Bootstrap p value contradicts p value for likelihood ratio test - r
I have the same problem as the one posted by #soapsuds here. I did not want to ask a duplicate question but when I tried to edit the original question to provide the reproducible example that was missing in the original post my edits got rejected. Since the reproducible example has a lot of elements, I could not write it as a comment to the original question either, so I provide my code and my reproducible data here, as a separate question.
I am trying to compare two models using the likelihood ratio test. From bootstrapping I get a set of 1000 p-values. Here are the numbers I get:
chi2 <- c(41.83803376, 69.23970174, 42.5479637, 50.90208302, 39.18366824, 78.88589665, 28.88469406, 34.99980796, 85.80860848, 66.01750186, 29.06286, 46.43221576, 46.50523792, 59.87362884, 46.17274808, 77.97429928, 48.04404216, 12.88592623, 43.1883816, 33.24251471, 53.27310465, 56.92595147, 47.99838583, 46.0718587, 49.0760042, 29.70866297, 66.80696553, 66.61091741, 37.82375112, 50.19760846, 30.99961864, 27.17687828, 37.46944206, 66.36226432, 48.30737714, 43.64410333, 23.78480451, 42.52842793, 60.49309556, 46.29154, 26.96744296, 32.21561396, 48.20316788, 38.73153704, 67.80328765, 55.00664931, 36.74645735, 23.3647159, 56.35290442, 38.11055268, 58.3316501, 36.00500638, 41.36949956, 49.09067881, 64.42712507, 23.97787069, 54.5394799, 87.02114296, 26.01402166, 50.47426712, 38.58006084, 48.47626864, 22.28809699, 58.87590487, 17.59264288, 33.32650413, 67.77868338, 60.95427815, 37.19931376, 36.23280256, 53.54379697, 70.06479334, 41.3482703, 34.54099647, 55.99585144, 30.60500406, 32.02745276, 37.92670127, 44.23450124, 40.38607671, 44.02263294, 40.89874789, 62.74174279, 50.95137406, 47.12851204, 26.03848394, 36.6202765, 61.06296311, 50.17094183, 35.93242228, 41.8913277, 35.19089913, 38.88574534, 66.075866, 26.34296242, 49.99887059, 42.97123036, 34.89006324, 66.5460019, 67.61855859, 48.52166614, 41.41324193, 46.76294302, 14.87650733, 24.11661382, 62.28747719, 43.94865019, 44.20328393, 41.17756328, 43.74055584, 49.46236395, 38.59558107, 42.85073398, 49.81046036, 36.60331917, 39.85328124, 59.31376822, 61.36038822, 52.56707689, 29.19196892, 46.473958, 39.12904163, 38.75057931, 36.32493909, 49.61088785, 33.42904297, 34.73661836, 33.97736002, 37.44094284, 57.73605417, 43.14773064, 42.78707831, 26.84112684, 48.47832871, 45.94043053, 71.13563773, 46.28614795, 42.33386157, 59.31216832, 46.72946806, 47.76027545, 52.45174304, 49.99459367, 59.00971014, 24.03299408, 17.09453132, 37.44112252, 46.6352525, 60.42442286, 39.35194465, 46.57121135, 56.28622077, 59.20354176, 57.72511864, 41.97053375, 27.97077407, 29.70497125, 46.63976021, 40.24305901, 24.84335714, 36.08600444, 61.619572, 69.31377401, 86.91496878, 44.47955842, 44.1230351, 46.12514671, 43.97381958, 71.99269072, 47.01277643, 50.08167664, 27.01076954, 31.32586466, 40.96782215, 19.07024825, 53.00009679, 43.15397869, 42.49652848, 53.47325607, 43.45891027, 42.57719313, 39.40459925, 42.15077856, 52.23784844, 33.07947933, 45.02462309, 59.187763, 51.9198527, 48.3179841, 76.10501177, 34.95091433, 40.75545034, 31.27034043, 39.83209227, 47.87278051, 46.25057806, 62.84591205, 41.24656655, 68.14749236, 53.11576938, 39.20515676, 61.96116013, 35.64665684, 72.52689101, 54.64239536, 34.14169048, 34.32282338, 49.60786171, 50.32976034, 43.83560386, 57.49367366, 81.65759842, 61.59398941, 37.77960776, 30.74484476, 34.72859511, 32.46631033, 37.41725027, 34.04569722, 54.11932007, 34.62264522, 28.36753913, 30.95379445, 84.06354755, 29.32445434, 56.7720931, 33.23951864, 48.61860157, 39.3563214, 32.44713462, 61.25078174, 32.49661836, 40.38508488, 26.73565294, 58.16191656, 61.12461262, 23.701462, 22.14004554, 57.80213129, 57.15936762, 31.51238062, 44.60223083, 30.60135802, 46.96637333, 42.79517081, 56.85541543, 48.79421654, 29.72862307, 41.61735121, 43.37983393, 41.16802781, 61.69637392, 37.29991153, 39.0936012, 57.39158494, 57.55033901, 50.72878897, 34.82491685, 42.66486539, 34.54565803, 55.04161695, 44.56687339, 53.46745359, 57.22210412, 34.8578696, 28.81098073, 51.4033337, 51.9568532, 60.98717632, 62.98817996, 44.1335128, 33.38418814, 59.71059054, 45.82016411, 29.47178401, 30.64995791, 28.52106318, 53.98066153, 64.22209517, 58.29438562, 39.18280924, 38.1302144, 41.90062316, 28.68650929, 69.42769639, 33.79539164, 26.08549507, 55.29167497, 97.25975259, 63.07957724, 56.59002373, 51.40088678, 71.33491023, 46.24955174, 33.90101761, 38.0669817, 52.50993176, 51.84637529, 39.93642798, 61.9268346, 30.25561485, 49.57396856, 44.70170977, 57.00286149, 40.39009586, 63.23642634, 59.23643766, 55.80521902, 68.58421775, 24.04456631, 51.64338572, 61.14103174, 59.29371792, 46.51493959, 43.48297587, 39.99164284, 44.62589755, 58.89385062, 60.96824416, 54.02310453, 43.54420281, 44.24628098, 47.0991445, 58.9015349, 60.54157696, 34.86277089, 33.79969585, 34.57183642, 47.21383117, 55.3529805, 36.49813553, 44.94388291, 29.43134497, 43.41469037, 43.033338, 63.37329389, 38.22029171, 43.2894392, 23.42769168, 55.18117532, 19.39227876, 28.29656641, 28.56075122, 39.57260362, 65.48606054, 31.05339648, 24.87488959, 61.6027878, 59.56983406, 37.53918879, 28.67095839, 36.51499868, 44.43350204, 53.35842664, 48.30182354, 31.03494822, 45.68689659, 46.11113306, 53.89204524, 29.75548276, 35.60906482, 53.35195594, 56.28657675, 44.77245145, 60.20671942, 41.62253735, 40.34528594, 38.48551456, 27.39317425, 51.05414332, 38.41986986, 75.05074423, 34.16773046, 52.18497954, 49.63059496, 28.7365636, 10.59466471, 38.1033901, 52.20531405, 47.031987, 47.45955635, 44.64312012, 50.32229588, 62.40798968, 37.7455721, 31.97746406, 51.17250147, 45.91231295, 66.58450378, 32.68956686, 34.35845347, 70.34703042, 41.47493453, 53.67684859, 35.66735299, 19.76630329, 35.69026569, 76.57475236, 62.11269107, 37.06632602, 57.91686258, 33.95869501, 55.18034702, 66.09725866, 46.80608564, 46.75623531, 55.49605214, 45.7813294, 22.37612777, 62.40414132, 50.51745906, 46.86535062, 54.4172637, 35.44713601, 45.40918234, 43.83215257, 57.14754799, 24.20941074, 44.8145542, 50.79673435, 42.14561269, 32.73720673, 28.51047028, 32.14753623, 28.43006627, 39.50188334, 58.51806717, 37.96898151, 73.14656287, 48.23605238, 75.31273481, 29.57608972, 43.62952257, 30.47534709, 43.24927262, 43.61475563, 53.48883918, 53.85263136, 41.91477406, 56.16405384, 46.21202327, 55.52602904, 49.88481191, 46.31478116, 72.29722834, 40.48187205, 35.31368051, 40.57713079, 34.15725967, 65.85738596, 32.16093944, 32.07117679, 46.44579516, 53.3243447, 69.35531671, 21.70205174, 44.30678622, 40.13349937, 51.7431728, 43.03690121, 26.53566586, 18.74773427, 25.97768442, 66.68668827, 42.97352559, 31.61567696, 61.57362103, 55.07104736, 25.05950764, 53.04884067, 30.47176616, 43.33249885, 44.48360752, 40.59006165, 44.29759954, 69.71063388, 47.70186943, 51.12166943, 40.15048072, 44.96459746, 56.31842906, 57.79593771, 49.19795057, 33.58506451, 42.67650993, 47.96512915, 57.98722437, 42.08107371, 66.85903821, 45.30286487, 38.39187118, 48.02442004, 35.97047743, 56.71378254, 40.51082047, 43.78022461, 60.33208664, 35.78159098, 40.98937317, 36.20547787, 45.2382906, 47.81497885, 20.44519563, 16.68817267, 38.31035896, 38.60590267, 70.75756511, 31.73001452, 45.85476281, 47.11473565, 31.40248172, 42.94971714, 39.34376633, 21.09018956, 31.45915941, 53.82696054, 73.59824534, 31.5694168, 39.02189966, 46.91790827, 60.66603832, 59.81148782, 20.46813743, 54.95108785, 66.71844123, 49.48461319, 25.10459028, 60.26169536, 21.90344297, 63.56310687, 38.70295559, 58.19794152, 25.68981924, 61.4804908, 41.97067608, 22.77156359, 48.51789441, 50.31845297, 42.36456456, 43.35814281, 41.32891651, 35.17106573, 48.45296117, 30.55292595, 55.26758567, 71.25929921, 34.62580089, 43.89804598, 46.06384675, 30.74209253, 47.99143497, 34.02715801, 37.95367551, 45.14366438, 40.73655716, 45.32116105, 48.17651965, 63.54774876, 16.32237452, 54.22730144, 46.02331286, 45.44633826, 53.56976595, 53.96781286, 19.79116777, 42.05820938, 45.48852278, 37.34932167, 45.134461, 49.60637239, 29.99017683, 35.2785614, 71.54855053, 61.55744768, 55.7627296, 37.72455372, 62.51288842, 48.17063649, 65.26648616, 48.4831201, 33.49833137, 32.10986243, 15.42586026, 41.95660905, 30.07072484, 42.33604863, 53.20660203, 48.27036556, 32.92677161, 33.59521848, 44.04333058, 59.30038922, 48.84064622, 63.31815488, 36.01169023, 44.42967033, 23.14247159, 53.6314237, 42.43225997, 28.18151375, 44.0733306, 55.93530003, 30.86515779, 34.10702034, 59.38495522, 57.79906004, 64.86160093, 56.70670687, 43.24880707, 40.00049219, 44.08430336, 17.50391283, 72.81320114, 41.55481964, 63.461066, 50.81938548, 58.7427594, 35.27822458, 33.5188344, 46.13196979, 56.94022883, 66.96258461, 39.19601268, 21.95750575, 51.67252792, 46.51047909, 30.42289547, 46.47496475, 41.6440483, 42.36900563, 68.29398345, 30.14059255, 38.90124252, 40.87014585, 51.33635945, 51.72908337, 50.8177621, 31.65411733, 56.75197699, 47.76885318, 34.18305356, 52.52137441, 48.39806899, 18.34609209, 32.5461584, 60.15104883, 36.29250847, 39.02418361, 34.68801402, 48.02453889, 31.36738248, 42.44522981, 71.79176852, 34.25588794, 38.46866138, 45.01393624, 63.38509325, 32.44823195, 64.59346474, 53.80793998, 41.2889141, 28.86534461, 34.85039051, 37.04622686, 31.83207726, 36.65410743, 27.66293315, 23.11203257, 41.61059067, 19.97321534, 59.879676, 39.84187157, 47.324581, 38.24903991, 41.0234849, 62.30809429, 48.47191326, 23.26696808, 29.91547934, 78.39181209, 41.86240014, 33.53717515, 39.63756903, 74.86377649, 56.30173648, 40.29403413, 59.12602764, 47.23561802, 51.32370456, 45.44426051, 55.54666292, 58.85362888, 38.30516953, 46.11300177, 37.96931091, 41.01315149, 63.09345867, 26.74145771, 31.37447907, 39.26896396, 65.35880308, 60.0670218, 45.48057201, 29.76683425, 51.39638136, 46.12180705, 60.72093818, 45.01613513, 37.04611291, 31.32979098, 57.82548455, 29.89919764, 38.77980495, 55.71511912, 66.9872235, 48.74616069, 32.87503301, 56.10335632, 28.72445387, 41.00675821, 55.22238115, 38.56391412, 21.82487917, 51.87394855, 41.62740713, 72.32943223, 49.85456187, 41.76869194, 55.686196, 46.18471338, 52.57455653, 23.03383172, 51.460223, 45.88045256, 47.91709836, 53.09464847, 65.17159616, 48.0076358, 42.50038253, 50.57143193, 22.05776575, 25.5770314, 57.41889173, 37.07408252, 69.83286794, 53.31690771, 36.14562381, 35.3626014, 70.74448842, 30.01870438, 41.95755074, 64.41141845, 48.12704663, 29.33183678, 47.45391445, 35.76760392, 17.57864013, 42.66918162, 27.84884911, 37.83419437, 56.38203205, 32.93395446, 19.45549279, 48.49557175, 63.74692618, 48.36501421, 38.45370018, 63.77499738, 43.40984685, 61.28735474, 47.00513455, 31.82012086, 40.85624032, 32.79590137, 43.79441893, 47.93350586, 26.44410209, 22.71480768, 41.74097624, 29.7828174, 35.24077319, 37.1436077, 63.62150539, 35.27952907, 30.9258966, 35.22384343, 45.0069715, 47.38652625, 60.86474384, 53.19528479, 37.61239521, 64.78497877, 39.50008676, 43.11733875, 34.67761458, 55.21401193, 57.22836509, 30.10411603, 30.03903287, 53.62027996, 40.63516283, 50.229386, 39.59707517, 55.53993024, 62.31160356, 48.65142538, 59.51279601, 51.46268896, 36.70086545, 45.73324953, 39.82026282, 51.51657943, 39.9507342, 26.65847555, 18.11032673, 41.57393548, 37.24804734, 59.78878572, 42.18870686, 57.73556775, 29.83442692, 24.27687775, 44.54663257, 48.40426261, 34.13830576, 64.47843419, 53.82888778, 45.77073351, 41.95910655, 56.25654343, 42.44938602, 18.92651056, 62.89841562, 42.28210051, 60.01632343, 56.38799965, 53.56842386, 71.059581, 59.21196097, 72.29678294, 40.0820475, 74.53163756, 46.35508897, 48.65592196, 36.69711286, 54.84914739, 57.62299813, 63.0750109, 25.53592874, 19.43203054, 63.18532427, 54.79806194, 28.75123602, 47.68037559, 36.06887062, 48.53619627, 42.05208952, 14.47366507, 26.25183654, 57.37741978, 24.92962789, 47.85306044, 35.55674275, 43.62606531, 51.98445971, 57.10441923, 45.20539557, 43.22417529, 48.20941756, 37.12416781, 39.54238987, 45.31000358, 24.59001204, 32.61256929, 31.61553515, 55.76617515, 57.82479513, 34.12465645, 52.1634834, 50.140277, 34.5334757, 70.76112738, 47.22161503, 35.44101995, 54.50312705, 47.74706989, 21.04494842, 42.42698916, 57.8551517, 49.67127478, 67.6702045, 30.64335682, 31.87819093, 45.79096976, 42.72129981, 56.22043416, 22.12571532, 31.93377902, 31.9561172, 60.28281847, 37.49005649, 30.63141229, 22.82707918, 29.55804713, 55.79929136, 39.64043613, 31.79538118, 61.92391469, 19.30462724, 37.00041938, 61.26446455, 47.10048686, 34.70929308, 33.34157984, 49.28331646, 39.9565451, 48.80158593, 29.25279435, 49.96980394, 68.7766356, 49.61949286, 18.80600378, 52.93721773, 24.29791779, 67.69568275, 54.22725318, 35.67531845, 58.05037476, 70.54029077, 55.59508174, 42.07974012, 61.62117032, 44.47174079, 40.13197612, 61.19863058, 35.16748823, 54.79320966, 46.40640448, 41.99222891, 53.33216862, 19.04146695, 29.60278169, 38.43089591, 61.22497978, 32.04678119, 30.77915985, 38.02625789, 74.25140223, 30.44626923, 42.69951906, 28.99988779, 49.76041564, 30.86941271, 58.65788956, 62.64967161, 23.5689175, 42.21941421, 54.88455829, 38.10115824, 24.12341961, 32.84464782, 81.72102673, 42.42771851, 37.75191241, 32.05927543, 43.55812503, 64.79161154, 61.05179286, 53.24693267, 36.29056269, 61.49030629, 53.68500702, 65.93501988, 50.7243041, 51.72139759, 64.80610623, 58.2860023, 33.16444766, 42.7872046, 55.14190562, 39.14341079, 36.05577261, 30.03351742, 24.16526837, 47.94163599, 52.55045103, 56.60625705, 61.6878126, 23.13212844, 50.50369148, 47.79873905, 47.01238239, 35.9159739, 53.18067189, 48.42928497, 67.48879213, 37.37609292, 19.7749038, 47.87115046, 48.90378974)
p.values <- c(9.92E-11, 8.72E-17, 6.90E-11, 9.71E-13, 3.86E-10, 6.58E-19, 7.68E-08, 3.30E-09, 1.98E-20, 4.47E-16, 7.01E-08, 9.48E-12, 9.14E-12, 1.01E-14, 1.08E-11, 1.04E-18, 4.17E-12, 0.000331062, 4.97E-11, 8.14E-09, 2.90E-13, 4.53E-14, 4.27E-12, 1.14E-11, 2.46E-12, 5.02E-08, 2.99E-16, 3.31E-16, 7.74E-10, 1.39E-12, 2.58E-08, 1.86E-07, 9.29E-10, 3.75E-16, 3.64E-12, 3.94E-11, 1.08E-06, 6.97E-11, 7.38E-15, 1.02E-11, 2.07E-07, 1.38E-08, 3.84E-12, 4.86E-10, 1.81E-16, 1.20E-13, 1.35E-09, 1.34E-06, 6.06E-14, 6.68E-10, 2.21E-14, 1.97E-09, 1.26E-10, 2.44E-12, 1.00E-15, 9.74E-07, 1.52E-13, 1.07E-20, 3.39E-07, 1.21E-12, 5.26E-10, 3.34E-12, 2.35E-06, 1.68E-14, 2.74E-05, 7.79E-09, 1.83E-16, 5.84E-15, 1.07E-09, 1.75E-09, 2.53E-13, 5.74E-17, 1.27E-10, 4.17E-09, 7.26E-14, 3.16E-08, 1.52E-08, 7.35E-10, 2.91E-11, 2.08E-10, 3.25E-11, 1.60E-10, 2.36E-15, 9.47E-13, 6.65E-12, 3.35E-07, 1.44E-09, 5.53E-15, 1.41E-12, 2.04E-09, 9.65E-11, 2.99E-09, 4.49E-10, 4.34E-16, 2.86E-07, 1.54E-12, 5.56E-11, 3.49E-09, 3.42E-16, 1.98E-16, 3.27E-12, 1.23E-10, 8.01E-12, 0.000114784, 9.07E-07, 2.97E-15, 3.37E-11, 2.96E-11, 1.39E-10, 3.75E-11, 2.02E-12, 5.21E-10, 5.91E-11, 1.69E-12, 1.45E-09, 2.74E-10, 1.34E-14, 4.75E-15, 4.16E-13, 6.56E-08, 9.28E-12, 3.97E-10, 4.82E-10, 1.67E-09, 1.87E-12, 7.39E-09, 3.77E-09, 5.58E-09, 9.42E-10, 3.00E-14, 5.08E-11, 6.10E-11, 2.21E-07, 3.34E-12, 1.22E-11, 3.33E-17, 1.02E-11, 7.69E-11, 1.35E-14, 8.15E-12, 4.82E-12, 4.41E-13, 1.54E-12, 1.57E-14, 9.47E-07, 3.56E-05, 9.42E-10, 8.55E-12, 7.65E-15, 3.54E-10, 8.83E-12, 6.27E-14, 1.42E-14, 3.01E-14, 9.27E-11, 1.23E-07, 5.03E-08, 8.53E-12, 2.24E-10, 6.22E-07, 1.89E-09, 4.17E-15, 8.40E-17, 1.13E-20, 2.57E-11, 3.08E-11, 1.11E-11, 3.33E-11, 2.16E-17, 7.05E-12, 1.47E-12, 2.02E-07, 2.18E-08, 1.55E-10, 1.26E-05, 3.34E-13, 5.06E-11, 7.08E-11, 2.62E-13, 4.33E-11, 6.79E-11, 3.44E-10, 8.45E-11, 4.92E-13, 8.85E-09, 1.95E-11, 1.43E-14, 5.78E-13, 3.62E-12, 2.69E-18, 3.38E-09, 1.73E-10, 2.24E-08, 2.77E-10, 4.55E-12, 1.04E-11, 2.24E-15, 1.34E-10, 1.52E-16, 3.14E-13, 3.82E-10, 3.50E-15, 2.37E-09, 1.65E-17, 1.45E-13, 5.12E-09, 4.67E-09, 1.88E-12, 1.30E-12, 3.57E-11, 3.39E-14, 1.62E-19, 4.22E-15, 7.92E-10, 2.94E-08, 3.79E-09, 1.21E-08, 9.54E-10, 5.38E-09, 1.89E-13, 4.00E-09, 1.00E-07, 2.64E-08, 4.79E-20, 6.12E-08, 4.89E-14, 8.15E-09, 3.11E-12, 3.53E-10, 1.22E-08, 5.02E-15, 1.19E-08, 2.09E-10, 2.33E-07, 2.41E-14, 5.36E-15, 1.12E-06, 2.53E-06, 2.90E-14, 4.02E-14, 1.98E-08, 2.41E-11, 3.17E-08, 7.22E-12, 6.08E-11, 4.69E-14, 2.84E-12, 4.97E-08, 1.11E-10, 4.51E-11, 1.40E-10, 4.01E-15, 1.01E-09, 4.04E-10, 3.57E-14, 3.29E-14, 1.06E-12, 3.61E-09, 6.50E-11, 4.16E-09, 1.18E-13, 2.46E-11, 2.63E-13, 3.89E-14, 3.55E-09, 7.98E-08, 7.52E-13, 5.67E-13, 5.74E-15, 2.08E-15, 3.07E-11, 7.56E-09, 1.10E-14, 1.30E-11, 5.67E-08, 3.09E-08, 9.27E-08, 2.02E-13, 1.11E-15, 2.26E-14, 3.86E-10, 6.62E-10, 9.60E-11, 8.51E-08, 7.93E-17, 6.12E-09, 3.27E-07, 1.04E-13, 6.08E-23, 1.99E-15, 5.37E-14, 7.53E-13, 3.01E-17, 1.04E-11, 5.80E-09, 6.84E-10, 4.28E-13, 6.00E-13, 2.62E-10, 3.56E-15, 3.79E-08, 1.91E-12, 2.29E-11, 4.35E-14, 2.08E-10, 1.83E-15, 1.40E-14, 8.00E-14, 1.22E-16, 9.41E-07, 6.66E-13, 5.31E-15, 1.36E-14, 9.09E-12, 4.28E-11, 2.55E-10, 2.39E-11, 1.66E-14, 5.80E-15, 1.98E-13, 4.14E-11, 2.90E-11, 6.75E-12, 1.66E-14, 7.20E-15, 3.54E-09, 6.11E-09, 4.11E-09, 6.36E-12, 1.01E-13, 1.53E-09, 2.03E-11, 5.79E-08, 4.43E-11, 5.38E-11, 1.71E-15, 6.32E-10, 4.72E-11, 1.30E-06, 1.10E-13, 1.06E-05, 1.04E-07, 9.08E-08, 3.16E-10, 5.85E-16, 2.51E-08, 6.12E-07, 4.20E-15, 1.18E-14, 8.96E-10, 8.58E-08, 1.51E-09, 2.63E-11, 2.78E-13, 3.65E-12, 2.53E-08, 1.39E-11, 1.12E-11, 2.12E-13, 4.90E-08, 2.41E-09, 2.79E-13, 6.26E-14, 2.21E-11, 8.54E-15, 1.11E-10, 2.13E-10, 5.52E-10, 1.66E-07, 8.99E-13, 5.70E-10, 4.59E-18, 5.06E-09, 5.05E-13, 1.86E-12, 8.29E-08, 0.001134145, 6.71E-10, 5.00E-13, 6.98E-12, 5.62E-12, 2.36E-11, 1.30E-12, 2.79E-15, 8.06E-10, 1.56E-08, 8.46E-13, 1.24E-11, 3.35E-16, 1.08E-08, 4.58E-09, 4.97E-17, 1.19E-10, 2.36E-13, 2.34E-09, 8.75E-06, 2.31E-09, 2.12E-18, 3.24E-15, 1.14E-09, 2.73E-14, 5.63E-09, 1.10E-13, 4.29E-16, 7.84E-12, 8.04E-12, 9.36E-14, 1.32E-11, 2.24E-06, 2.80E-15, 1.18E-12, 7.60E-12, 1.62E-13, 2.62E-09, 1.60E-11, 3.58E-11, 4.04E-14, 8.64E-07, 2.17E-11, 1.02E-12, 8.47E-11, 1.05E-08, 9.32E-08, 1.43E-08, 9.71E-08, 3.28E-10, 2.01E-14, 7.19E-10, 1.20E-17, 3.78E-12, 4.02E-18, 5.38E-08, 3.97E-11, 3.38E-08, 4.82E-11, 4.00E-11, 2.60E-13, 2.16E-13, 9.53E-11, 6.67E-14, 1.06E-11, 9.22E-14, 1.63E-12, 1.01E-11, 1.85E-17, 1.98E-10, 2.81E-09, 1.89E-10, 5.08E-09, 4.85E-16, 1.42E-08, 1.49E-08, 9.42E-12, 2.83E-13, 8.22E-17, 3.18E-06, 2.81E-11, 2.37E-10, 6.33E-13, 5.37E-11, 2.59E-07, 1.49E-05, 3.45E-07, 3.18E-16, 5.55E-11, 1.88E-08, 4.26E-15, 1.16E-13, 5.56E-07, 3.25E-13, 3.39E-08, 4.62E-11, 2.56E-11, 1.88E-10, 2.82E-11, 6.87E-17, 4.96E-12, 8.68E-13, 2.35E-10, 2.01E-11, 6.16E-14, 2.91E-14, 2.31E-12, 6.82E-09, 6.46E-11, 4.34E-12, 2.64E-14, 8.76E-11, 2.92E-16, 1.69E-11, 5.79E-10, 4.21E-12, 2.00E-09, 5.04E-14, 1.96E-10, 3.67E-11, 8.01E-15, 2.21E-09, 1.53E-10, 1.78E-09, 1.74E-11, 4.68E-12, 6.14E-06, 4.41E-05, 6.03E-10, 5.19E-10, 4.04E-17, 1.77E-08, 1.27E-11, 6.70E-12, 2.10E-08, 5.62E-11, 3.55E-10, 4.38E-06, 2.04E-08, 2.19E-13, 9.57E-18, 1.92E-08, 4.19E-10, 7.40E-12, 6.76E-15, 1.04E-14, 6.06E-06, 1.24E-13, 3.13E-16, 2.00E-12, 5.43E-07, 8.30E-15, 2.87E-06, 1.55E-15, 4.93E-10, 2.37E-14, 4.01E-07, 4.47E-15, 9.27E-11, 1.82E-06, 3.27E-12, 1.31E-12, 7.58E-11, 4.56E-11, 1.29E-10, 3.02E-09, 3.38E-12, 3.25E-08, 1.05E-13, 3.13E-17, 4.00E-09, 3.46E-11, 1.14E-11, 2.95E-08, 4.28E-12, 5.43E-09, 7.24E-10, 1.83E-11, 1.74E-10, 1.67E-11, 3.90E-12, 1.57E-15, 5.34E-05, 1.79E-13, 1.17E-11, 1.57E-11, 2.50E-13, 2.04E-13, 8.64E-06, 8.86E-11, 1.54E-11, 9.88E-10, 1.84E-11, 1.88E-12, 4.34E-08, 2.86E-09, 2.71E-17, 4.30E-15, 8.18E-14, 8.15E-10, 2.65E-15, 3.91E-12, 6.54E-16, 3.33E-12, 7.13E-09, 1.46E-08, 8.58E-05, 9.33E-11, 4.17E-08, 7.69E-11, 3.00E-13, 3.71E-12, 9.57E-09, 6.79E-09, 3.21E-11, 1.35E-14, 2.78E-12, 1.76E-15, 1.96E-09, 2.64E-11, 1.50E-06, 2.42E-13, 7.32E-11, 1.10E-07, 3.16E-11, 7.49E-14, 2.77E-08, 5.22E-09, 1.30E-14, 2.90E-14, 8.03E-16, 5.06E-14, 4.82E-11, 2.54E-10, 3.15E-11, 2.87E-05, 1.43E-17, 1.15E-10, 1.64E-15, 1.01E-12, 1.80E-14, 2.86E-09, 7.06E-09, 1.11E-11, 4.49E-14, 2.77E-16, 3.83E-10, 2.79E-06, 6.56E-13, 9.11E-12, 3.47E-08, 9.28E-12, 1.09E-10, 7.56E-11, 1.41E-16, 4.02E-08, 4.46E-10, 1.63E-10, 7.78E-13, 6.37E-13, 1.01E-12, 1.84E-08, 4.94E-14, 4.80E-12, 5.02E-09, 4.26E-13, 3.48E-12, 1.84E-05, 1.16E-08, 8.79E-15, 1.70E-09, 4.19E-10, 3.87E-09, 4.21E-12, 2.14E-08, 7.27E-11, 2.39E-17, 4.83E-09, 5.56E-10, 1.96E-11, 1.70E-15, 1.22E-08, 9.21E-16, 2.21E-13, 1.31E-10, 7.76E-08, 3.56E-09, 1.15E-09, 1.68E-08, 1.41E-09, 1.44E-07, 1.53E-06, 1.11E-10, 7.85E-06, 1.01E-14, 2.75E-10, 6.02E-12, 6.23E-10, 1.50E-10, 2.94E-15, 3.35E-12, 1.41E-06, 4.51E-08, 8.45E-19, 9.79E-11, 6.99E-09, 3.06E-10, 5.04E-18, 6.22E-14, 2.18E-10, 1.48E-14, 6.29E-12, 7.83E-13, 1.57E-11, 9.13E-14, 1.70E-14, 6.05E-10, 1.12E-11, 7.19E-10, 1.51E-10, 1.97E-15, 2.33E-07, 2.13E-08, 3.69E-10, 6.24E-16, 9.17E-15, 1.54E-11, 4.87E-08, 7.55E-13, 1.11E-11, 6.58E-15, 1.95E-11, 1.15E-09, 2.18E-08, 2.86E-14, 4.55E-08, 4.74E-10, 8.38E-14, 2.73E-16, 2.91E-12, 9.83E-09, 6.88E-14, 8.34E-08, 1.52E-10, 1.08E-13, 5.30E-10, 2.99E-06, 5.92E-13, 1.10E-10, 1.82E-17, 1.66E-12, 1.03E-10, 8.50E-14, 1.08E-11, 4.14E-13, 1.59E-06, 7.31E-13, 1.26E-11, 4.45E-12, 3.18E-13, 6.87E-16, 4.25E-12, 7.07E-11, 1.15E-12, 2.65E-06, 4.25E-07, 3.52E-14, 1.14E-09, 6.45E-17, 2.84E-13, 1.83E-09, 2.74E-09, 4.07E-17, 4.28E-08, 9.33E-11, 1.01E-15, 3.99E-12, 6.10E-08, 5.63E-12, 2.22E-09, 2.76E-05, 6.48E-11, 1.31E-07, 7.70E-10, 5.97E-14, 9.53E-09, 1.03E-05, 3.31E-12, 1.41E-15, 3.54E-12, 5.61E-10, 1.39E-15, 4.44E-11, 4.93E-15, 7.08E-12, 1.69E-08, 1.64E-10, 1.02E-08, 3.65E-11, 4.41E-12, 2.71E-07, 1.88E-06, 1.04E-10, 4.83E-08, 2.91E-09, 1.10E-09, 1.51E-15, 2.86E-09, 2.68E-08, 2.94E-09, 1.96E-11, 5.83E-12, 6.11E-15, 3.02E-13, 8.63E-10, 8.35E-16, 3.28E-10, 5.16E-11, 3.89E-09, 1.08E-13, 3.88E-14, 4.09E-08, 4.23E-08, 2.43E-13, 1.83E-10, 1.37E-12, 3.12E-10, 9.16E-14, 2.93E-15, 3.06E-12, 1.22E-14, 7.30E-13, 1.38E-09, 1.36E-11, 2.78E-10, 7.10E-13, 2.60E-10, 2.43E-07, 2.08E-05, 1.13E-10, 1.04E-09, 1.06E-14, 8.29E-11, 3.00E-14, 4.71E-08, 8.34E-07, 2.48E-11, 3.47E-12, 5.13E-09, 9.76E-16, 2.19E-13, 1.33E-11, 9.32E-11, 6.36E-14, 7.25E-11, 1.36E-05, 2.18E-15, 7.90E-11, 9.41E-15, 5.95E-14, 2.50E-13, 3.47E-17, 1.42E-14, 1.85E-17, 2.44E-10, 5.97E-18, 9.87E-12, 3.05E-12, 1.38E-09, 1.30E-13, 3.17E-14, 1.99E-15, 4.34E-07, 1.04E-05, 1.88E-15, 1.34E-13, 8.23E-08, 5.02E-12, 1.90E-09, 3.24E-12, 8.89E-11, 0.000142133, 3.00E-07, 3.60E-14, 5.95E-07, 4.59E-12, 2.48E-09, 3.98E-11, 5.59E-13, 4.13E-14, 1.77E-11, 4.88E-11, 3.83E-12, 1.11E-09, 3.21E-10, 1.68E-11, 7.09E-07, 1.12E-08, 1.88E-08, 8.16E-14, 2.87E-14, 5.17E-09, 5.11E-13, 1.43E-12, 4.19E-09, 4.03E-17, 6.34E-12, 2.63E-09, 1.55E-13, 4.85E-12, 4.49E-06, 7.34E-11, 2.82E-14, 1.82E-12, 1.93E-16, 3.10E-08, 1.64E-08, 1.32E-11, 6.31E-11, 6.48E-14, 2.55E-06, 1.60E-08, 1.58E-08, 8.22E-15, 9.19E-10, 3.12E-08, 1.77E-06, 5.43E-08, 8.03E-14, 3.05E-10, 1.71E-08, 3.57E-15, 1.11E-05, 1.18E-09, 4.99E-15, 6.74E-12, 3.83E-09, 7.73E-09, 2.22E-12, 2.60E-10, 2.83E-12, 6.35E-08, 1.56E-12, 1.10E-16, 1.87E-12, 1.45E-05, 3.44E-13, 8.25E-07, 1.91E-16, 1.79E-13, 2.33E-09, 2.55E-14, 4.51E-17, 8.90E-14, 8.76E-11, 4.16E-15, 2.58E-11, 2.37E-10, 5.16E-15, 3.03E-09, 1.34E-13, 9.61E-12, 9.16E-11, 2.82E-13, 1.28E-05, 5.30E-08, 5.67E-10, 5.09E-15, 1.51E-08, 2.89E-08, 6.98E-10, 6.88E-18, 3.43E-08, 6.38E-11, 7.24E-08, 1.74E-12, 2.76E-08, 1.88E-14, 2.47E-15, 1.21E-06, 8.16E-11, 1.28E-13, 6.72E-10, 9.04E-07, 9.98E-09, 1.57E-19, 7.33E-11, 8.03E-10, 1.50E-08, 4.12E-11, 8.33E-16, 5.56E-15, 2.94E-13, 1.70E-09, 4.45E-15, 2.35E-13, 4.66E-16, 1.06E-12, 6.40E-13, 8.26E-16, 2.27E-14, 8.47E-09, 6.10E-11, 1.12E-13, 3.94E-10, 1.92E-09, 4.25E-08, 8.84E-07, 4.39E-12, 4.19E-13, 5.32E-14, 4.02E-15, 1.51E-06, 1.19E-12, 4.72E-12, 7.05E-12, 2.06E-09, 3.04E-13, 3.42E-12, 2.12E-16, 9.74E-10, 8.71E-06, 4.55E-12, 2.69E-12)
While p-values range from 6.08038E-23 to 0.001134145, the bootstrapped p-value I get is 0.4995005 and I don't understand why. I am using the following function to find the bootstrapped p-value:
(1+sum(logit.boot$t[,2] > logit.boot$t0[2]))/(1+logit.boot$R)
where logit.boot$t[,2] takes on values from the p.values vector, logit.boot$t0[2] equals 2.664684e-11 and logit.boot$R = 1000.
EDIT
Here is the code I used for bootstrapping:
logit.bootstrap <- function(data, indices){
d <- data[indices, ]
Mf1 <- glm(Y ~ A + B + C, data = d, family = "binomial")
data.setM1 <- na.omit(d[, all.vars(formula(Mf1))])
M1.io <- glm(Y ~ A + B, data = data.setM1, family = "binomial")
my.test <- lrtest(Mf1, M1.io)
return(c(my.test$"Chisq"[2], my.test$"Pr(>Chisq)"[2]))
}
logit.boot <- boot(data=my.data, statistic=logit.bootstrap, R=1000) # 10'000 samples
In the result of the boot function, t0 should the p value on the original data, and t is some p values which are generated from random resampling/permutation on the original data.
And in your case, you shouldn't use
(1+sum(logit.boot$t[,2] > logit.boot$t0[2]))/(1+logit.boot$R)
to get information from your bootstrapped p values, you may use
quantile(logit.boot$t[,2], c(0.025,0.975))
or something like this to obtain a bootstrapped 95% confidence interval on your p value. This is not very meaningful, since the meaning of p value is already a probability (confidence level), why do you bother to obtain a confidence interval for p value? And the validness of the bootstrap method relies on the correctness of your parametric model. So if you want to use non-parametric approach toward this problem, I think you need to find some other approaches instead of this one.
Related
I don't know how to read the output when using ranef against the output of gamm in the mgcv package
id:268 levels group:10 levels Fitted a mixed-effects model using gamm in the mgcv package. Fitted a generalized additive model for age and bmi, but could not read the output of the random effect using ranef model: ilrgamm1 <-gamm(y~z1+z2+z3+s(age)+sex+s(bmi)+time,random=list(id=~1+time,group=~1),data=data,method = "REML") At this time, there are mysterious things like g and g.0 in the output of ranef names(ranef(ilrgamm1$lme)) [1] "g" "g.0" "id" "group" ranef(ilrgamm1$lme)[1:2] $g Xr1 Xr2 Xr3 Xr4 Xr5 Xr6 Xr7 Xr8 1 0.1130164 0.6108163 -0.1332607 0.4076337 -0.04366085 2.503919 -0.9792176 -0.5911858 $g.0 Xr.01 Xr.02 Xr.03 Xr.04 Xr.05 Xr.06 Xr.07 Xr.08 1/1 0.1983299 0.2758039 -1.100218 -0.4742126 -0.5449821 1.477916 -0.6329114 1.053759 What is Xr1 or Xr2? Furthermore, there are 268 random effects in group, not 10. $group (Intercept) 1/1/102/1 0.0172579674 1/1/103/1 0.0201196786 1/1/104/1 -0.0281116571 1/1/105/1 -0.0217217446 1/1/106/1 0.0124654493 1/1/108/1 -0.0282589006 1/1/109/1 -0.0499878886 1/1/110/1 0.0492600500 1/1/111/1 0.0507119068 1/1/113/1 0.0546332994 1/1/114/1 0.0393550975 1/1/115/1 -0.0148861329 1/1/116/1 0.0375339571 1/1/117/1 0.0148069805 1/1/118/1 -0.0351320894 1/1/119/1 -0.1195068445 1/1/120/1 -0.1160370216 1/1/121/1 0.0473366382 1/1/122/1 -0.0111156856 1/1/123/1 0.0076577605 1/1/124/1 -0.0042122818 1/1/125/1 0.0249031339 1/1/126/1 -0.1207996724 1/1/127/1 0.0275137051 1/1/128/1 -0.0004621387 1/1/130/1 -0.0080189325 1/1/132/1 -0.0147162203 1/1/133/1 0.0019108355 1/1/134/1 0.0048134559 1/1/135/1 -0.0275929191 1/1/136/1 0.0024070977 1/1/138/1 -0.0364971159 1/1/139/1 -0.0250644476 1/1/140/1 -0.0161684667 1/1/143/1 0.0097684438 1/1/144/1 -0.0254024942 1/1/145/1 -0.0308170535 1/1/146/1 -0.0314913020 1/1/147/1 0.0047849092 1/1/148/1 0.0398563674 1/1/149/1 -0.0328543231 1/1/201/2 -0.0386289339 1/1/202/2 -0.0164038050 1/1/203/2 -0.0310222871 1/1/204/2 -0.0465893084 1/1/206/2 -0.0639166021 1/1/207/2 0.0178124681 1/1/208/2 -0.0215777533 1/1/209/2 -0.0008097909 1/1/211/2 -0.0276369553 1/1/218/2 -0.0233586483 1/1/219/2 -0.0381510950 1/1/220/2 -0.0245044572 1/1/221/2 0.0257439303 1/1/222/2 -0.0526194229 1/1/223/2 -0.0598638388 1/1/224/2 -0.0564427102 1/1/226/2 -0.0682312455 1/1/227/2 0.0025178471 1/1/228/2 0.0050413163 1/1/229/2 0.0006566180 1/1/230/2 -0.0394159991 1/1/233/2 -0.0339136266 1/1/234/2 -0.0355879691 1/1/235/2 0.0264388355 1/1/236/2 -0.0190059575 1/1/237/2 -0.0466046545 1/1/238/2 -0.0103843873 1/1/239/2 0.0030630609 1/1/242/2 -0.0385347399 1/1/246/2 -0.0233604289 1/1/247/2 -0.0549077802 1/1/249/2 -0.0309410264 1/1/250/2 -0.0138412118 1/1/251/2 -0.0236995292 1/1/252/2 -0.0263367786 1/1/253/2 -0.0158340565 1/1/254/2 -0.0003306973 1/1/255/2 -0.0106150344 1/1/256/2 -0.0223922258 1/1/258/2 0.0042958519 1/1/301/3 0.1100838962 1/1/302/3 0.0240153141 1/1/303/3 0.0403893185 1/1/306/3 0.0483381436 1/1/307/3 -0.0129870303 1/1/309/3 0.0173975588 1/1/310/3 -0.0189250961 1/1/313/3 0.0357035256 1/1/315/3 0.0012214394 1/1/316/3 0.0325373842 1/1/317/3 -0.0085589305 1/1/319/3 0.0524899049 1/1/321/3 0.0416124283 1/1/322/3 0.0095534385 1/1/325/3 0.0321591953 1/1/326/3 -0.0054073693 1/1/327/3 0.0050364482 1/1/328/3 0.0531385640 1/1/331/3 0.0232251446 1/1/332/3 0.0189221949 1/1/333/3 -0.0181158192 1/1/334/3 -0.0359340965 1/1/335/3 0.0083524511 1/1/336/3 -0.0118781160 1/1/337/3 -0.0085829648 1/1/338/3 0.0095829746 1/1/401/4 -0.0512378233 1/1/402/4 -0.0219261499 1/1/403/4 -0.0160446585 1/1/407/4 0.0017872369 1/1/408/4 -0.0371254332 1/1/409/4 0.0303154843 1/1/411/4 -0.0026150821 1/1/412/4 0.1418719283 1/1/414/4 -0.0556019328 1/1/415/4 0.0073027068 1/1/416/4 -0.0122557311 1/1/417/4 0.0367134933 1/1/418/4 -0.0253763258 1/1/419/4 -0.0203686506 1/1/421/4 -0.0187932155 1/1/422/4 -0.0189659510 1/1/423/4 -0.0306159126 1/1/424/4 0.0273308724 1/1/425/4 0.0040657657 1/1/426/4 0.0312199779 1/1/429/4 0.0036135869 1/1/430/4 -0.0256442792 1/1/433/4 0.0438767257 1/1/434/4 0.0150299855 1/1/435/4 -0.0058240553 1/1/436/4 0.0028309330 1/1/437/4 -0.0023443246 1/1/438/4 0.0115472464 1/1/439/4 -0.0071635162 1/1/441/4 -0.0187692003 1/1/442/4 -0.0301687031 1/1/443/4 -0.0054707553 1/1/501/5 0.0233900218 1/1/502/5 0.0270437356 1/1/503/5 -0.0505494678 1/1/504/5 -0.0555547708 1/1/506/5 -0.0232974224 1/1/508/5 -0.0316901016 1/1/510/5 0.0498275109 1/1/511/5 0.0140125034 1/1/513/5 -0.1284098189 1/1/514/5 0.0336408919 1/1/515/5 -0.0328592365 1/1/516/5 -0.0264024730 1/1/601/6 -0.0064048726 1/1/602/6 0.0136098007 1/1/603/6 0.0437196138 1/1/604/6 0.0685239133 1/1/605/6 -0.0141230573 1/1/606/6 0.0555226687 1/1/607/6 -0.0411745650 1/1/608/6 0.0219745785 1/1/609/6 -0.0045706685 1/1/610/6 -0.0176662070 1/1/611/6 0.0408741543 1/1/612/6 0.0187626096 1/1/613/6 0.0561545743 1/1/614/6 0.0284241671 1/1/615/6 0.0157012751 1/1/616/6 0.0496079608 1/1/701/7 -0.0398327297 1/1/702/7 -0.0140910866 1/1/705/7 0.0286548362 1/1/706/7 0.0369761615 1/1/708/7 0.0116733825 1/1/709/7 0.0001330362 1/1/710/7 0.0274371733 1/1/711/7 0.0090225922 1/1/712/7 0.0765875063 1/1/713/7 0.0148952419 1/1/714/7 -0.0054933850 1/1/716/7 0.0043641233 1/1/717/7 -0.0119174808 1/1/719/7 0.0010953154 1/1/723/7 -0.0371240564 1/1/801/8 0.0636698316 1/1/803/8 0.0246677751 1/1/804/8 -0.0445965919 1/1/806/8 -0.0289816619 1/1/807/8 0.0076561215 1/1/808/8 0.0237686430 1/1/809/8 0.0450896739 1/1/810/8 0.0149585857 1/1/811/8 0.0075693911 1/1/812/8 0.0085475577 1/1/813/8 -0.0136763527 1/1/814/8 0.0117384418 1/1/815/8 0.0067855948 1/1/816/8 0.0140344652 1/1/817/8 0.0103800524 1/1/818/8 -0.0361848876 1/1/819/8 0.0449431626 1/1/820/8 -0.0092320086 1/1/822/8 -0.0404730405 1/1/823/8 -0.0494073578 1/1/824/8 -0.0029941736 1/1/825/8 -0.0145742585 1/1/826/8 -0.0314564014 1/1/828/8 0.0183565957 1/1/829/8 0.0288121410 1/1/830/8 0.0286684412 1/1/831/8 0.0059331890 1/1/832/8 0.0341139486 1/1/833/8 0.0386864016 1/1/834/8 0.0147205534 1/1/835/8 -0.0031409478 1/1/901/9 0.0660687434 1/1/902/9 0.0564001190 1/1/903/9 0.0756466936 1/1/904/9 0.0096398307 1/1/905/9 0.0221015690 1/1/906/9 0.0046220720 1/1/907/9 0.0412366347 1/1/908/9 0.0284303878 1/1/909/9 0.0452359853 1/1/910/9 -0.0195940019 1/1/911/9 -0.0154676475 1/1/912/9 0.0184574647 1/1/913/9 0.0460445032 1/1/914/9 -0.0067133484 1/1/915/9 -0.0087355534 1/1/916/9 -0.0043938763 1/1/917/9 -0.0470434649 1/1/919/9 0.0794927553 1/1/920/9 0.0555903561 1/1/921/9 -0.0036186615 1/1/922/9 0.0078238313 1/1/923/9 0.0143975055 1/1/924/9 0.0731162776 1/1/925/9 -0.0065668921 1/1/926/9 0.0549429919 1/1/927/9 0.0368946293 1/1/928/9 0.0247474240 1/1/929/9 -0.0404517417 1/1/930/9 -0.0076552298 1/1/1001/10 0.0117082112 1/1/1002/10 0.0068444544 1/1/1003/10 0.0327977955 1/1/1004/10 0.0071551344 1/1/1005/10 -0.0052717304 1/1/1006/10 0.0483668189 1/1/1007/10 -0.0167403419 1/1/1008/10 -0.0364566907 1/1/1009/10 -0.0254350538 1/1/1010/10 0.0504571167 1/1/1011/10 -0.0039537094 1/1/1012/10 -0.0054692797 1/1/1013/10 0.0224140597 1/1/1014/10 -0.0310392331 1/1/1015/10 -0.0498130767 1/1/1016/10 -0.0223939677 1/1/1017/10 0.0041103780 1/1/1018/10 0.0880528857 1/1/1019/10 -0.0467056887 1/1/1022/10 -0.0769873686 1/1/1025/10 -0.0229126779 1/1/1028/10 -0.0340772236 1/1/1029/10 -0.0251866535 1/1/1030/10 0.0307034344 1/1/1031/10 -0.0369165146 1/1/1035/10 -0.0056637857 Why not 10 types, which is the number of levels in group?
Is there a reason you're using gamm() instead of gam()? Also, why list time as both a fixed and random effect? If there aren't reasons for these choices, then one possible solution could be to fit your model as ilrgamm1 <- gam(y~z1+z2+z3+s(age)+sex+s(bmi)+s(time, bs="re"), data=data, method = "REML") - the s(time, bs="re") is indicating that time is a random effect. Then you can use summary(ilrgamm1) to look at your results, and partial effects plots in plot.gam() or with ggpredict to visualize trends in your smoothed variables.
Fitting distributions with R
Good afternoon. I have a vector 'a' containing 16000 values. I get the descriptive statistics with the help of the following: library(pastecs) library(timeDate) stat.desc(a) skewness(a) kurtosis(a) Especially skewness=-0.5012, kurtosis=420.8073 (1) Then I build a histogram of my empirical data: hist(a, col="lightblue", breaks = 140, border="white", main="", xlab="Value",xlim=c(-0.001,0.001)) After this I try to fit a theoretical distribution to my empirical data. I choose Variance-Gamma distribution and try to get its parameter estimates on my data: library(VarianceGammma) a_VG<-vgFit(a) The parameter estimates are the following: vgC=-11.7485, sigma=0.4446, theta=11.7193, nu=0.1186 (2) Further, I create a sample from the Variance-Gamma distribution with the parameters from (2) and build a histogram of created theoretical values: VG<-rvg(length(a),vgC=-11.7485,sigma=0.4446,theta=11.7193,nu=0.1186) hist(VG,breaks=140,col="orange",main="",xlab="Value") Bu the second histogram differs absolutely from the first (empirical) histogram. Moreover, it is built on the basis of the parameters (2), which I got on the empirical data. What's wrong with my code? How can I fix it? P.S. When I type dput(a[abs(a) > 5e-4]) I get: c(0.000801110480004752, 0.000588162271316861, 0.000555169128569233, 0.000502563410256229, 0.000854633994686438, 0.00593622112246628, -0.000506168123513007, -0.000502909585836875, 0.000720924373137422, 0.00119141739181039, 0.000548159382141478, -0.000516511318695123, -0.000744590777740584, 0.000595213912401249, 0.000514055190913965, -0.000589061375421807, -0.00175392114572581, 0.000745548313668465, -0.00075910234096277, -0.00059987613053103, 0.000583568488865538, 0.00426484136013094, 0.000610760059768012, 0.000575522836335551, 0.000823785810599276, 0.00181936036509178, -0.00073316272551871, -0.00184238143420679, -0.000519146793923397, -0.00120324664043103, -0.000882469414168696, -0.00148118339830283, 0.000929612782487155, 0.000565364610238817, 0.000578158613453894, 0.00060479145432879, -0.00520576206828594, 0.000708404040882016, 0.00105224485893451, 0.000636486872540587, -0.00359655507585543, 0.000769164650506582, 0.000635701125126786, 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0.00160093110088155, 0.000785262438315115, -0.000912313442922752, 0.000609996052359563, 0.000720137994393966, 0.000568163899000496, 0.00128685533068307, -0.000756787473447318, 0.000765932134255465, 0.00064884753100003, 0.000687571386270847, -0.000582094290400903, -0.000693177295971736, -0.000601776208094762, 0.000503616387996786, -0.000615095866544735, -0.000799593899689199, 0.000773750859128342, -0.000522576090260074, 0.000503578107212022, -0.00104492224837571, 0.000547928732299141, 0.00310304337507183, 0.000893382870797765, -0.000577792878910799, -0.000647710366578735, -0.00061992948706191, 0.000825702487162516, 0.000606579510524341, 0.000552792484727505, 0.000688600840895504, 0.000505093563534231, -0.000728420573667066, -0.00157924525963438, -0.000603846616019865, -0.000521941317177976, 0.00150498158245682, -0.000584572670337735, 0.000713757870583365, 0.000524287801789924, 0.00107217649464886, 0.00213147531822244, 0.000566012832157625, -0.00069828890607937, 0.000641567963736378, -0.000509531713644762, -0.000547564140049417, -0.00115275240244728, 0.000560465768010943, -0.000651807371497171, -0.00096487058986483, 0.000753687665266511, -0.000665599418910645, -0.000691278087025182, -0.000578010050725553, -0.000685833148198256, 0.000698470819832764, 0.00102943368139208, -0.000725840586788706, 0.00125882415960632, -0.000630791474954151, -0.000764813558678412, -0.000638539347184164, 0.000654486496518558, 0.000547453642294471, 0.000572020020495501, -0.000605791001705214, 0.00660211658324172, 0.00114928683282756, 0.000985676480677711, -0.000694668292547718, -0.000528955637964401, 0.000647975568638159, 0.00116454536417443, 0.000506748841724303, -0.000500925156604382, -0.000567015088082101, 0.00128711230206946, 0.000533633762033858, 0.00505991432758357, 0.000518058378462527, -0.000592822519784875, 0.00177414999018666, 0.00059845426944527, -0.000511614433724716, 0.0016614697907098, 0.000852196464322219, 0.00241689725305427, -0.000614317948913978, -0.000729717143318709, -0.000612900648802039, -0.000727983564232204, -0.000694965869158182, -0.000527752006066251, -0.000584233784708843, 0.000522097476268968, 0.000543092880677776, 0.000947121210698398, -0.00241810275096377, 0.00181893137435019, 0.000931873879297385, 0.000512116215015013, 0.000724985702444059, -0.000566713495050664, 0.000603953591362227 ) After fitting the data look like the following (empirical histogram-blue, theoretical histogram-orange): The same when include freq=FALSE in hist
This will all be due to anomalous values in a not represented by the histogram you've shown. This could be the cause of both the very high kurtotsis, and the vgFit() algorithm failing to find a good fit. Type dput(a[abs(a) > 5e-4]) in the console and copy the output into your question. People then may be able to recreate aomething like the vector a without having to get all 16000 values and debug the vgFit issue. Thanks for the extra data. There are some extreme values in there, but I don;t think those are what is causing the problem in vgFit. Fitting 4 parameters which can be almost any value is difficult, but you can help it along by rescaling your data to something typical. Try this: b <- (a-mean(a))/sd(a) vgf <- vgFit(b) vgf$param VG <- rvg(16000, param = vgf$param) VG_rescaled <- VG*sd(a)+mean(a) hist(VG_rescaled, breaks=140, col="orange", main="", xlab="Value") and see if the two histograms are close enough now.
R: The 61928th question about the "singular gradient matrix at initial parameter estimates" error message
I have the following data:
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To which I want to fit the function
While the formula is kind of a beast, it has physical meaning, so I would like to not change it.
I have the following code:
index_min <- which(mydf[,2] == min(mydf[,2]))[1]
n0start <- -119
n1start <- 16
df0start <- 120
df1start <- 1
f0start <- mydf[index_min,1]
f1start <- mydf[index_min,1]
plot(x=mydf[,1],y=mydf[,2])
eq = function(f,n0, n1, f0, f1, df0, df1){ n0+n1*4*(f-f1)^2/(4*(f-f1)^2+(4*((f-f0)/df0)*(f-f1)-df1)^2)}
lines(mydf[,1], eq(mydf[,1],n0start, n1start, f0start, f1start, df0start, df1start), col="red" )
res <- try(nlsLM( y ~ n0+n1*4*(f-f1)^2/(4*(f-f1)^2+(4*((f-f0)/df0)*(f-f1)-df1)^2),
start=c(n0=n0start, n1=n1start,f0=f0start,df0=df0start,f1=f1start,df1=df1start) , data = mydf))
coef(res)
As you can see, the starting values look rather decent, but I get the "singular gradient matrix at initial parameter estimates" error. I have looked through all the other posts, however, I don't see why my formula is overdetermined or why the starting values should be bad.
Okay, I figured out the mistake. nlsLM requires data to be a data-frame and not just a bare matrix. The error message is simply misleading.
How to fit a loess curve over this decomposed time series data in R?
We have time series data with some seasonality from the past 4 years. We want to predict the general rise in trend next year. For this, we decomposed the time series and observed the trend line:
However, this trend line is placed in the middle of the values rather than the values themselves. We are not satisfied with simply extrapolating this trend line since it falls very short of expected traffic.
Since we are interested in only the general rise in trend and not the seasonality, we remove the seasonality from the components:
mydata <- read.csv("values.csv")
mydataseries <- ts(mydata, start=c(2012,1,1),frequency = 365.25
mydataseriescomponents <- decompose(mydataseries)
mydataseriesminusseasons <- mydataseries - mydataseriescomponents$seasonal
We are now trying to fit a loess() curve in R around this time series data minus the seasonal component, that is mydataseriesminusseasons:
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However, we are not familiar with the form of the loess() function and it seems to expect a formula which we do not know. We tried the following:
y <- c(mydata)
x <- 1:1614
lo <- loess(y~x)
We get the error: Error in model.frame.default(formula = y ~ x) :
invalid type (list) for variable 'y'
How do we fit a loess curve around the values to get the general trend line? Also, after removing the seasonal component we have some negative values for some reason. Would this affect the loess() curve?
Resampling multivariate time series in R
I have a set of bivariate time series and want to resample them to the same length. I thought that it might be a good idea to use the mean length of all time series, since I wanted to avoid that some series will be shortened or extended too much. My first naive idea was using the resample method of the signal package, and resample each dimension of a series separately. I don't like this idea very much, since I have the fear that the alignment between the dimensions might suffer from the independent resampling, but I hoped this effect might be negligible. My current problem is that the resampling of the data creates artifacts at the end and beginning of a series. It seems that the resample method assumes that the series starts from zero. Now I'm wondering if there is a more suitable resampling method which in the best case is readily available (in R) and maybe also supports bivariate time series. Plot: http://i.stack.imgur.com/3OwAt.png Code example: example <- c(-2014.1, -2014.1, -2014.1, -2014, -2014, -2013.9, -2013.9, -2013.7, -2013.5, -2013.4, -2013.1, -2012.9, -2012.6, -2012.4, -2012, -2011.7, -2011.4, -2011, -2010.5, -2010.1, -2009.5, -2009.1, -2008.6, -2008, -2007.5, -2006.9, -2006.4, -2005.7, -2005.1, -2004.4, -2003.7, -2003, -2002.4, -2001.5, -2000.7, -1999.9, -1999.1, -1998.2, -1997.4, -1996.5, -1995.6, -1994.6, -1993.6, -1992.6, -1991.7, -1990.7, -1989.7, -1988.9, -1987.9, -1986.9, -1985.9, -1984.9, -1984, -1983, -1982.1, -1981.1, -1980.1, -1979.2, -1978.2, -1977.2, -1976.4, -1975.4, -1974.4, -1973.4, -1972.4, -1971.5, -1970.5, -1969.6, -1968.6, -1967.6, -1966.6, -1965.7, -1964.7, -1963.9, -1962.9, -1961.9, -1960.9, -1959.9, -1958.9, -1957.9, -1956.9, -1955.9, -1955, -1954, -1953, -1952.1, -1951.5, -1951.1, -1950.6, -1950.1, -1949.6, -1949.2, -1948.7, -1948.2, -1947.9, -1947.4, -1946.9, -1946.4, -1945.9, -1945.4, -1944.9, -1944.4, -1943.9, -1943.4, -1943, -1942.5, -1942, -1941.5, -1941, -1940.6, -1940.1, -1939.6, -1939.1, -1938.7, -1938.2, -1937.7, -1937.2, -1936.7, -1936.2, -1935.7, -1935.4, -1934.9, -1934.4, -1933.9, -1933.4, -1932.9, -1932.4, -1931.9, -1931.4, -1931, -1930.5, -1930, -1929.5, -1929, -1928.6, -1928.1, -1927.6, -1927.1, -1926.6, -1926.2, -1925.7, -1925.2, -1924.7, -1924.4, -1923.9, -1923.4, -1922.9, -1922.4, -1921.9, -1921.4, -1920.9, -1920.4, -1919.9, -1919.4, -1919, -1918.5, -1918, -1917.5, -1917, -1916.6, -1916.1, -1915.6, -1915.1, -1914.6, -1914.2, -1913.7, -1913.2, -1912.7, -1912.2, -1911.7, -1911.4, -1910.9, -1910.4, -1909.9, -1909.4, -1908.9, -1908.4, -1907.9, -1907.5, -1906.9, -1906.5, -1906, -1905.5, -1905, -1904.6, -1904, -1903.6, -1903.1, -1902.7, -1902.2, -1901.7, -1901.2, -1900.7, -1900.4, -1899.9, -1899.4, -1898.9, -1898.4, -1897.9, -1897.4, -1896.9, -1896.4, -1895.9, -1895.2, -1895, -1894.5, -1894, -1893.6, -1893.1, -1892.6, -1892.1, -1891.6, -1891.2, -1890.7, -1890.2, -1889.7, -1889.2, -1888.9, -1888.4, -1887.9, -1887.4, -1886.9, -1886.4, -1885.6, -1885.4, -1884.9, -1884.5, -1884, -1883.5, -1883, -1882.5, -1882.1, -1881.6, -1881.1, -1880.6, -1880.2, -1879.7, -1879.2, -1878.7, -1878.4, -1877.9, -1877.4, -1876.9, -1876.1, -1875.6, -1875.1, -1874.6, -1874.1, -1873.6, -1873.2, -1872.7, -1872.2, -1871.7, -1871.4, -1870.7, -1870.4, -1869.9, -1869.4, -1868.9, -1868.4, -1867.9, -1867.4, -1867.2, -1866.4, -1866, -1865.5, -1865, -1864.5, -1864.1, -1863.6, -1863.1, -1862.6, -1862.1, -1861.7, -1861.2, -1860.7, -1860.2, -1859.7, -1859.4, -1858.9, -1858.4, -1857.9, -1857.4, -1856.9, -1856.4, -1855.9, -1855.4, -1854.9, -1854.5, -1854, -1853.5, -1853.1, -1852.6, -1852.1, -1851.6, -1851.1, -1850.7, -1850.2, -1849.7, -1849.2) plot(example, t="l", main="Original") plot(resample(example,250,length(example)), t="l", main="Resampled")