0.994997519557208
6.7 7 0.996211693669129
6.7 7.1 0.997168752232531
6.7 7.2 0.997911890239109
6.7 7.3 0.998480342935144
6.7 7.4 0.998908727831324