I have a vector that I want to transform into a probability density function. The mean is 1. How do I plot this?
The vector:
x <-
c(0.7601401, 0.8607037, 0.8748152, 0.885415, 0.8904619, 0.899021,
0.9034128, 0.9050411, 0.9093876, 0.9141021, 0.9172803, 0.9209636,
0.9238607, 0.9268591, 0.9293789, 0.9313833, 0.9335163, 0.9360798,
0.9406245, 0.9427261, 0.9441703, 0.9473808, 0.9502454, 0.9518683,
0.9540568, 0.955987, 0.9580035, 0.9617511, 0.9635325, 0.964507,
0.9674928, 0.9692979, 0.9705296, 0.9732977, 0.9754498, 0.977204,
0.9793093, 0.9821249, 0.9841156, 0.9864521, 0.9873941, 0.9883275,
0.9904071, 0.9920552, 0.9946789, 0.9967097, 0.997695, 0.9992215,
1.001643, 1.0038606, 1.006269, 1.0077312, 1.0091087, 1.0100767,
1.0113615, 1.0124576, 1.0154025, 1.017386, 1.0189122, 1.021932,
1.0238598, 1.0258631, 1.0273012, 1.0294901, 1.031085, 1.0336801,
1.0371085, 1.0387533, 1.0406862, 1.0436292, 1.0453442, 1.0471563,
1.0514885, 1.0531803, 1.055339, 1.059578, 1.0643068, 1.0668389,
1.0694237, 1.073174, 1.0759322, 1.0786821, 1.0846407, 1.0904819,
1.0968733, 1.1039872, 1.1081845, 1.1144191, 1.124116, 1.1378536,
1.1631801, 0.8238456, 0.8621417, 0.8750536, 0.8864652, 0.8913426,
0.899054, 0.9034444, 0.9052496, 0.9096515, 0.9141042, 0.9174039,
0.9215185, 0.9240734, 0.9272829, 0.9294991, 0.9315397, 0.9335967,
0.9370766, 0.9408574, 0.9427551, 0.9444246, 0.9474299, 0.9503871,
0.9520593, 0.9541102, 0.9560759, 0.9586173, 0.9618885, 0.9636027,
0.9653304, 0.9677295, 0.9693085, 0.9706549, 0.9735909, 0.9757686,
0.9772445, 0.9795081, 0.9823502, 0.9843492, 0.9866112, 0.9874782,
0.9883432, 0.9904612, 0.99227, 0.9948917, 0.9968164, 0.9979785,
0.999409, 1.0017522, 1.0038956, 1.0064009, 1.007936, 1.0092714,
1.0101577, 1.0113745, 1.0124722, 1.015455, 1.0174442, 1.0190047,
1.0221244, 1.0241163, 1.0262672, 1.0274717, 1.0295358, 1.0311976,
1.0337105, 1.0376287, 1.0391993, 1.0412049, 1.043784, 1.0458161,
1.0471989, 1.0515136, 1.0532311, 1.0553901, 1.0598511, 1.0647286,
1.0674053, 1.0695112, 1.0731966, 1.0765993, 1.0804314, 1.0846581,
1.0915069, 1.0983415, 1.1041094, 1.1087707, 1.1153954, 1.1244647,
1.1387218, 1.1631806, 0.8268138, 0.8625675, 0.8751377, 0.886809,
0.8920388, 0.8991269, 0.9034886, 0.9060083, 0.9102798, 0.9143602,
0.9178468, 0.9221248, 0.9241217, 0.9273233, 0.929575, 0.9316477,
0.9337809, 0.9374138, 0.9410309, 0.9429906, 0.9455161, 0.9475521,
0.9506105, 0.9522978, 0.9541801, 0.9567138, 0.9588403, 0.961889,
0.9637248, 0.9658144, 0.9678051, 0.9694083, 0.9708232, 0.9737623,
0.9757794, 0.9772462, 0.9799384, 0.9826819, 0.9844438, 0.9866957,
0.9875501, 0.9884927, 0.9905356, 0.992504, 0.9952807, 0.9970651,
0.9979877, 0.9996339, 1.0018148, 1.0039266, 1.0064778, 1.0080148,
1.0093728, 1.0101958, 1.0115577, 1.0128605, 1.0155767, 1.0176005,
1.0191159, 1.0222069, 1.0241286, 1.0263724, 1.0275547, 1.0298965,
1.0314567, 1.0347959, 1.0377489, 1.0392566, 1.0413151, 1.043814,
1.0462065, 1.0472742, 1.0516567, 1.0533239, 1.0556568, 1.0599784,
1.0648656, 1.0674582, 1.0695187, 1.0737822, 1.0767733, 1.0805175,
1.085116, 1.0919541, 1.0987864, 1.1045691, 1.1090898, 1.1155974,
1.1244743, 1.1403648, 1.1653051, 0.8287487, 0.8632563, 0.8783957,
0.8872595, 0.8921496, 0.8991388, 0.9038431, 0.9063199, 0.9106785,
0.9144, 0.9189027, 0.9223262, 0.924352, 0.9275484, 0.9296723,
0.932303, 0.9340644, 0.9375086, 0.9410767, 0.9431117, 0.9455282,
0.9476748, 0.9506839, 0.9524355, 0.9542676, 0.9570338, 0.9591047,
0.9620121, 0.9638592, 0.9660401, 0.9678991, 0.9695856, 0.9710773,
0.9740787, 0.9760428, 0.9773099, 0.9800677, 0.9830478, 0.9845491,
0.9868047, 0.9876641, 0.9885819, 0.990765, 0.9929082, 0.9953852,
0.9972524, 0.9980094, 0.999655, 1.0019781, 1.0041123, 1.0065022,
1.0080436, 1.0093745, 1.0102597, 1.011591, 1.0133388, 1.0160004,
1.0177403, 1.0197461, 1.0223301, 1.0243601, 1.0264419, 1.0277154,
1.0300746, 1.0315714, 1.0348406, 1.0377535, 1.0396123, 1.0416248,
1.0438679, 1.0463796, 1.0473053, 1.0518621, 1.0535013, 1.0566508,
1.0602571, 1.0649945, 1.0675837, 1.0696383, 1.0737915, 1.0768286,
1.0807683, 1.0866947, 1.0922428, 1.0993173, 1.1053873, 1.1097462,
1.1160662, 1.1245894, 1.1439087, 1.1653756, 0.8336881, 0.8641065,
0.8801013, 0.8873061, 0.892528, 0.8992721, 0.9040462, 0.9064932,
0.9118009, 0.9147806, 0.9194353, 0.922346, 0.924704, 0.9279243,
0.9298283, 0.9325862, 0.9345334, 0.9376213, 0.9413217, 0.9435098,
0.9457126, 0.948086, 0.9507289, 0.9526298, 0.9544503, 0.9570495,
0.9594515, 0.9622223, 0.9639176, 0.9664818, 0.9681109, 0.9697204,
0.9715772, 0.974512, 0.9761773, 0.9774122, 0.9801115, 0.9830508,
0.9848412, 0.9868423, 0.987876, 0.9886175, 0.9911154, 0.9930527,
0.995429, 0.9972859, 0.9980303, 1.000341, 1.0023467, 1.0041273,
1.0066877, 1.0081464, 1.0094208, 1.0103294, 1.0117416, 1.0134278,
1.0162053, 1.0179561, 1.0202328, 1.0227929, 1.0244661, 1.0266619,
1.0278932, 1.0301724, 1.0318422, 1.034844, 1.0378449, 1.0396893,
1.0416388, 1.0441611, 1.0464143, 1.0485936, 1.0520624, 1.0535133,
1.0568916, 1.0602833, 1.0652996, 1.0678024, 1.0700347, 1.0739087,
1.0768747, 1.0811584, 1.08706, 1.092342, 1.0994397, 1.1057555,
1.1101622, 1.1197734, 1.1260845, 1.144113, 1.1656149, 0.8364483,
0.8665228, 0.8801799, 0.8876104, 0.8951142, 0.9004983, 0.9041605,
0.9067967, 0.9123546, 0.9151016, 0.9195613, 0.9224414, 0.9250538,
0.9280133, 0.9299708, 0.9326035, 0.9346515, 0.9380998, 0.9414107,
0.9435515, 0.9461568, 0.9482194, 0.9508463, 0.9527514, 0.9546165,
0.9571445, 0.9596505, 0.9624602, 0.963954, 0.9665085, 0.9682382,
0.9699198, 0.9716006, 0.974844, 0.9761914, 0.9775793, 0.9801981,
0.9835029, 0.9848844, 0.9869101, 0.9878904, 0.988621, 0.9913567,
0.9933158, 0.9955091, 0.9973969, 0.9982784, 1.0004438, 1.0024634,
1.0043027, 1.006941, 1.0081515, 1.0094608, 1.0103653, 1.0117473,
1.0142556, 1.0165854, 1.0181703, 1.0205428, 1.022822, 1.0245166,
1.02681, 1.0278956, 1.0302434, 1.032, 1.0355287, 1.037906, 1.0401162,
1.041886, 1.0442367, 1.0464702, 1.0486031, 1.0521055, 1.0540157,
1.0570109, 1.0605447, 1.0654882, 1.0679469, 1.0700915, 1.0746908,
1.076962, 1.0811779, 1.0880499, 1.0925217, 1.1000415, 1.1068352,
1.1103446, 1.1198258, 1.1278557, 1.144615, 1.1659478, 0.8428518,
0.8685433, 0.8806388, 0.8878492, 0.8957898, 0.9008499, 0.9041908,
0.907718, 0.9124535, 0.9154017, 0.9195873, 0.9228693, 0.9252186,
0.9280587, 0.9302431, 0.9327066, 0.9346733, 0.9382416, 0.9415437,
0.943858, 0.9463773, 0.9482644, 0.9510351, 0.9529947, 0.9548508,
0.9571599, 0.9597816, 0.9632479, 0.9640826, 0.9667552, 0.9688842,
0.9699332, 0.972455, 0.9748674, 0.9765135, 0.9782475, 0.9804558,
0.9835081, 0.9849378, 0.9869651, 0.9879891, 0.9886996, 0.9913707,
0.9933895, 0.9958194, 0.9974753, 0.9984636, 1.0005798, 1.0027019,
1.0043733, 1.007001, 1.008266, 1.0097909, 1.0104785, 1.0119673,
1.0145991, 1.0166569, 1.0182844, 1.0211453, 1.0231898, 1.0245254,
1.0268803, 1.0279336, 1.0304433, 1.0322581, 1.035589, 1.0382374,
1.0401349, 1.0422173, 1.0444101, 1.0465514, 1.0486237, 1.0521174,
1.054423, 1.057825, 1.0606672, 1.0656468, 1.0684325, 1.0709319,
1.0749488, 1.0770775, 1.0820547, 1.0881332, 1.0925439, 1.1000816,
1.1069162, 1.1106217, 1.120701, 1.1302275, 1.1459233, 1.168602,
0.8488214, 0.8702316, 0.8809238, 0.8890422, 0.8958309, 0.9019966,
0.9042307, 0.9079107, 0.9132285, 0.9154252, 0.9198471, 0.9230071,
0.9253286, 0.9283751, 0.9304839, 0.9327088, 0.9348709, 0.9382659,
0.9417193, 0.943864, 0.9466926, 0.9485074, 0.9511567, 0.9532046,
0.9554877, 0.9574181, 0.9600951, 0.963362, 0.9643226, 0.9667904,
0.9689357, 0.9699377, 0.9726418, 0.9749534, 0.9766888, 0.9786503,
0.9816446, 0.9836604, 0.9850502, 0.9869678, 0.9880833, 0.9894534,
0.9914227, 0.9937725, 0.9962026, 0.9975144, 0.9987734, 1.0006365,
1.0029989, 1.0047337, 1.0071024, 1.0086273, 1.0098207, 1.0110381,
1.0119714, 1.01463, 1.0166717, 1.0183924, 1.0212439, 1.0234931,
1.0245751, 1.0269182, 1.0286172, 1.0306589, 1.0322592, 1.0359487,
1.038269, 1.0402549, 1.0422949, 1.0445157, 1.0466529, 1.0487325,
1.0523088, 1.0546675, 1.0584369, 1.0618338, 1.0658283, 1.0687319,
1.0712981, 1.0750357, 1.0777228, 1.082814, 1.0896652, 1.092878,
1.1002284, 1.1072211, 1.1112157, 1.1210507, 1.1331503, 1.1510694,
1.1729349, 0.8533762, 0.8705291, 0.882901, 0.8898704, 0.8964921,
0.9026909, 0.9043496, 0.9082536, 0.9132482, 0.9167392, 0.9200439,
0.923801, 0.9257428, 0.9287926, 0.9310369, 0.9327569, 0.935825,
0.9396949, 0.9418288, 0.9439139, 0.9468012, 0.9487367, 0.9513136,
0.9534568, 0.9557201, 0.9577258, 0.9606246, 0.9634036, 0.9644277,
0.9668756, 0.9689947, 0.9699737, 0.9726901, 0.9749863, 0.9768128,
0.9790117, 0.9817103, 0.9837448, 0.9853122, 0.98713, 0.9881006,
0.9896077, 0.9918675, 0.9939561, 0.9962435, 0.9975984, 0.9988964,
1.0007611, 1.0032765, 1.0052762, 1.0072548, 1.008838, 1.0098927,
1.011057, 1.0120007, 1.0153072, 1.0167114, 1.0183976, 1.0217836,
1.0235315, 1.0250021, 1.0270587, 1.0287792, 1.0308106, 1.032815,
1.0363681, 1.0384448, 1.0403337, 1.0423298, 1.0446048, 1.046717,
1.0487547, 1.0527611, 1.0548012, 1.0586172, 1.0622363, 1.0665121,
1.0690857, 1.0720846, 1.0755489, 1.0784088, 1.0840459, 1.089944,
1.093356, 1.1004089, 1.1073085, 1.1124143, 1.1211267, 1.1339292,
1.1517573, 1.198363, 0.8571564, 0.8726188, 0.8845936, 0.8898835,
0.8980876, 0.9028112, 0.9044216, 0.9086289, 0.9133134, 0.9168552,
0.9203877, 0.9238087, 0.926413, 0.929035, 0.9313117, 0.9330642,
0.9358536, 0.9400013, 0.9420459, 0.9439535, 0.9469293, 0.9491097,
0.9513609, 0.9535177, 0.9557438, 0.9577505, 0.9608692, 0.9634749,
0.9644502, 0.9669999, 0.9690555, 0.9703173, 0.9727357, 0.9750629,
0.9769616, 0.9790537, 0.9817755, 0.9837511, 0.9856411, 0.9872352,
0.9882029, 0.990218, 0.9919514, 0.9941591, 0.9965548, 0.9976154,
0.9990174, 1.0010466, 1.003395, 1.0055957, 1.007304, 1.0089384,
1.0099392, 1.0112263, 1.012146, 1.0153451, 1.0171438, 1.0186295,
1.0217893, 1.0236026, 1.0256126, 1.0271073, 1.0289151, 1.0309176,
1.033115, 1.036525, 1.0385212, 1.0406626, 1.0429921, 1.0447711,
1.0468007, 1.0501243, 1.0527771, 1.0549067, 1.0590568, 1.0635208,
1.066646, 1.0691815, 1.0725159, 1.0756145, 1.0784323, 1.0844119,
1.0900761, 1.0937593, 1.1018746, 1.1080635, 1.112721, 1.1223554,
1.1342245, 1.1589387, 1.3267105, 0.8593686, 0.8738442, 0.8847164,
0.8900729, 0.8985823, 0.9030722, 0.9047745, 0.9089145, 0.9133781,
0.9170319, 0.9205633, 0.9238323, 0.92677, 0.929173, 0.931353,
0.933437, 0.9359658, 0.9403774, 0.9424343, 0.9441537, 0.9472636,
0.9497636, 0.9514515, 0.9539961, 0.955913, 0.9577903, 0.9613333,
0.9635, 0.9644766, 0.9672279, 0.9690912, 0.9704478, 0.9730493,
0.975256, 0.9771195, 0.9792525, 0.982091, 0.9839065, 0.9862609,
0.9872484, 0.9882653, 0.9902789, 0.9919861, 0.9944648, 0.996593,
0.9976709, 0.9992019, 1.0012796, 1.0036569, 1.0061553, 1.0075918,
1.0089733, 1.0100455, 1.0112892, 1.0124005, 1.0153452, 1.0172939,
1.0186304, 1.0218203, 1.0238237, 1.0256419, 1.0272337, 1.0289253,
1.0310775, 1.0333971, 1.0365301, 1.0386841, 1.0406816, 1.04333,
1.0448819, 1.0471028, 1.0501926, 1.0528722, 1.0551719, 1.059271,
1.0638416, 1.0667384, 1.0691885, 1.0729292, 1.0756769, 1.0786645,
1.0844603, 1.0902495, 1.0940123, 1.1037544, 1.1080977, 1.1140658,
1.1234296, 1.1354923, 1.1600791)
You can get a nonparametric estimate by using the ?density function to compute the kernel density estimate, with default Gaussian kernel.
x <- density(vector)
plot(x)
Note that you can also generate an empirical cdf with the base ecdf function. That allows you to calculate F(x) for any x. E.g.
x <- rnorm(1000)
cdf <- ecdf(x)
plot(cdf)
f <- cdf(0.5)
f
[1] 0.692
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, 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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.
Here is the code sample
cq = c(15.5193589369923,15.6082097993496,15.3048276641115,15.887944383963,15.3813224284544,14.9723432922121,14.8742353464212,15.0448020475332,15.1584221729435,15.3692219904727,15.2369219681739,15.0804950645883,15.0836397511495,14.8821059462034,14.6827696388115,14.5701385743889,14.8506248103639,14.8475325690146,14.7377458445001,14.6258765734272,15.3585770134881,15.2994209401567,15.5178103826596,15.2411668198437,15.3413307248142,15.3645926457095,15.2241340874265,15.7516405898009,15.7683360263607,15.5852354340738,14.7451372367313,14.650625258402,14.7596201108925,15.0504977144055,15.0178091754821,15.100874342289,14.5156700740607,15.0530667717479,14.754595621435,15.5879633065185,15.3449828894141,15.3112460363113,15.232600495493,15.4378070492087,15.1621663266126,16.0120124580213,16.2104534293435,16.2765899877946,17.1446379330444,17.1717364140053,17.0155350105157,15.5218922723681,15.4543443324508,15.5282690363252,15.0202919978723,15.0410524376083,15.1169661551775,15.335220483258,15.3191814464955,15.0679651604846,14.7270263787123,14.70717761566,14.7907442084919,15.8468089268423,15.6714073529734,15.5478017041242,14.6949593095613,14.7537769900696,14.830942214569,15.0820225358985,15.3454125813989,15.304399073517,15.4159319040107,16.1250033895004,15.5359407225865,15.3251900155103,15.1571914994646,15.412721442436,15.913112918988,15.9852823695227,16.0912887332562,15.4897399161851,15.0710262650299,15.3517226832146,17.0001128578501,17.1040579605654,16.9578316599788,15.8842918497549,15.7016383123704,15.8513519332371,16.9420990886101,17.0793832045434,16.9288868492911,14.9127628979216,14.7689529893246,15.0534122173222,15.3185448628303,15.5507864243439,15.3737185073511,15.4350799532271,15.2414612478027,15.361320770232,15.7401140808761,15.8582795450189,15.7634036480016,15.5797995263497,15.9126261329496,15.9256641722586,15.1308493265056,15.2450158090279,15.0699176510971,15.0368959001792,14.8828877909216,14.8852035927172,15.8253506435753,15.8938440960183,15.888311876759,15.4872886586516,15.5492199156675,15.7313291529313,16.5365758222542,16.8386981731158,16.7239280992675,15.9356391540897,15.7383049532238,15.9409000309973,15.2005952554035,15.0390142751348,15.154888655127,14.6373767323354,14.3087397097081,14.3970067065903,14.6453627024929,14.8109205614192,14.6266778290643,15.5170574352528,15.359943766027,15.5869322081508,15.5246550838727,15.4670382654415,15.4211907882731,16.9534561402918,17.4848334482537,17.3182067272327,15.7804318020053,15.86794322314,15.6532944587946,16.543432367992,16.6848617423114,16.8344939905775,15.5212254647114,15.8348559815603,15.6592827767612,15.3027400892518,15.5498124465958,15.8362202772445,14.8415823671167,15.7307379811374,14.8529575353737,16.6466266958983,16.1687733596343,16.0342973266029,14.5976161739123,14.776507726931,14.6780484406283,15.3927619991024,15.3106866267163,15.2920260038624,15.9666798099925,16.2595244266754,16.1035265916681,16.018233002759,15.8460056716414,16.0722176294152,16.2763177549617,16.364250121284,16.2995041975045,16.3975912697976,16.182759197402,16.1164022801451,16.5026752161837,16.2401540005223,16.3715573563274,18.4119769797938,18.1208386122385,18.0068316479116,17.1455993749728,17.0558275544137,16.9150038143768)
sample = c("CD4 LM","CD4 LM","CD4 JC","CD4 JC","CD4 JC","CD4 BM","CD4 BM","CD4 BM","CD4 MC","CD4 MC","CD4 MC","CD4 TM","CD4 TM","CD4 TM","CD4 MM","CD4 MM","CD4 MM","CD4 SRits","CD4 SRits","CD4 SRits","CD4 GV","CD4 GV","CD4 GV","CD4 WW","CD4 WW","CD4 WW","CD4 CH","CD4 CH","CD4 FJ","CD4 FJ","CD4 KS","CD4 KS","CD4 KS","CD4 NG","CD4 NG","CD4 NG","CD4 CG","CD4 CG","CD4 CG","CD4 CSR","CD4 CSR","CD4 CSR","CD4 JM","CD4 JM","CD4 JM","CD4 DF","CD4 DF","CD4 DF","CD4 AM","CD4 AM","CD4 AM","CD4 BP","CD4 BP","CD4 BP","CD4 ER","CD4 ER","CD4 ER","CD4 SRusse","CD4 SRusse","CD4 SRusse","CD4 DS","CD4 DS","CD4 DS","CD4 KJ","CD4 KJ","CD4 KJ","CD4 GD","CD4 GD","CD4 GD","CD4 KG","CD4 KG","CD4 KG","CD4 KR","CD4 KR","CD4 KR","CD4 FN","CD4 FN","CD4 FN","CD4 RM","CD4 RM","CD4 RM","CD4 LA","CD4 LA","CD4 LA","CD4 EC","CD4 EC","CD4 EC","CD4 KW","CD4 KW","CD4 KW","CD4 HB","CD4 HB","CD4 HB","CD8 LM","CD8 LM","CD8 LM","CD8 JC","CD8 JC","CD8 JC","CD8 BM","CD8 BM","CD8 BM","CD8 MC","CD8 MC","CD8 MC","CD8 TM","CD8 TM","CD8 TM","CD8 MM","CD8 MM","CD8 MM","CD8 SRits","CD8 SRits","CD8 SRits","CD8 GV","CD8 GV","CD8 GV","CD8 WW","CD8 WW","CD8 WW","CD8 CH","CD8 CH","CD8 CH","CD8 FJ","CD8 FJ","CD8 FJ","CD8 KS","CD8 KS","CD8 KS","CD8 NG","CD8 NG","CD8 NG","CD8 CG","CD8 CG","CD8 CG","CD8 CSR","CD8 CSR","CD8 CSR","CD8 JM","CD8 JM","CD8 JM","CD8 DF","CD8 DF","CD8 DF","CD8 AM","CD8 AM","CD8 AM","CD8 BP","CD8 BP","CD8 BP","CD8 ER","CD8 ER","CD8 ER","CD8 SRusse","CD8 SRusse","CD8 SRusse","CD8 DS","CD8 DS","CD8 DS","CD8 KJ","CD8 KJ","CD8 KJ","CD8 GD","CD8 GD","CD8 GD","CD8 KG","CD8 KG","CD8 KG","CD8 KR","CD8 KR","CD8 KR","CD8 FN","CD8 FN","CD8 FN","CD8 RM","CD8 RM","CD8 RM","CD8 LA","CD8 LA","CD8 LA","CD8 EC","CD8 EC","CD8 EC","CD8 KW","CD8 KW","CD8 KW","CD8 HB","CD8 HB","CD8 HB")
df = data.frame(cq, sample)
df.res <- lm(cq~sample, data = df)
require(lsmeans)
t<- pairs(lsmeans(df.res, "sample"))
system.time(tc <- confint(t, level=0.95))
The time taken by the confint function is
user system elapsed
10.58 0.00 10.60
I have tried using confint.default but I get an error
tc <- confint.default(t, level=0.95)
Error: $ operator not defined for this S4 class
It's a bit buried in the documentation, but what's slowing you down is the multiple-comparisons correction computations. There's wide variation in the elapsed time for the available methods. See the Confidence-limit and P-value adjustments section of ?summary.ref.grid for details, and decide which method meets your criteria of being both fast enough and reliable enough for your use case ...
adj <- c("tukey","scheffe","sidak","bonferroni","dunnettx","mvt")
sapply(adj,function(a) system.time(confint(t,adjust=a))["elapsed"])
## tukey.elapsed scheffe.elapsed sidak.elapsed bonferroni.elapsed
9.256 0.195 0.168 0.173
## dunnettx.elapsed mvt.elapsed
14.370 1.821
Here is a fast way:
confint(t,method="Wald")