Estrategia de construcción de modelos para regresión logística
Julián Serna
2022-06-02
library(faux)## Warning: package 'faux' was built under R version 4.1.3##
## ************
## Welcome to faux. For support and examples visit:
## https://debruine.github.io/faux/
## - Get and set global package options with: faux_options()
## ************set.seed(0374)
dfa <- rnorm_multi(n = 1000,
mu = c(67, 30, 30, 320),
sd = c(2, 3, 5, 10),
varnames = c('Edad', 'dap', 'rto', 'clolA'),
r = c(0.4, 0.6, 0.5, 0.6, 0.7, 0.8))
dfa$hibrido <- round(runif(n = 1000, min = 0,max = 1.2))
w <- 0.5 * dfa$clolA - 0.01 * dfa$dap - 0.6 * dfa$rto - 0.02 * dfa$Edad
dfa$abortos <- factor(ifelse(w > 140, '1', '0' ),
labels = c('No', 'Si')) #"Si" para 1 y "No" para 0
dfa## Edad dap rto clolA hibrido abortos
## 1 71.54128 34.44222 37.96619 333.7880 1 Si
## 2 66.07308 34.62718 26.93766 318.3208 0 Si
## 3 66.15741 30.16967 29.32100 319.5784 0 Si
## 4 68.39663 23.70414 35.60376 319.7968 1 No
## 5 65.82920 28.74009 21.05399 309.7039 0 Si
## 6 68.54235 30.78879 31.10149 326.4118 1 Si
## 7 65.60846 27.55027 25.38762 320.2942 0 Si
## 8 66.51456 30.34411 28.42637 319.0012 1 Si
## 9 69.80460 28.79572 31.31242 326.6107 0 Si
## 10 69.54156 34.27971 34.16086 323.5461 1 No
## 11 65.81237 28.29244 29.01384 312.5279 1 No
## 12 70.46750 30.13767 33.64183 325.1211 0 Si
## 13 69.06079 32.39578 32.30499 323.5801 1 Si
## 14 68.91308 32.75361 36.75882 331.2354 1 Si
## 15 67.46599 28.04149 33.03414 316.1659 1 No
## 16 67.61577 31.44313 26.27395 315.7318 1 Si
## 17 66.50358 29.26811 31.00190 321.0358 1 Si
## 18 64.20801 28.00664 28.68606 314.0877 0 No
## 19 67.07901 30.15911 28.72171 312.9514 1 No
## 20 64.93714 30.16374 26.19983 304.2450 1 No
## 21 66.08605 27.66166 26.79018 326.6489 1 Si
## 22 62.01661 29.18309 27.40287 314.7946 0 No
## 23 68.20410 33.22791 30.57247 310.7975 0 No
## 24 69.34143 28.64872 30.48656 309.9376 0 No
## 25 71.89346 34.47614 34.29333 320.5141 0 No
## 26 69.60923 31.60909 35.41533 329.5294 1 Si
## 27 68.49996 30.46021 31.79415 320.0735 0 No
## 28 66.73113 31.17770 27.59886 310.2700 1 No
## 29 64.97169 33.73291 29.11639 326.0137 1 Si
## 30 69.39363 29.87451 30.65551 319.8304 1 No
## 31 71.12368 30.64036 30.77576 319.9243 0 No
## 32 65.22799 31.83110 28.44020 321.0410 1 Si
## 33 62.50587 29.17257 17.80814 311.7386 1 Si
## 34 65.64200 28.87648 29.78898 317.4535 0 No
## 35 65.22643 25.37555 22.10462 308.2426 1 No
## 36 68.21499 31.38258 33.03782 328.2542 1 Si
## 37 67.37934 36.84724 36.33874 333.4524 1 Si
## 38 65.38862 28.30701 23.43424 310.5335 0 No
## 39 65.82613 29.04143 24.43052 311.7545 1 No
## 40 64.68485 25.82266 21.68720 314.0191 0 Si
## 41 72.22708 35.33579 33.40906 324.5110 0 Si
## 42 63.13838 29.19808 24.20022 298.6144 0 No
## 43 63.63559 27.44568 28.54031 320.3750 0 Si
## 44 68.64598 32.56668 35.86984 330.7324 1 Si
## 45 64.53641 32.75905 32.91333 322.6482 0 No
## 46 68.64840 35.32103 39.91400 332.3247 1 Si
## 47 64.79532 25.26520 27.53543 308.6617 0 No
## 48 66.94512 33.76416 33.17152 325.1996 1 Si
## 49 67.48842 27.45325 30.75828 320.5571 1 Si
## 50 69.46859 33.76787 36.04585 331.7325 1 Si
## 51 64.97100 29.36723 21.53259 310.8876 0 Si
## 52 70.10018 35.46529 36.40969 332.2821 1 Si
## 53 65.11034 29.21042 29.21446 321.7316 1 Si
## 54 68.22373 27.51368 28.37586 321.2915 0 Si
## 55 67.10684 29.08635 31.07975 323.3635 1 Si
## 56 65.67612 29.52568 28.04291 316.4922 1 No
## 57 67.33387 32.86313 30.58263 325.5587 1 Si
## 58 67.33683 27.89820 27.55708 306.9192 0 No
## 59 66.62414 30.14963 30.75665 323.7068 1 Si
## 60 63.60940 29.32915 23.00045 298.4685 1 No
## 61 67.17320 31.97927 39.62021 337.7534 0 Si
## 62 66.55233 32.27759 30.32966 322.4596 1 Si
## 63 67.84747 32.18745 32.78118 329.4447 1 Si
## 64 69.88092 36.05226 33.77857 333.1827 0 Si
## 65 64.98052 28.22373 25.12646 311.4402 0 No
## 66 63.33917 28.56920 33.86916 327.1792 0 Si
## 67 65.63841 28.94783 29.14746 313.7369 0 No
## 68 68.40621 28.54538 25.63928 305.8709 1 No
## 69 69.09388 29.57002 35.70056 322.8428 1 No
## 70 67.90924 29.25682 32.80083 324.1483 0 Si
## 71 65.02948 22.56744 28.28074 315.0773 1 No
## 72 69.42565 31.29099 29.76491 314.7310 1 No
## 73 64.20069 28.57144 30.12430 318.8329 1 No
## 74 68.77339 29.29972 32.46673 326.5471 0 Si
## 75 68.72048 29.70789 30.64043 325.2904 0 Si
## 76 63.23471 23.12674 19.34359 289.2073 1 No
## 77 68.50878 32.16923 31.28023 324.2343 1 Si
## 78 62.86049 29.91434 23.59126 309.5636 1 No
## 79 68.16233 29.45746 30.45067 325.5808 1 Si
## 80 66.93122 31.86258 27.33891 324.3402 1 Si
## 81 67.45008 30.78561 30.82236 316.9132 1 No
## 82 64.48793 24.61024 22.69952 303.5850 1 No
## 83 65.79304 24.36561 27.82113 315.7995 0 No
## 84 61.70634 26.64751 16.99864 300.8035 0 No
## 85 67.19673 24.51398 29.88426 311.9830 1 No
## 86 66.20565 26.05601 28.50461 317.0313 1 No
## 87 66.23795 27.02943 31.99942 312.7854 1 No
## 88 66.19850 27.29830 25.40514 315.2574 1 Si
## 89 66.22342 32.27677 34.48007 324.2674 0 No
## 90 65.41854 24.17698 25.12565 313.5487 1 Si
## 91 67.68344 31.39580 35.45779 330.4139 1 Si
## 92 67.16692 35.73936 32.26434 332.3812 1 Si
## 93 69.51484 28.99919 36.15234 321.8369 1 No
## 94 66.21610 30.83181 33.66320 313.2871 1 No
## 95 69.53038 28.56183 31.31782 322.3364 0 Si
## 96 68.49399 35.02601 36.56882 330.4282 1 Si
## 97 62.67741 25.42855 19.48673 301.8810 1 No
## 98 64.69801 28.00146 31.77773 317.8248 0 No
## 99 68.04585 29.94968 30.08574 311.2546 0 No
## 100 67.51192 31.01061 39.54605 327.1562 0 No
## 101 64.90597 30.80302 23.95678 312.5645 0 Si
## 102 67.36349 34.99616 30.79670 328.3911 1 Si
## 103 67.24092 29.37187 35.35980 326.4247 1 Si
## 104 65.39386 27.23672 25.00090 309.0943 0 No
## 105 66.75690 35.04275 34.65372 333.8958 0 Si
## 106 66.21521 31.50874 28.93213 319.4508 1 Si
## 107 68.30028 35.45667 35.71522 332.9094 1 Si
## 108 70.68591 27.00119 34.63305 318.0383 1 No
## 109 64.98556 28.20250 28.62829 309.9912 0 No
## 110 69.33807 33.00286 34.05698 325.9808 1 Si
## 111 65.76988 32.06663 32.74918 326.3041 1 Si
## 112 67.71241 30.66233 29.41726 312.5830 1 No
## 113 66.33667 27.58148 32.19749 321.1544 0 No
## 114 67.90063 27.38701 30.22436 322.3314 0 Si
## 115 70.54666 35.53103 33.88839 323.8931 0 No
## 116 64.79230 28.41393 28.21083 314.6804 1 No
## 117 68.68266 34.39532 30.36738 333.2489 1 Si
## 118 67.51359 28.71184 32.14870 324.8160 1 Si
## 119 67.50595 29.14726 28.36695 334.2459 0 Si
## 120 69.87346 33.60145 28.14950 313.8024 1 No
## 121 70.00484 35.04217 33.72616 324.6157 0 Si
## 122 65.79571 31.78290 29.74783 311.1871 1 No
## 123 67.54890 31.50471 33.01582 326.3501 0 Si
## 124 68.23432 29.41926 32.00782 327.9283 1 Si
## 125 68.90791 35.48049 34.50889 338.8119 0 Si
## 126 69.48789 31.00027 29.89272 320.4859 0 Si
## 127 65.11470 28.76417 29.39345 320.2549 0 Si
## 128 65.72964 29.04623 28.88847 315.0685 1 No
## 129 67.80965 35.51009 36.49923 320.3128 1 No
## 130 65.57632 30.08054 23.61102 315.3046 1 Si
## 131 66.68738 29.13126 30.77408 325.4811 0 Si
## 132 63.03075 28.38881 22.71192 314.9244 0 Si
## 133 67.76200 31.64210 37.54013 330.3250 1 Si
## 134 62.45069 32.14340 21.57860 312.9787 1 Si
## 135 66.90603 27.22415 27.42735 305.4958 1 No
## 136 66.41155 30.91978 31.98110 324.9563 1 Si
## 137 66.38753 30.54886 27.64397 318.8025 0 Si
## 138 69.95648 29.82693 35.90185 327.1924 0 Si
## 139 68.29481 34.22039 34.69508 337.0437 0 Si
## 140 66.80723 27.67961 25.24673 317.3440 0 Si
## 141 67.27768 27.15705 24.51847 312.7976 1 Si
## 142 62.31184 22.63407 19.73386 301.9188 1 No
## 143 65.57698 27.37593 26.84496 317.0917 1 Si
## 144 68.95178 26.17672 30.19392 316.3260 0 No
## 145 66.97125 30.60406 29.82481 327.0370 0 Si
## 146 67.76345 30.54383 37.44252 330.0063 1 Si
## 147 67.70664 33.22303 37.16005 336.0389 0 Si
## 148 67.18665 25.47724 25.50923 302.9350 1 No
## 149 67.52439 27.27855 25.75095 314.8899 0 Si
## 150 69.26386 30.69306 37.01761 329.2008 0 Si
## 151 65.04025 26.38285 25.94383 306.3684 1 No
## 152 66.35969 30.02136 21.60263 299.8101 1 No
## 153 65.52293 27.15300 26.00163 321.6162 1 Si
## 154 66.25670 28.95746 32.05557 319.7970 1 No
## 155 68.73937 35.97005 38.97934 333.4331 1 Si
## 156 65.88632 29.23759 27.09809 317.6061 1 Si
## 157 64.36197 31.59865 29.20525 318.3025 0 Si
## 158 67.15887 26.47435 30.15137 313.3011 1 No
## 159 66.83961 32.44822 26.80693 313.1711 1 No
## 160 66.44034 26.04870 29.55649 313.7985 1 No
## 161 66.79984 32.49101 36.91775 333.6390 1 Si
## 162 69.93959 32.81465 32.68472 330.7706 1 Si
## 163 70.10287 35.16953 34.14295 338.7983 1 Si
## 164 67.45601 36.46653 39.51268 342.8620 0 Si
## 165 65.45617 27.48571 26.88312 306.1193 0 No
## 166 66.80485 28.75885 28.25966 319.5780 1 Si
## 167 64.47893 28.41149 20.74300 300.5275 1 No
## 168 66.36451 28.98863 29.86185 321.1841 0 Si
## 169 69.41865 32.66135 31.76962 325.9044 1 Si
## 170 73.54523 35.27074 36.93465 347.4611 1 Si
## 171 71.22959 29.80751 37.00958 336.0921 1 Si
## 172 65.50391 30.66292 26.93542 328.5678 1 Si
## 173 67.14930 32.34402 34.66046 328.8081 0 Si
## 174 66.55999 29.22912 24.88795 319.3619 1 Si
## 175 64.35679 27.17979 24.97132 318.0260 1 Si
## 176 69.27533 30.65526 33.79598 329.6518 1 Si
## 177 70.77539 31.47362 36.33631 325.3606 0 No
## 178 68.72533 33.03940 32.82310 326.2772 1 Si
## 179 67.50257 24.94743 26.64423 307.2597 1 No
## 180 70.91636 29.57879 39.50417 324.9590 1 No
## 181 66.99999 25.46935 26.37363 302.2847 0 No
## 182 64.93996 30.05499 29.80090 320.0556 1 Si
## 183 69.98385 32.22656 37.01802 337.0626 0 Si
## 184 65.60944 25.53260 21.28074 307.5523 1 No
## 185 71.68065 27.70616 39.40619 316.1201 1 No
## 186 64.99963 27.15966 24.74277 307.9585 1 No
## 187 65.94970 27.17466 23.48588 306.1746 0 No
## 188 69.15035 31.68955 35.87535 318.9269 0 No
## 189 68.31625 33.53638 32.59687 335.2108 0 Si
## 190 69.34038 33.91384 31.74457 315.6984 1 No
## 191 66.91568 31.11684 36.54629 328.5572 1 Si
## 192 65.95550 29.65758 32.12178 327.0308 1 Si
## 193 67.63432 30.71609 30.25664 319.3631 0 No
## 194 65.77804 29.44676 23.95862 314.9151 0 Si
## 195 68.33431 30.74885 31.91659 315.3790 1 No
## 196 67.91340 28.04193 28.83571 318.4654 1 Si
## 197 66.00476 28.39027 24.56096 318.7288 1 Si
## 198 69.04428 27.63465 34.65717 326.4169 0 Si
## 199 68.84695 30.94301 30.66655 325.1676 1 Si
## 200 64.27864 30.24894 24.52315 321.3333 1 Si
## 201 68.73711 33.12543 38.96371 325.9967 0 No
## 202 67.44962 31.05130 35.28657 329.0949 0 Si
## 203 68.76803 31.12815 28.09758 317.8255 1 Si
## 204 68.09695 35.98201 37.08783 336.6128 1 Si
## 205 66.34566 28.27880 29.16019 319.4350 1 Si
## 206 67.52181 24.62407 32.06355 317.8396 1 No
## 207 65.36069 33.29645 31.59186 315.4179 0 No
## 208 64.24479 26.95199 28.01980 306.7454 1 No
## 209 62.20050 27.41634 27.37406 311.8839 1 No
## 210 67.88071 31.16150 33.65343 332.1194 1 Si
## 211 65.49276 28.96791 32.94846 314.7496 0 No
## 212 68.56872 30.24013 34.75313 327.9753 1 Si
## 213 65.54177 29.09708 29.26879 314.9662 0 No
## 214 68.82531 27.84566 35.11428 324.6248 1 No
## 215 66.01983 32.39630 34.44991 325.8446 0 Si
## 216 67.40138 26.69101 25.75671 309.6874 1 No
## 217 65.57712 28.88655 28.72283 316.4878 0 No
## 218 67.81085 26.97813 25.26858 312.2029 1 No
## 219 66.85105 31.07116 34.22999 319.1754 0 No
## 220 64.76094 29.03394 24.58686 311.6585 0 No
## 221 68.95770 28.12224 30.54342 318.3293 1 No
## 222 69.75547 28.84600 36.22407 323.6118 1 No
## 223 66.38918 35.27560 32.30910 336.8297 1 Si
## 224 67.23534 38.44486 39.64903 341.2809 1 Si
## 225 67.60058 29.25122 25.29615 316.9348 1 Si
## 226 65.09575 22.07174 20.60404 300.8612 0 No
## 227 66.68590 27.26630 26.44340 308.7667 1 No
## 228 64.30272 25.71865 21.14005 302.2527 0 No
## 229 63.43519 29.40821 31.65974 327.4832 1 Si
## 230 67.16265 36.17529 32.82061 329.5285 0 Si
## 231 67.83227 27.52816 27.96803 313.1415 1 No
## 232 65.78404 37.19154 32.93006 329.0691 0 Si
## 233 63.54815 28.04565 19.11371 299.4594 1 No
## 234 63.01690 28.58116 21.19373 311.0073 0 Si
## 235 63.33848 31.11767 26.55362 307.3493 1 No
## 236 65.82156 29.24179 29.11908 316.4593 1 No
## 237 67.34049 31.35844 34.66901 330.8461 0 Si
## 238 70.20002 35.05136 39.35817 339.8173 1 Si
## 239 67.59411 28.98129 37.32152 336.2650 0 Si
## 240 64.80798 26.40053 22.87971 298.3046 1 No
## 241 67.98217 32.21349 30.69910 319.5348 0 No
## 242 69.83273 32.99878 34.92564 333.3510 1 Si
## 243 62.29753 28.17113 22.59824 306.8095 1 No
## 244 69.65309 33.85140 37.07785 327.9033 0 No
## 245 65.74047 24.02429 27.86227 313.4362 0 No
## 246 70.07036 35.57894 46.72264 345.3589 1 Si
## 247 68.13433 30.42778 31.74492 320.4742 1 No
## 248 71.07789 29.89409 31.02659 327.2445 1 Si
## 249 70.03819 30.05672 31.92435 323.2096 1 Si
## 250 65.66115 28.01624 26.34592 321.8564 1 Si
## 251 68.75849 29.45187 32.92136 332.2115 1 Si
## 252 67.60868 26.39147 25.33125 301.7950 1 No
## 253 68.37544 28.24075 33.02692 321.2500 0 No
## 254 68.13470 33.38993 36.07237 334.0402 1 Si
## 255 63.18915 23.76528 23.57592 307.6337 1 No
## 256 66.87992 30.61114 30.86098 318.4727 1 No
## 257 67.86508 33.23495 34.22266 325.1002 1 Si
## 258 70.58021 32.29179 36.96811 333.4452 0 Si
## 259 68.46384 28.54739 27.71897 308.3485 1 No
## 260 65.69683 30.75338 32.83810 325.8774 0 Si
## 261 64.92027 29.65885 27.79164 321.2913 1 Si
## 262 68.18755 30.97352 26.62937 313.1811 1 No
## 263 67.41207 29.66029 26.78465 317.3367 0 Si
## 264 64.57174 26.45744 25.08695 312.6545 0 No
## 265 67.45905 31.87403 27.62401 309.5944 0 No
## 266 65.12906 28.52154 28.26224 314.2798 0 No
## 267 70.37808 34.14262 35.02771 330.0232 1 Si
## 268 65.50831 25.03618 32.28359 321.9086 1 Si
## 269 66.72618 31.05374 32.13424 328.8746 1 Si
## 270 65.17581 28.28531 25.03008 307.9207 0 No
## 271 68.53199 28.58353 30.38227 326.5353 1 Si
## 272 69.21156 29.20030 34.90906 333.1148 0 Si
## 273 68.94070 31.62837 32.88167 327.6616 1 Si
## 274 67.19719 31.71606 33.55768 320.3777 1 No
## 275 65.91776 36.22012 32.75627 331.3043 1 Si
## 276 67.12048 29.20447 27.62069 330.8051 0 Si
## 277 69.26849 30.94924 33.62748 320.7345 0 No
## 278 63.15917 26.04965 23.52423 302.4780 1 No
## 279 65.75784 29.60511 31.50566 322.2399 0 Si
## 280 66.61261 26.54392 28.95585 315.3219 1 No
## 281 65.02787 24.96585 24.82772 316.1616 1 Si
## 282 63.30967 30.97508 26.52720 322.1221 1 Si
## 283 68.02804 29.41656 32.88142 317.8502 1 No
## 284 65.26320 23.06945 25.15134 306.8104 1 No
## 285 68.81798 24.99346 26.39465 299.7773 0 No
## 286 70.64438 31.30508 33.64379 331.6168 1 Si
## 287 68.10981 35.01348 31.45151 322.4347 0 Si
## 288 67.59881 31.19245 28.14598 312.9502 1 No
## 289 67.47644 33.03903 30.50039 326.0674 1 Si
## 290 66.06239 36.70826 38.80353 340.1676 1 Si
## 291 65.37669 27.42711 30.26889 315.1816 1 No
## 292 62.57380 25.34300 19.71984 294.3471 0 No
## 293 66.31937 26.31606 27.58076 308.8933 0 No
## 294 68.25719 34.78475 29.55384 330.1075 0 Si
## 295 67.58090 29.10567 31.33034 319.5978 0 No
## 296 63.75361 26.00812 22.60296 299.8710 1 No
## 297 69.68151 33.54569 39.06159 338.4192 1 Si
## 298 67.31486 27.20637 26.74780 319.4008 0 Si
## 299 65.46706 28.16839 26.04137 312.9171 1 No
## 300 67.43449 28.79167 27.85577 302.9120 0 No
## 301 65.40507 29.25914 22.32926 306.9898 0 No
## 302 67.81345 31.87349 34.33978 330.6805 1 Si
## 303 66.59892 28.09293 19.26837 308.7875 1 Si
## 304 68.16798 32.29253 30.82607 316.2792 0 No
## 305 65.65922 30.13505 26.29294 318.5937 0 Si
## 306 67.73575 30.92471 33.84666 329.1189 1 Si
## 307 67.94975 28.28692 25.91269 308.5498 0 No
## 308 66.21738 29.03661 24.08480 308.1788 1 No
## 309 69.77003 28.89013 40.30601 332.3968 1 Si
## 310 65.53791 27.50513 22.16163 306.6213 0 No
## 311 69.42492 26.36183 29.48998 318.9007 1 Si
## 312 63.40162 28.14374 23.01994 309.5435 1 No
## 313 65.25226 32.53673 29.26392 330.1669 1 Si
## 314 70.92400 31.60763 37.96802 332.2186 1 Si
## 315 68.00222 33.32307 31.30513 320.2835 1 No
## 316 66.11207 25.31192 26.84180 304.5149 0 No
## 317 68.20025 31.57835 29.82258 324.3244 1 Si
## 318 67.42210 32.38637 30.61870 324.3135 1 Si
## 319 67.68449 29.95618 28.72148 312.7451 1 No
## 320 68.98964 34.20711 37.75267 334.9424 1 Si
## 321 66.50379 28.28126 29.18283 321.7715 0 Si
## 322 67.48831 30.79893 33.06417 325.6097 1 Si
## 323 68.97692 30.71814 34.21589 330.4322 1 Si
## 324 69.87555 30.29590 32.03139 313.6841 1 No
## 325 65.27800 30.32776 24.77931 319.2355 0 Si
## 326 65.38241 32.72506 24.99928 317.1660 1 Si
## 327 65.67722 30.11057 34.56032 335.6170 0 Si
## 328 66.37206 30.84120 34.45327 326.5701 0 Si
## 329 65.43031 27.67761 24.13593 308.4435 1 No
## 330 70.25500 32.01586 39.08675 336.5122 1 Si
## 331 66.20157 29.42022 24.60056 312.6986 0 No
## 332 69.48619 34.44977 30.01306 326.1283 1 Si
## 333 69.20842 29.60928 36.99388 330.4798 0 Si
## 334 65.81427 31.12548 27.30784 321.3973 1 Si
## 335 69.06787 28.40649 31.48671 324.2537 0 Si
## 336 66.54101 25.88795 28.43742 327.3833 1 Si
## 337 62.98376 26.44288 23.62723 305.7044 0 No
## 338 68.16571 34.84686 33.04749 327.5888 0 Si
## 339 65.97212 32.11499 35.90706 326.9017 1 Si
## 340 65.76917 22.40984 23.41388 308.6439 0 No
## 341 66.39100 33.79131 27.98117 315.9856 0 No
## 342 65.86692 29.91639 29.56663 312.7955 1 No
## 343 69.11730 34.96359 37.60239 339.7601 1 Si
## 344 65.10253 26.85490 23.32584 311.6465 0 Si
## 345 68.74669 33.76395 29.61503 313.8445 0 No
## 346 67.85413 31.70527 30.21213 332.0675 0 Si
## 347 64.50095 28.47488 29.34682 318.3736 1 Si
## 348 68.15091 33.07887 36.14047 324.2900 1 No
## 349 66.88689 32.06766 41.73943 334.0501 0 Si
## 350 66.82999 27.58519 30.61707 322.6872 0 Si
## 351 66.73535 25.62934 30.93538 310.1461 0 No
## 352 65.48873 29.13448 29.44944 311.9266 0 No
## 353 69.28254 27.60468 30.09740 316.0702 0 No
## 354 68.73484 29.24505 31.29212 319.1797 0 No
## 355 69.42607 30.09013 28.52096 323.4371 1 Si
## 356 66.29262 33.56653 27.77942 314.4572 1 No
## 357 66.07936 31.07447 22.94252 319.0540 0 Si
## 358 66.27740 29.72928 27.81131 319.7047 1 Si
## 359 69.54801 29.41927 26.52709 312.7800 1 No
## 360 65.15605 24.27635 28.32927 325.0995 0 Si
## 361 67.39781 32.43455 35.22939 327.1442 0 Si
## 362 69.91904 32.20505 27.95329 315.0712 1 No
## 363 65.85306 26.54884 26.53424 304.4293 1 No
## 364 70.38729 40.80578 43.53297 353.9491 1 Si
## 365 64.13026 25.96561 25.50769 308.8434 1 No
## 366 66.11438 31.04447 21.54118 310.0167 0 Si
## 367 69.57530 30.91696 36.71267 341.0009 0 Si
## 368 65.40909 31.34206 26.72981 313.4856 0 No
## 369 65.82415 31.35581 27.72739 316.6033 0 Si
## 370 69.47460 27.77477 30.48400 318.9044 0 No
## 371 64.79414 23.33656 25.82414 304.9744 0 No
## 372 68.95310 28.02290 35.85017 324.8095 0 No
## 373 67.93691 31.04953 26.07497 311.7453 0 No
## 374 66.98113 30.19124 32.17793 326.4708 0 Si
## 375 67.30118 29.70864 31.83157 325.1534 0 Si
## 376 66.48097 35.24810 35.31354 336.8319 1 Si
## 377 66.23317 33.73846 33.26472 328.4840 1 Si
## 378 66.49428 30.60431 29.52872 310.3987 0 No
## 379 68.18722 32.82861 29.25482 323.7943 0 Si
## 380 66.02528 28.39625 31.31908 319.7314 0 No
## 381 67.81468 29.30892 30.70301 323.9040 1 Si
## 382 65.35733 21.49343 26.62116 303.5713 0 No
## 383 67.25129 29.54643 29.56491 324.0108 1 Si
## 384 65.69473 30.89084 32.46265 321.7837 1 No
## 385 66.53564 29.48428 28.15185 311.6304 0 No
## 386 63.17086 28.90609 21.64578 304.7720 0 No
## 387 66.03764 29.28991 28.71604 315.4703 1 No
## 388 68.62979 29.30852 27.69658 320.9672 1 Si
## 389 65.45405 29.14997 21.91907 314.5057 1 Si
## 390 67.56683 31.29487 23.73723 314.9014 1 Si
## 391 68.39707 33.71916 28.82030 329.9708 0 Si
## 392 65.50685 29.91357 27.66495 322.8538 1 Si
## 393 65.45859 31.24216 30.66891 324.5328 0 Si
## 394 69.43351 33.23055 33.17497 323.1329 0 No
## 395 65.18389 30.77234 27.77975 312.0016 0 No
## 396 64.13583 25.56295 32.14580 320.8579 0 No
## 397 67.43420 28.59397 27.48836 311.2537 1 No
## 398 66.01138 27.46737 31.44799 327.1469 0 Si
## 399 67.33194 37.79067 32.16727 339.2167 1 Si
## 400 69.44424 27.75897 32.88062 325.2454 1 Si
## 401 66.40328 32.73075 34.20935 322.9230 1 No
## 402 67.78039 26.44943 29.74926 319.8812 0 Si
## 403 66.11330 30.18743 23.28984 312.4628 0 Si
## 404 63.76442 26.17886 24.93735 310.5896 1 No
## 405 65.27798 28.95256 25.59105 318.7517 0 Si
## 406 70.50239 30.67284 33.02852 323.0978 1 Si
## 407 66.46543 30.42381 28.41058 315.8252 0 No
## 408 70.95853 33.74658 45.61875 347.6133 1 Si
## 409 66.95677 27.86644 34.34289 323.5137 0 No
## 410 67.91163 30.73249 35.21965 323.6623 1 No
## 411 65.81542 28.78217 28.44662 325.8136 0 Si
## 412 64.39975 27.35603 30.03160 326.3388 1 Si
## 413 67.01770 34.16009 36.51459 331.1608 1 Si
## 414 70.82637 30.32732 33.05428 334.8114 1 Si
## 415 69.66628 32.78489 37.42481 341.7736 1 Si
## 416 66.12617 27.46973 23.44573 312.4063 1 Si
## 417 69.78992 33.34512 38.21023 333.1738 1 Si
## 418 65.76476 29.58757 28.61123 311.8320 1 No
## 419 63.89539 29.15553 25.12664 317.0987 0 Si
## 420 66.09681 27.47282 24.57563 306.4417 0 No
## 421 66.51691 29.55804 36.98920 336.8754 1 Si
## 422 70.11508 29.23059 32.19231 320.5372 1 No
## 423 72.55858 30.75986 34.06183 327.9791 0 Si
## 424 68.66343 32.92779 33.87163 332.0920 1 Si
## 425 67.84455 25.26338 32.20148 326.2829 1 Si
## 426 67.72826 30.21499 32.02920 323.1622 0 Si
## 427 66.49572 24.85123 26.71216 310.1076 0 No
## 428 64.54199 30.49364 29.11400 313.8883 0 No
## 429 64.63642 32.48595 28.80279 334.4725 1 Si
## 430 70.66630 31.53001 35.70423 328.3100 0 Si
## 431 69.97215 27.30205 32.53043 317.0810 1 No
## 432 67.19773 28.22874 27.96275 320.3673 0 Si
## 433 64.11855 26.03792 22.19327 307.0040 0 No
## 434 68.70996 30.07941 30.90294 323.6728 1 Si
## 435 66.67029 27.58309 29.06251 307.4765 1 No
## 436 66.25022 30.83019 26.41568 316.8947 0 Si
## 437 65.65406 27.12027 28.15916 322.0986 1 Si
## 438 66.22496 25.92496 23.66577 300.5305 1 No
## 439 64.16059 25.00523 20.77680 303.6908 1 No
## 440 62.00905 29.56874 24.08008 310.3519 0 No
## 441 68.85687 29.44111 28.23306 315.4042 1 No
## 442 66.13797 22.32421 20.00195 297.7006 1 No
## 443 62.71654 25.34817 25.29011 311.9600 0 No
## 444 66.42435 25.81720 30.91623 319.5630 1 No
## 445 66.89481 26.05688 18.66025 310.7599 1 Si
## 446 66.55192 30.38309 29.14155 310.4599 1 No
## 447 67.18330 29.84698 28.07915 314.7016 1 No
## 448 70.38030 33.83905 33.87453 327.9364 0 Si
## 449 64.13871 27.76602 25.46435 301.4484 0 No
## 450 68.62499 32.89461 41.42269 334.0346 1 Si
## 451 67.01129 30.18252 31.28367 314.5645 1 No
## 452 67.35414 26.23311 28.86630 312.6091 1 No
## 453 69.42811 35.76588 32.05269 331.1787 0 Si
## 454 65.87392 29.16910 32.48712 326.6108 1 Si
## 455 66.57620 28.38834 26.76754 313.1956 1 No
## 456 67.86381 28.81657 32.63439 323.0550 0 Si
## 457 69.48795 31.87146 30.78924 332.1234 1 Si
## 458 67.12499 30.83648 37.23001 331.7960 0 Si
## 459 65.15264 30.07212 23.87023 316.3287 0 Si
## 460 65.84055 30.24819 32.78782 312.9886 1 No
## 461 68.09069 35.45674 38.61500 340.8559 1 Si
## 462 71.91526 33.18470 33.73788 327.3973 1 Si
## 463 66.03327 32.65676 25.28490 315.5066 1 Si
## 464 68.50522 26.52931 26.53669 306.9712 0 No
## 465 64.10826 26.08243 21.03585 300.2427 1 No
## 466 65.60643 28.83425 29.38246 314.2840 1 No
## 467 68.65284 34.51297 27.85779 324.3246 0 Si
## 468 68.64987 28.84541 33.01058 320.0368 1 No
## 469 68.66836 30.35854 33.66134 312.9909 1 No
## 470 67.50801 31.18096 34.31250 323.2730 0 No
## 471 68.48807 32.38432 33.09322 330.0171 0 Si
## 472 65.16954 28.40288 24.09552 312.3612 0 Si
## 473 69.74754 32.52692 34.59184 326.3295 1 Si
## 474 67.51010 29.93514 38.90934 322.6903 0 No
## 475 65.49813 32.97716 31.72681 315.2141 0 No
## 476 70.19361 34.39399 33.46533 333.3450 0 Si
## 477 66.97279 27.94414 34.01626 307.0109 0 No
## 478 67.85284 31.80259 36.49730 334.6524 0 Si
## 479 66.33493 31.43167 34.74890 322.7718 1 No
## 480 64.39520 32.45172 27.69487 327.6873 1 Si
## 481 64.99531 28.87219 27.98876 321.6764 1 Si
## 482 70.53619 32.47441 36.57797 327.4681 0 Si
## 483 64.03936 28.66621 21.57471 315.1203 1 Si
## 484 70.04910 39.49299 39.41341 346.4758 1 Si
## 485 64.55957 27.73225 27.37933 320.5771 0 Si
## 486 65.55075 28.53126 28.02980 311.9099 1 No
## 487 68.04762 28.07185 30.77361 318.0522 0 No
## 488 63.76261 25.26371 20.87311 304.0255 1 No
## 489 63.63459 29.01131 29.97383 324.3494 1 Si
## 490 69.61731 30.34161 30.20600 322.4434 1 Si
## 491 63.42933 33.11109 25.63230 317.8678 0 Si
## 492 68.02188 32.45959 32.58248 328.4442 0 Si
## 493 65.69962 27.32765 27.77164 316.8588 1 Si
## 494 62.12251 27.76422 22.83982 308.5518 1 No
## 495 65.15240 27.18248 27.35217 309.1350 1 No
## 496 68.03899 31.36541 34.13046 331.4195 1 Si
## 497 65.18814 29.62366 20.83881 314.8713 1 Si
## 498 67.51834 31.77844 32.53089 323.3783 0 Si
## 499 68.93372 32.04011 37.63910 332.8172 1 Si
## 500 67.68392 32.07817 32.62918 321.6353 0 No
## 501 65.51407 25.97315 26.50100 309.9433 0 No
## 502 69.30492 31.34006 25.33306 318.4599 0 Si
## 503 64.23440 29.02335 25.79688 311.0386 0 No
## 504 66.94796 27.12211 23.28035 314.8118 1 Si
## 505 64.32846 30.34931 29.01691 319.7979 1 Si
## 506 65.68742 29.84704 26.57612 314.9990 0 No
## 507 68.20118 31.95739 34.91266 329.0943 1 Si
## 508 68.39618 27.30009 29.52871 312.1763 0 No
## 509 66.82279 32.81598 29.83599 321.3016 0 Si
## 510 66.77904 32.21118 30.92790 334.6223 1 Si
## 511 66.40402 25.39292 24.14452 308.0638 1 No
## 512 66.14554 31.25214 25.93142 317.3388 1 Si
## 513 68.63682 33.04777 34.45678 333.5523 0 Si
## 514 65.75575 27.41827 28.02900 313.8245 1 No
## 515 66.44829 26.49301 17.84038 298.1151 1 No
## 516 67.80259 30.71812 38.30935 329.8731 0 Si
## 517 61.87350 30.57216 29.83134 331.7514 1 Si
## 518 68.35314 33.23125 33.89463 325.7949 1 Si
## 519 66.91543 28.45994 21.54576 306.9141 0 No
## 520 68.56772 29.34593 37.97542 328.3699 1 No
## 521 63.93609 30.06678 25.35234 309.5234 1 No
## 522 66.10439 27.56689 27.97353 310.2567 0 No
## 523 65.52149 33.28575 25.45313 319.9598 0 Si
## 524 61.97944 22.82240 15.03649 291.6788 1 No
## 525 66.88737 33.38099 43.74508 336.7173 1 Si
## 526 68.54390 30.50267 29.17172 316.5363 1 No
## 527 64.51542 29.25465 30.52951 316.9084 1 No
## 528 65.98189 29.82920 23.84225 313.7286 0 Si
## 529 69.20557 28.81775 32.25579 317.5470 0 No
## 530 66.35441 32.87215 31.96291 322.2218 0 Si
## 531 69.39227 34.50151 36.95069 337.0068 1 Si
## 532 66.93476 30.81219 29.74172 313.5534 1 No
## 533 66.23277 28.12068 27.54526 312.2036 1 No
## 534 67.32164 26.70882 23.75449 308.9702 1 No
## 535 69.49170 31.65541 28.21811 318.5950 1 Si
## 536 66.68577 27.36054 32.15398 311.9572 1 No
## 537 66.97224 27.49903 31.52956 325.0926 1 Si
## 538 65.79672 30.51424 34.34945 327.2857 1 Si
## 539 69.44869 33.31930 34.97302 325.9348 0 Si
## 540 69.19184 31.06595 36.23726 327.0091 1 Si
## 541 63.12883 31.22747 20.77483 307.5462 1 No
## 542 66.42323 26.38288 30.02292 312.5956 0 No
## 543 69.22254 34.78634 35.11037 329.8591 1 Si
## 544 66.01600 29.39622 23.64147 311.5171 1 No
## 545 66.86412 30.01902 41.01159 330.1233 1 No
## 546 64.32765 29.17566 25.80159 321.4832 1 Si
## 547 66.99444 26.10810 23.89917 312.1486 0 Si
## 548 65.77219 29.73139 27.18170 305.4290 1 No
## 549 65.95829 29.10160 26.05139 310.2497 1 No
## 550 67.54278 31.65615 31.78936 328.0474 1 Si
## 551 69.68205 26.46152 36.60233 324.1934 1 No
## 552 70.15497 37.39540 35.89374 343.0231 0 Si
## 553 69.06708 29.58146 31.83376 319.5035 1 No
## 554 64.13675 27.79042 26.05491 319.8333 1 Si
## 555 70.54930 33.80404 41.77709 347.2833 1 Si
## 556 70.38151 30.68575 37.82676 324.2741 1 No
## 557 64.07766 34.16489 29.11761 328.2308 0 Si
## 558 68.05570 26.26395 28.49519 308.5624 1 No
## 559 69.25371 34.40222 36.76729 331.9363 0 Si
## 560 64.44552 22.18003 17.93130 293.7687 1 No
## 561 67.36076 27.94441 29.63249 322.0304 1 Si
## 562 67.02608 29.70616 26.68604 315.6108 1 Si
## 563 67.94382 36.30208 29.77153 335.4776 1 Si
## 564 67.71082 28.93622 25.81449 312.5060 1 No
## 565 64.21226 27.23220 23.56985 311.7129 0 Si
## 566 67.33559 30.91698 22.83577 312.2232 1 Si
## 567 67.26031 31.40697 35.46232 327.9126 0 Si
## 568 65.37225 24.99690 23.88324 309.8119 0 No
## 569 68.56769 34.17417 36.36979 330.1182 1 Si
## 570 67.32825 35.53194 35.01561 334.4122 0 Si
## 571 64.83686 30.26915 30.93030 329.4699 1 Si
## 572 69.13223 26.51104 32.52172 313.5508 1 No
## 573 66.42731 24.07671 26.16218 313.5603 1 No
## 574 66.66593 31.47148 27.57052 311.9046 1 No
## 575 64.84465 24.42718 25.75127 307.9222 0 No
## 576 63.42287 29.05805 23.81298 322.3020 0 Si
## 577 65.23744 31.61927 33.71052 329.1855 0 Si
## 578 66.86661 29.10941 31.85595 316.4996 1 No
## 579 66.58096 27.66650 32.38892 325.5532 1 Si
## 580 63.51303 34.09929 26.76232 331.0771 1 Si
## 581 66.50639 26.03076 29.61533 310.5241 0 No
## 582 67.29563 27.56507 36.74206 328.8624 1 Si
## 583 66.05993 27.41584 29.20993 320.6071 1 Si
## 584 64.43544 31.45106 31.39429 311.6861 1 No
## 585 68.58241 34.24978 35.13386 330.4062 1 Si
## 586 66.51981 28.46606 31.32682 328.4489 1 Si
## 587 66.98664 28.34687 30.28297 320.9998 1 Si
## 588 64.42373 25.92550 28.53968 308.7999 0 No
## 589 66.92164 34.64459 31.96780 331.9726 0 Si
## 590 70.85322 32.96383 35.31669 322.4832 1 No
## 591 68.59352 29.34075 29.64273 330.7478 1 Si
## 592 68.91508 27.67295 33.58501 328.2776 1 Si
## 593 67.88974 31.41938 37.93727 322.2734 1 No
## 594 64.66927 30.78378 23.77505 316.9595 1 Si
## 595 70.57069 31.20681 25.75509 312.3107 0 No
## 596 68.03959 35.00796 33.40897 333.1150 0 Si
## 597 67.91548 31.36760 35.82635 330.9748 1 Si
## 598 66.82311 24.68332 29.03917 309.1137 1 No
## 599 65.42522 27.40618 25.57449 311.0392 1 No
## 600 64.49616 29.09145 21.91470 304.9343 1 No
## 601 64.40048 26.98674 21.89457 300.5967 0 No
## 602 68.43453 29.78288 32.12457 325.8697 1 Si
## 603 67.17103 30.81486 29.51105 320.7129 0 Si
## 604 64.93253 29.79518 26.02533 315.8074 0 Si
## 605 64.60640 27.14553 26.34816 311.3421 1 No
## 606 67.29449 26.93955 34.87341 320.1175 0 No
## 607 66.46342 29.80708 30.32613 319.2147 0 No
## 608 66.23866 30.48344 30.06075 318.7166 1 No
## 609 66.51626 24.68992 20.06335 300.3099 1 No
## 610 66.18399 28.57596 24.79913 316.8759 1 Si
## 611 71.79794 29.79619 33.79986 315.4508 0 No
## 612 68.12430 30.84571 34.79038 325.6356 1 Si
## 613 63.57009 28.47309 25.09440 307.8012 0 No
## 614 68.25608 25.11541 24.06810 302.0263 0 No
## 615 69.33778 31.32173 32.50078 326.1699 0 Si
## 616 67.41728 31.21002 29.13099 318.4154 1 Si
## 617 69.09560 28.84485 34.35842 321.5045 1 No
## 618 67.25738 28.71971 26.61773 324.1556 0 Si
## 619 66.87659 29.03405 24.44719 309.9389 1 No
## 620 68.24752 30.46554 34.57710 320.5921 1 No
## 621 65.71353 28.07919 25.84142 312.8599 1 No
## 622 70.39338 31.29195 33.40904 326.0514 0 Si
## 623 70.54842 28.08578 35.43081 327.1475 0 Si
## 624 66.80342 31.47317 29.07022 318.9920 1 Si
## 625 68.24428 31.50877 31.62933 323.9951 1 Si
## 626 66.29774 31.89115 25.58843 317.1448 0 Si
## 627 69.32424 35.24125 35.95667 331.5396 1 Si
## 628 64.87715 30.48719 27.15305 310.1959 0 No
## 629 66.98611 28.85275 25.33120 317.0952 0 Si
## 630 65.14085 25.58680 28.52848 305.4430 1 No
## 631 66.95297 25.22230 29.64643 323.2671 1 Si
## 632 69.36832 35.49943 40.59567 340.1236 1 Si
## 633 67.17923 28.12982 31.06418 314.2329 0 No
## 634 67.90226 26.94935 32.57875 322.4254 0 Si
## 635 65.97838 28.32390 31.26239 315.4431 1 No
## 636 65.83879 27.79585 22.79679 311.6862 1 Si
## 637 64.96171 32.33191 30.38690 322.7211 0 Si
## 638 68.44785 28.82544 36.60112 328.0170 0 Si
## 639 68.03972 27.73026 30.63545 316.4378 1 No
## 640 67.19670 34.65860 36.18994 326.1230 0 No
## 641 70.48687 33.28012 36.36436 332.0708 0 Si
## 642 69.09921 32.95183 34.64057 332.1349 0 Si
## 643 64.54450 31.38244 28.26097 314.7421 1 No
## 644 66.97755 34.89369 32.60784 326.0003 0 Si
## 645 63.25928 24.89819 18.62149 304.0119 1 No
## 646 70.38446 39.11634 34.35843 343.2707 1 Si
## 647 67.73657 31.39926 31.53602 332.8785 1 Si
## 648 70.46231 32.90051 41.41096 341.0968 1 Si
## 649 68.21442 35.47751 32.88859 333.0998 1 Si
## 650 71.41176 33.33699 39.43010 325.9834 0 No
## 651 65.74479 26.77831 28.29542 314.1983 1 No
## 652 66.33870 31.65906 34.90160 335.3252 1 Si
## 653 66.05578 30.87118 25.93890 321.0643 0 Si
## 654 63.95818 25.59017 15.78516 301.7126 1 No
## 655 67.29880 32.23737 33.86479 332.1403 1 Si
## 656 64.48509 25.50521 17.28157 288.7985 1 No
## 657 70.88089 36.20063 39.26693 333.6409 0 Si
## 658 65.38531 31.21012 29.02352 326.2389 0 Si
## 659 66.47912 32.45349 28.79905 322.3735 1 Si
## 660 65.99851 31.48573 33.76305 317.8537 0 No
## 661 67.78629 33.18010 33.24383 329.9470 0 Si
## 662 65.29783 28.79073 30.95316 318.5531 0 No
## 663 67.35003 28.87788 33.41060 317.7379 0 No
## 664 69.46956 29.21461 37.52152 324.8539 0 No
## 665 64.82328 26.75996 30.14612 315.5103 0 No
## 666 65.61217 28.06752 20.53959 316.2628 0 Si
## 667 66.43976 31.47551 28.28070 319.9952 1 Si
## 668 64.69149 29.90381 30.82334 322.4147 1 Si
## 669 68.93581 34.14112 31.22764 332.4869 1 Si
## 670 71.08954 33.02296 36.83492 336.4776 1 Si
## 671 65.51430 28.38463 30.40840 307.9759 1 No
## 672 66.29610 29.97808 29.06387 319.1710 1 Si
## 673 64.92135 27.97880 32.41698 316.4045 0 No
## 674 65.02270 28.92880 31.60134 326.9486 0 Si
## 675 68.41850 27.77158 27.76123 325.7766 1 Si
## 676 68.05844 34.31700 36.36483 328.4584 0 Si
## 677 65.67036 29.64557 19.80853 301.5231 1 No
## 678 67.75021 29.91170 38.51388 323.5238 0 No
## 679 68.21533 30.40572 33.32052 327.0615 1 Si
## 680 67.98244 31.18542 22.67465 307.1620 1 No
## 681 63.56664 25.57259 18.43077 303.2064 1 No
## 682 63.69293 28.83013 23.08961 307.3858 1 No
## 683 68.32480 32.68395 32.39407 319.7640 1 No
## 684 69.60261 29.12910 28.88672 310.8668 1 No
## 685 66.45016 30.47705 33.75671 325.9100 1 Si
## 686 67.13936 28.53709 27.87555 312.6465 0 No
## 687 65.45500 26.53574 29.67022 319.3121 1 Si
## 688 69.02912 24.66896 30.43010 310.9386 1 No
## 689 66.92460 28.86949 29.14692 311.9765 1 No
## 690 66.23402 26.01455 28.75609 308.2076 1 No
## 691 66.93577 28.46286 25.80195 311.7107 0 No
## 692 68.09016 32.60342 34.08066 326.5285 0 Si
## 693 69.99533 30.94010 36.71229 342.3975 1 Si
## 694 64.29315 28.58236 26.22755 316.9251 0 Si
## 695 69.76715 30.91114 33.29071 318.5931 1 No
## 696 62.64885 26.72119 27.20097 321.8108 1 Si
## 697 67.97118 32.13175 27.42151 322.4176 1 Si
## 698 69.80034 32.16704 33.79457 329.8872 1 Si
## 699 63.24443 26.78852 30.53630 324.2814 1 Si
## 700 66.71068 31.98095 33.95590 324.4397 0 Si
## 701 69.52510 31.75522 40.23397 340.1064 0 Si
## 702 69.45215 27.69954 31.88922 322.2462 1 Si
## 703 65.49522 28.73001 25.71128 312.0448 1 No
## 704 65.04924 23.09568 24.03795 303.5991 1 No
## 705 65.57671 29.83720 22.88238 319.6501 1 Si
## 706 67.62962 32.26440 31.61017 328.7486 1 Si
## 707 65.53854 26.88637 21.47132 310.4601 1 Si
## 708 64.48055 29.80768 32.10303 326.0211 1 Si
## 709 66.90356 32.74935 28.89468 325.0438 0 Si
## 710 66.79422 29.33207 28.49406 317.1343 0 No
## 711 67.65979 32.78052 33.31821 327.7492 0 Si
## 712 63.19021 31.59902 28.19361 312.1195 0 No
## 713 70.89663 32.60133 36.43743 335.2307 1 Si
## 714 66.61324 25.37633 23.29044 303.7789 0 No
## 715 64.26567 28.51876 23.94065 307.3036 0 No
## 716 69.09660 30.54374 31.61564 324.6196 0 Si
## 717 66.55094 28.73974 24.80363 316.3370 1 Si
## 718 69.00602 27.07916 32.12113 323.4782 1 Si
## 719 71.76807 32.15487 34.66212 334.5740 1 Si
## 720 68.92210 30.85461 32.76996 320.3755 0 No
## 721 68.27461 33.90278 38.03431 327.5892 1 No
## 722 67.04167 30.68917 30.61447 320.4218 0 Si
## 723 69.92752 25.41911 31.17461 315.2718 1 No
## 724 67.90304 30.32579 27.42987 316.4883 1 Si
## 725 67.51306 29.56511 36.49604 334.1788 1 Si
## 726 70.49769 27.13544 26.04479 321.1075 0 Si
## 727 67.83942 34.45080 34.47116 334.8058 1 Si
## 728 67.04791 27.82613 25.05478 320.0825 1 Si
## 729 62.44850 22.20367 14.18187 290.7947 0 No
## 730 65.22906 28.16335 28.69320 316.5671 0 No
## 731 68.29827 25.96263 26.40611 316.2636 1 Si
## 732 64.58601 27.90243 26.38144 302.2963 0 No
## 733 65.71569 22.57257 26.04062 312.0558 0 No
## 734 66.80450 29.70048 35.22541 320.9102 1 No
## 735 68.02429 33.00789 31.19902 321.7717 0 Si
## 736 72.25919 33.18829 34.12459 325.4672 1 Si
## 737 64.12748 31.09591 34.77513 324.9322 1 Si
## 738 70.68663 28.20255 37.21868 327.7644 1 No
## 739 66.51677 26.44769 26.04182 309.3987 1 No
## 740 66.89669 30.96053 33.05509 324.1516 0 Si
## 741 70.14865 26.56876 29.01918 313.5476 1 No
## 742 69.15357 30.73919 33.02892 325.1493 0 Si
## 743 66.62655 30.60813 30.99323 322.5989 1 Si
## 744 66.98041 29.90898 25.54694 309.2369 1 No
## 745 68.02964 29.99491 29.31038 321.6099 0 Si
## 746 68.86517 30.40313 33.57823 331.0980 1 Si
## 747 67.68689 34.52733 36.48749 329.4536 1 Si
## 748 68.09565 28.54629 27.18476 319.6845 0 Si
## 749 68.45080 33.17307 39.29871 332.4580 1 Si
## 750 68.92577 34.78793 34.70356 327.1824 0 Si
## 751 66.82865 28.45661 30.03904 318.1878 0 No
## 752 65.95150 33.37765 27.17691 319.3121 1 Si
## 753 65.88621 29.91939 33.55361 326.2824 1 Si
## 754 66.03121 20.92882 27.67806 302.1254 0 No
## 755 68.71071 31.50939 32.45617 325.4492 0 Si
## 756 67.98654 30.66666 35.33900 323.6551 1 No
## 757 71.63474 29.38480 33.09221 310.0574 1 No
## 758 66.92667 31.52801 31.27688 324.2329 1 Si
## 759 68.36998 25.27306 31.00372 307.9839 0 No
## 760 66.14778 27.74967 38.29337 324.4409 1 No
## 761 65.00395 33.63784 27.66791 325.7848 1 Si
## 762 68.76955 40.35149 41.79419 349.8699 1 Si
## 763 65.08986 31.72547 26.54652 324.1781 1 Si
## 764 62.80328 27.72530 25.98258 315.2428 0 Si
## 765 64.51642 28.45630 25.63621 315.6949 1 Si
## 766 67.76339 29.62252 25.46553 306.9367 0 No
## 767 70.24220 30.87650 32.95938 327.4531 0 Si
## 768 67.24824 33.02097 36.34155 325.7536 0 No
## 769 64.17126 24.87888 18.79091 295.0811 1 No
## 770 66.76494 29.92908 29.45106 319.9097 1 Si
## 771 68.30040 30.09799 32.17605 326.1134 0 Si
## 772 67.58902 28.97670 25.14548 309.2789 0 No
## 773 66.06010 29.79064 24.17129 315.1610 1 Si
## 774 68.83792 30.53063 37.08017 316.4013 1 No
## 775 67.32540 30.98407 27.97873 318.2834 0 Si
## 776 64.21860 26.84226 26.81371 321.4019 1 Si
## 777 66.59580 29.27479 33.44460 318.1071 0 No
## 778 66.83340 25.35809 28.03467 305.5876 0 No
## 779 70.53720 33.74601 32.51980 328.7572 0 Si
## 780 66.58885 35.39537 33.56074 338.6062 0 Si
## 781 66.92960 27.13193 25.96257 315.1917 0 Si
## 782 62.66587 29.31085 21.73038 312.5183 1 Si
## 783 67.77708 25.45570 32.71209 320.5629 0 No
## 784 66.25921 32.30773 27.50045 321.3447 0 Si
## 785 66.44858 28.48142 26.11209 318.9553 0 Si
## 786 63.73955 25.58481 25.40056 310.7526 0 No
## 787 67.96931 25.41097 31.02112 319.6352 1 No
## 788 69.08667 33.83578 25.09425 314.7426 1 Si
## 789 65.57422 30.45516 33.36145 324.8144 1 Si
## 790 66.97754 26.27275 26.84929 318.8153 0 Si
## 791 67.86460 29.32587 32.96949 313.0157 0 No
## 792 63.85396 25.91275 20.38384 302.2925 1 No
## 793 64.05068 27.39238 26.85676 309.9410 1 No
## 794 63.38793 27.14416 23.76218 312.3178 1 Si
## 795 68.09577 31.37712 35.72721 326.3301 1 Si
## 796 64.58673 28.82359 24.39263 307.8878 1 No
## 797 67.63102 25.74283 31.43776 319.3347 0 No
## 798 64.68334 28.66389 23.25744 306.8553 1 No
## 799 65.43882 29.97720 24.98915 308.1299 1 No
## 800 64.99659 30.36704 25.43017 310.4568 1 No
## 801 69.07953 30.71920 34.60555 328.9052 0 Si
## 802 65.20663 30.34776 26.18499 319.7860 1 Si
## 803 69.61596 35.23328 42.74108 343.5063 0 Si
## 804 68.31563 32.42243 29.23919 314.9225 1 No
## 805 65.13603 30.84220 33.66371 331.2475 1 Si
## 806 65.66324 26.51670 31.11964 319.9342 0 No
## 807 66.17626 29.97668 29.65821 318.1257 1 No
## 808 68.79197 31.13246 26.71494 320.3894 1 Si
## 809 67.29248 36.82193 36.76394 342.5891 1 Si
## 810 67.01405 34.93571 38.51554 337.2436 1 Si
## 811 64.88541 31.27468 34.27814 325.8999 1 Si
## 812 68.35307 32.18720 34.37553 336.3833 0 Si
## 813 66.56547 26.39380 26.30963 313.9345 1 No
## 814 68.55698 28.76876 27.70086 311.0612 0 No
## 815 65.59426 29.49915 24.55076 308.5157 0 No
## 816 66.89857 29.34892 26.61910 319.2615 0 Si
## 817 66.36748 31.97631 34.35059 324.7629 1 Si
## 818 66.67219 33.69596 24.44821 315.6769 1 Si
## 819 64.78786 29.69434 23.84475 316.3134 1 Si
## 820 67.46219 32.37500 34.44255 335.0983 1 Si
## 821 68.83058 32.02596 27.59951 318.7496 1 Si
## 822 68.23742 25.38771 28.41453 330.6628 0 Si
## 823 68.20988 28.85318 26.81282 305.8001 1 No
## 824 66.92053 30.29711 25.61656 318.3664 1 Si
## 825 67.38448 29.72828 26.46379 324.5899 0 Si
## 826 66.31245 28.39884 29.99952 320.1659 1 Si
## 827 70.23434 35.21161 32.28176 328.4007 1 Si
## 828 66.77461 29.16927 20.58275 310.8411 1 Si
## 829 66.86471 28.38088 32.36684 319.3957 1 No
## 830 67.76690 28.80364 34.54348 328.1666 1 Si
## 831 64.60334 23.43379 30.70471 318.5242 0 No
## 832 65.24598 30.15533 23.98741 322.1673 0 Si
## 833 69.29600 29.69010 28.22747 312.4425 0 No
## 834 63.99242 23.70040 23.32919 295.2995 0 No
## 835 63.52001 26.81948 23.89061 311.9402 1 Si
## 836 67.72088 32.69924 31.16332 330.3560 0 Si
## 837 64.69312 32.97698 30.69271 332.9003 1 Si
## 838 66.86508 26.60271 28.45328 300.0818 1 No
## 839 66.94463 30.63126 28.83763 329.1163 0 Si
## 840 64.21782 30.18378 27.49621 318.4762 0 Si
## 841 67.01524 29.01855 28.34203 317.3620 1 Si
## 842 65.42853 31.72867 33.89007 332.3599 1 Si
## 843 67.82779 27.21617 29.71018 316.7530 1 No
## 844 67.17567 29.47369 28.90339 308.0648 1 No
## 845 67.82371 31.96399 34.06345 323.4593 1 No
## 846 66.95343 32.71244 29.18393 312.3278 1 No
## 847 67.19695 31.36426 30.60764 319.7523 0 No
## 848 67.69704 35.66547 32.45171 324.0093 0 Si
## 849 67.99035 28.66199 31.47213 321.4738 1 Si
## 850 66.17552 28.68328 26.63418 316.1891 0 Si
## 851 66.55659 27.23888 29.96966 316.9349 0 No
## 852 67.62947 30.68614 38.08379 325.2903 0 No
## 853 67.86330 27.95280 26.90554 324.5864 1 Si
## 854 64.99382 31.32996 28.45344 319.1745 0 Si
## 855 63.94589 26.69399 29.51392 314.9593 1 No
## 856 68.55973 32.60749 34.11964 329.4075 1 Si
## 857 69.25666 32.40150 29.81313 324.8324 1 Si
## 858 70.52823 34.64184 33.43142 323.3846 0 No
## 859 64.18280 29.41585 23.58689 315.6945 1 Si
## 860 69.21625 27.01798 37.53696 325.4019 1 No
## 861 68.87298 32.16586 34.02753 323.6319 1 No
## 862 70.00454 34.99422 38.62682 327.9668 1 No
## 863 66.76968 24.46159 22.04311 306.6793 1 No
## 864 69.35583 32.33835 34.58256 322.2822 0 No
## 865 65.99320 27.74400 30.86685 318.5682 0 No
## 866 69.33364 30.99610 30.40332 309.0045 1 No
## 867 66.46282 28.79535 30.69750 325.9761 1 Si
## 868 64.75933 29.28097 33.34359 326.8174 1 Si
## 869 66.80823 30.92871 27.93285 316.6327 1 No
## 870 72.02332 38.09104 45.66409 355.2446 0 Si
## 871 66.70459 23.99755 29.46432 311.6499 0 No
## 872 64.87769 33.65860 35.77185 339.0901 0 Si
## 873 69.91994 36.00212 37.99623 342.1012 0 Si
## 874 67.44054 25.10183 26.26069 308.3562 1 No
## 875 64.48268 23.88567 20.05566 301.3325 0 No
## 876 66.40128 31.01194 31.88066 329.3348 1 Si
## 877 61.63049 23.01840 20.80514 295.2961 0 No
## 878 67.56650 36.50962 33.20764 329.8917 1 Si
## 879 67.86742 29.36302 21.74958 307.7596 1 No
## 880 67.08827 31.08298 35.03637 341.3543 1 Si
## 881 68.60899 35.19771 39.84897 345.4144 1 Si
## 882 68.23295 30.99289 30.99769 318.2740 0 No
## 883 65.73283 33.48234 23.30479 315.8159 0 Si
## 884 65.06839 26.39378 34.35129 319.8066 1 No
## 885 69.32327 26.43684 35.06031 322.5861 0 No
## 886 70.00057 29.25001 31.32607 332.9415 0 Si
## 887 69.90974 33.89974 34.62632 326.0983 0 Si
## 888 65.99597 31.67713 33.07086 324.6375 0 Si
## 889 68.08904 33.02046 35.98226 321.1942 1 No
## 890 67.28373 28.27554 31.89463 312.0939 1 No
## 891 65.27543 30.55934 30.76559 317.7810 0 No
## 892 65.01885 32.30763 26.88504 326.4014 1 Si
## 893 70.59857 37.51895 34.65054 335.2537 0 Si
## 894 67.35742 31.51786 29.83801 319.2253 1 Si
## 895 70.25070 28.83035 34.59190 320.1826 1 No
## 896 64.61432 32.38639 32.07063 320.2415 0 No
## 897 69.19193 35.54672 36.14887 325.7231 1 No
## 898 67.22536 27.20127 31.90400 323.9962 1 Si
## 899 64.37241 30.05774 21.04340 305.0418 0 No
## 900 65.39098 27.37193 32.13956 317.0771 1 No
## 901 67.91034 32.14941 31.63830 323.3818 0 Si
## 902 68.96110 34.78938 37.39996 341.7936 0 Si
## 903 63.80610 26.56797 22.89280 300.5569 1 No
## 904 67.93660 29.75799 31.65067 325.9965 1 Si
## 905 67.18866 23.57206 17.26964 289.9509 1 No
## 906 66.17264 33.08480 34.89649 339.3988 0 Si
## 907 68.98528 33.27509 30.54228 327.0215 1 Si
## 908 68.98585 33.03334 36.03975 337.4474 0 Si
## 909 67.26311 29.37995 31.59493 321.3476 0 Si
## 910 67.01477 31.80148 34.25213 321.2630 1 No
## 911 65.09200 25.07509 27.34519 309.9670 1 No
## 912 67.40285 28.33194 24.87250 309.2099 1 No
## 913 67.90724 24.83604 25.38782 311.1553 1 No
## 914 65.48867 30.43626 28.69705 312.8998 0 No
## 915 66.18483 31.05764 30.40023 321.9270 1 Si
## 916 64.77537 28.17410 34.22242 320.6012 0 No
## 917 71.27545 32.50275 33.81286 325.8476 1 Si
## 918 65.60872 31.96440 32.54926 322.2724 0 No
## 919 70.51272 34.13732 33.03618 336.1752 0 Si
## 920 67.44530 25.19172 27.88852 315.3779 1 No
## 921 62.08767 24.94625 13.87302 294.3731 1 No
## 922 64.84530 27.10931 26.15522 308.7935 0 No
## 923 63.03220 27.23163 18.62329 305.5705 1 Si
## 924 65.49648 29.94278 25.24348 305.3712 1 No
## 925 65.34068 30.26449 28.01758 320.5537 0 Si
## 926 67.50644 31.78847 29.51350 322.3378 1 Si
## 927 66.28479 28.80740 24.67300 315.2433 0 Si
## 928 63.52921 28.02038 22.32539 302.6941 1 No
## 929 66.07824 26.24358 23.72577 320.4409 1 Si
## 930 66.83675 31.44220 30.93991 318.4017 1 No
## 931 65.84909 29.51376 29.23746 314.5360 1 No
## 932 65.50177 30.77227 30.43056 315.9611 1 No
## 933 66.97372 32.34275 32.04850 325.2276 1 Si
## 934 66.05190 26.19506 26.38798 312.8922 1 No
## 935 67.51284 31.25854 32.40284 331.2993 1 Si
## 936 67.91875 24.63364 20.69560 311.4080 0 Si
## 937 66.23344 32.51766 35.41240 325.6215 1 No
## 938 68.24273 29.30366 26.91209 314.1706 1 No
## 939 66.47464 32.24307 38.01085 329.3725 0 Si
## 940 69.41556 26.93640 37.32914 332.6427 1 Si
## 941 66.92447 29.87046 27.53343 312.2952 0 No
## 942 66.34048 25.41921 19.18070 302.2491 1 No
## 943 69.20368 32.00423 33.87725 330.0055 1 Si
## 944 70.99411 30.33221 39.50636 324.6214 1 No
## 945 68.83363 30.63392 32.20964 327.0998 0 Si
## 946 68.07239 31.08340 30.94417 319.1490 0 No
## 947 67.01824 29.36737 25.49002 325.1917 1 Si
## 948 60.50098 27.36806 24.88923 313.7437 0 Si
## 949 68.29237 29.28472 24.32949 310.9174 0 No
## 950 64.77565 27.01239 26.69635 319.9422 0 Si
## 951 67.58033 28.63544 23.63843 313.3200 0 Si
## 952 64.06250 30.76751 20.28310 315.6814 1 Si
## 953 66.97107 29.63214 27.49768 313.5252 1 No
## 954 69.04145 29.91449 33.75260 317.6133 1 No
## 955 66.08458 27.81921 23.76446 307.0486 0 No
## 956 66.17363 28.11481 31.02966 318.0846 1 No
## 957 66.88312 31.06345 31.00311 334.3977 1 Si
## 958 68.91482 29.97498 32.00012 316.0488 1 No
## 959 63.93697 25.82978 19.30145 307.5768 0 Si
## 960 65.90328 29.57407 29.45064 315.5669 0 No
## 961 67.87731 35.49506 34.94025 332.5668 1 Si
## 962 66.06898 32.55603 39.49376 335.6202 1 Si
## 963 66.24130 28.96906 23.69585 318.4073 1 Si
## 964 66.10752 31.26102 32.27258 330.6542 1 Si
## 965 64.13228 23.82619 25.34491 306.8523 1 No
## 966 71.64538 30.44579 31.88180 318.4829 0 No
## 967 70.38017 28.92481 37.41983 323.5349 1 No
## 968 69.03119 31.36312 34.55906 340.0912 1 Si
## 969 67.98763 25.80102 29.27414 306.3897 1 No
## 970 64.98137 29.68729 24.84705 309.9782 1 No
## 971 66.66892 31.77032 27.92315 325.1639 1 Si
## 972 68.24058 28.43640 27.71633 307.0929 1 No
## 973 62.59370 26.92535 20.95109 300.0420 0 No
## 974 71.06447 30.62458 30.71697 323.5693 1 Si
## 975 67.53903 30.50723 29.01645 312.8906 1 No
## 976 65.25751 29.73409 25.34779 304.0084 1 No
## 977 66.32329 25.24861 26.35082 308.8190 1 No
## 978 65.62469 22.07819 26.33425 300.1748 1 No
## 979 67.00622 29.73061 31.62134 320.9386 1 No
## 980 68.50776 30.66185 33.24112 318.0760 0 No
## 981 65.86744 29.65058 26.36817 313.1361 1 No
## 982 67.45617 27.69443 22.06630 311.8560 0 Si
## 983 64.93045 28.25571 26.70554 312.8933 1 No
## 984 70.34029 32.98208 38.53282 336.1952 1 Si
## 985 65.83997 29.96401 27.56316 324.9924 0 Si
## 986 68.60847 28.78709 36.07035 329.3432 1 Si
## 987 66.29969 30.15208 30.60669 320.0077 0 Si
## 988 66.34954 29.48276 29.94939 308.8707 1 No
## 989 70.35683 28.75118 29.18935 312.6318 1 No
## 990 68.24502 34.91533 26.86669 318.2787 1 Si
## 991 66.50946 28.49194 28.76360 310.3747 1 No
## 992 66.47263 27.42581 21.29415 306.7382 1 No
## 993 65.58825 29.86701 28.14137 315.7103 1 No
## 994 65.15189 24.07161 21.96297 303.2308 1 No
## 995 68.53905 34.57773 35.70771 331.2938 1 Si
## 996 70.02903 28.35341 37.22945 327.4614 0 No
## 997 69.62932 31.98886 29.98818 318.4965 0 No
## 998 65.14514 29.35476 29.10309 317.7610 1 No
## 999 69.11046 32.66572 29.81070 328.1133 0 Si
## 1000 67.87817 32.82945 32.19648 336.1679 1 Si\["Si~ aborto" = 1, "NO~ aborto" = 0\]
Asignación
- Repertir hasta el paso 3, con estos datos (dfa)
Paso 1
## Análisis univariado
univariable_edad <- glm(abortos ~ Edad, family = binomial, data = dfa)
summary(univariable_edad)##
## Call:
## glm(formula = abortos ~ Edad, family = binomial, data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6631 -1.1963 0.8754 1.0875 1.6301
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -12.25316 2.19569 -5.581 2.40e-08 ***
## Edad 0.18565 0.03279 5.663 1.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.2 on 999 degrees of freedom
## Residual deviance: 1344.6 on 998 degrees of freedom
## AIC: 1348.6
##
## Number of Fisher Scoring iterations: 4univariable_dap <- glm(abortos ~ dap, family = binomial, data = dfa)
summary(univariable_dap)##
## Call:
## glm(formula = abortos ~ dap, family = binomial, data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1759 -1.0355 0.4722 0.9586 1.9955
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -10.59004 0.86737 -12.21 <2e-16 ***
## dap 0.36175 0.02913 12.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.2 on 999 degrees of freedom
## Residual deviance: 1168.6 on 998 degrees of freedom
## AIC: 1172.6
##
## Number of Fisher Scoring iterations: 4univariable_rto <- glm(abortos ~ rto, family = binomial, data = dfa)
summary(univariable_rto)##
## Call:
## glm(formula = abortos ~ rto, family = binomial, data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7848 -1.1724 0.8005 1.0574 1.6658
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.99243 0.41843 -7.152 8.58e-13 ***
## rto 0.10626 0.01388 7.655 1.93e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.2 on 999 degrees of freedom
## Residual deviance: 1314.0 on 998 degrees of freedom
## AIC: 1318
##
## Number of Fisher Scoring iterations: 4univariable_clolA <- glm(abortos ~ clolA, family = binomial, data = dfa)
summary(univariable_clolA)##
## Call:
## glm(formula = abortos ~ clolA, family = binomial, data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4107 -0.6139 0.1504 0.5980 2.5297
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -77.92453 4.87198 -15.99 <2e-16 ***
## clolA 0.24468 0.01528 16.02 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.18 on 999 degrees of freedom
## Residual deviance: 812.64 on 998 degrees of freedom
## AIC: 816.64
##
## Number of Fisher Scoring iterations: 5univariable_hib <- glm(abortos ~ hibrido, family = binomial, data = dfa)
summary(univariable_hib)##
## Call:
## glm(formula = abortos ~ hibrido, family = binomial, data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.262 -1.262 1.095 1.095 1.112
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.15492 0.10017 1.547 0.122
## hibrido 0.04271 0.12953 0.330 0.742
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.2 on 999 degrees of freedom
## Residual deviance: 1378.1 on 998 degrees of freedom
## AIC: 1382.1
##
## Number of Fisher Scoring iterations: 3Paso 2
Modelo multivariado
model1 <- glm(abortos ~ Edad + dap + hibrido + rto + clolA, family = binomial, data = dfa)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(model1)##
## Call:
## glm(formula = abortos ~ Edad + dap + hibrido + rto + clolA, family = binomial,
## data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.712e-03 -2.000e-08 2.000e-08 2.000e-08 1.456e-03
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.884e+04 1.711e+06 -0.046 0.963
## Edad -1.361e+01 5.725e+02 -0.024 0.981
## dap -1.260e+01 8.870e+02 -0.014 0.989
## hibrido 7.409e+00 1.436e+03 0.005 0.996
## rto -3.373e+02 7.310e+03 -0.046 0.963
## clolA 2.827e+02 6.146e+03 0.046 0.963
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1.3782e+03 on 999 degrees of freedom
## Residual deviance: 7.9035e-06 on 994 degrees of freedom
## AIC: 12
##
## Number of Fisher Scoring iterations: 25model2 <- glm(abortos ~ Edad + dap + rto + clolA, family = binomial, data = dfa) # Elimiando la variable con el pvalue más alto## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(model2)##
## Call:
## glm(formula = abortos ~ Edad + dap + rto + clolA, family = binomial,
## data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.793e-03 -2.000e-08 2.000e-08 2.000e-08 1.769e-03
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -81753.15 1773349.09 -0.046 0.963
## Edad -12.75 541.67 -0.024 0.981
## dap -12.59 1222.95 -0.010 0.992
## rto -349.43 7555.00 -0.046 0.963
## clolA 292.77 6392.67 0.046 0.963
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1.3782e+03 on 999 degrees of freedom
## Residual deviance: 8.7141e-06 on 995 degrees of freedom
## AIC: 10
##
## Number of Fisher Scoring iterations: 25model3 <- glm(abortos ~ Edad + rto + clolA, family = binomial, data = dfa) # Elimiando la variable con el pvalue más alto## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(model3)##
## Call:
## glm(formula = abortos ~ Edad + rto + clolA, family = binomial,
## data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.341 0.000 0.000 0.000 2.096
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17601.265 9708.152 -1.813 0.0698 .
## Edad -2.968 1.670 -1.777 0.0756 .
## rto -75.747 41.843 -1.810 0.0703 .
## clolA 62.872 34.680 1.813 0.0698 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.183 on 999 degrees of freedom
## Residual deviance: 10.638 on 996 degrees of freedom
## AIC: 18.638
##
## Number of Fisher Scoring iterations: 18modelfinal <- glm(abortos ~ rto + clolA, family = binomial, data = dfa) # Elimiando la variable con el pvalue más alto## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(modelfinal)##
## Call:
## glm(formula = abortos ~ rto + clolA, family = binomial, data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.761 0.000 0.000 0.000 1.896
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4675.199 1311.436 -3.565 0.000364 ***
## rto -20.058 5.611 -3.575 0.000350 ***
## clolA 16.530 4.635 3.566 0.000362 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.18 on 999 degrees of freedom
## Residual deviance: 26.54 on 997 degrees of freedom
## AIC: 32.54
##
## Number of Fisher Scoring iterations: 14\[Variables~ del~ modelo~ final: rto~ y~ CloA\]
pred<-modelfinal$fitted.values
hist(pred)
delta.coef <- abs((coef(model2)-coef(model1)[-c(4)])/coef(model1)[-c(4)])
round(delta.coef, 3)## (Intercept) Edad dap rto clolA
## 0.037 0.063 0.001 0.036 0.036delta.coef1 <- abs((coef(model3)-coef(model2)[-c(3)])/coef(model2)[-c(3)])
round(delta.coef1, 3)## (Intercept) Edad rto clolA
## 0.785 0.767 0.783 0.785delta.coef2 <- abs((coef(modelfinal)-coef(model3)[-c(2)])/coef(model3)[-c(2)])
round(delta.coef2, 3)## (Intercept) rto clolA
## 0.734 0.735 0.737\[\small Ninguna\ eliminación\ desestabiliza\ el\ modelo.\]
library(lmtest)## Warning: package 'lmtest' was built under R version 4.1.2## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.1.2##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numerictest1 <- lrtest(model1, model2)
test2 <- lrtest(model2, model3)
test1## Likelihood ratio test
##
## Model 1: abortos ~ Edad + dap + hibrido + rto + clolA
## Model 2: abortos ~ Edad + dap + rto + clolA
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 6 -3.9518e-06
## 2 5 -4.3570e-06 -1 0 0.9993test2## Likelihood ratio test
##
## Model 1: abortos ~ Edad + dap + rto + clolA
## Model 2: abortos ~ Edad + rto + clolA
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 5 0.0000
## 2 4 -5.3192 -1 10.638 0.001108 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\[\small Se\ puede\ pasar\ del\ modelo\ con\ 5\ variables\ al\ de\ 4,\ pero\ no\ del\ de\ 4\ al\ de\ 3.\]
anova(model3, model2, test = 'Chisq')## Analysis of Deviance Table
##
## Model 1: abortos ~ Edad + rto + clolA
## Model 2: abortos ~ Edad + dap + rto + clolA
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 996 10.638
## 2 995 0.000 1 10.638 0.001108 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\[ \small Teniendo\ en\ cuenta\ los\ resultados,\ la\ variable\ hibrido\ se\ puede\ eliminar.\] ## Paso 3
library(dplyr)## Warning: package 'dplyr' was built under R version 4.1.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#dfa %>%
#group_by(abortos) %>%
#summarise(no_rows = length(abortos))
Edad<-dfa$Edad
dap <-dfa$dap
rto <-dfa$rto
cloA<-dfa$clolA
par(mfrow = c(2,2))
scatter.smooth(Edad, pred, cex = 0.5)
scatter.smooth(dap, pred, cex = 0.5)
scatter.smooth(rto, pred, cex = 0.5)
scatter.smooth(cloA, pred, cex = 0.5)
\[No~ hay~ linearidad~ en~ las~ variables,ya~
que~ los~ valores~ predichos~ son~ de~ 0~ o~ 1.\] ## Analizando
las variables del modelo final
Cloa <- cut(dfa$clolA, breaks = 5)
tabla <- table(Cloa, dfa$abortos)
prop.table(tabla)##
## Cloa No Si
## (289,302] 0.039 0.000
## (302,315] 0.248 0.047
## (315,329] 0.167 0.312
## (329,342] 0.001 0.170
## (342,355] 0.000 0.016rtO <- cut(dfa$rto, breaks = 5)
tabla1 <- table(rtO, dfa$abortos)
prop.table(tabla1)##
## rtO No Si
## (13.8,20.4] 0.023 0.006
## (20.4,27] 0.144 0.115
## (27,33.6] 0.217 0.248
## (33.6,40.2] 0.070 0.162
## (40.2,46.8] 0.001 0.014\[La\ mayoría\ de\ abortos\ se\ da\ para~ contenidos~ de~ clorofila~ A~ medios~, mientras~ que~ los~ No~ abortos~ son~ mayores~ en~ rangos~ dónde~ el~ contenido~ de~ clorofila~ es~ bajo~ \\ Por~ otro~ lado,~ los~ abortos~ y~ no~ abortos~ alcanzan~ su~ máximo~ número~ cuándo~ los~ valores~ de~ rto~ son~ medios.\]
length(dfa$clolA)## [1] 1000probabilidades <- modelfinal$fitted.values
prob <- ifelse(probabilidades < 0.5, 0, 1)
table(prob, dfa$abortos)##
## prob No Si
## 0 452 3
## 1 3 542media_c <- mean(dfa$clolA)
colores_c <- ifelse(dfa$clolA < media_c, 'blue', 'green')
plot(modelfinal$fitted.values, cex = (dfa$clolA * 0.003), pch = 19, col = colores_c)
title("Valores ajustados para cloA")
abline(h = 0.5, cex = 1.2, col = 'red')
media_d <- mean(dfa$rto)
colores_d <- ifelse(dfa$rto < media_d, 'blue', 'green')
plot(modelfinal$fitted.values, cex = (dfa$rto * 0.03), pch = 19, col = colores_c)
title("Valores ajustados para rto")
abline(h = 0.5, cex = 1.2, col = 'red')
\[De~ acuerdo~ a~ las~ variables~ que~
llegaron~ al~ modelo~ final~ (CloA~ y~ rto~),~ se~ observa~ una~
tendencia~ en~ dónde~ los~ valores~ por~ debajo~ de~ la~ media~ se~
encuentran~ relacionados~ en~ mayor~ cantidad~ a~ el~ evento~ de~
"NO aborto",\\ es~ decir~ que~ cuándo~ el~ valor~ de~
CloA~ y~ rto~ sea~ mayor~ a~ la~ media~ probablemente~ haya~ aborto,~
sin~ embargo,~ hay~ valores~ que~ se~ salen~ de~ esa~ presunción~ por~
lo~ que~ se~ puede~ concluir~ que~ no~ son~ variables~ totalmente~
discrimiantorias\] ## Paso 4- Estudiando las interacciones del
modelo
model_inter1 <- glm(abortos ~ rto + clolA + rto:clolA, family = binomial, data = dfa)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(model_inter1)##
## Call:
## glm(formula = abortos ~ rto + clolA + rto:clolA, family = binomial,
## data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.569 0.000 0.000 0.000 2.143
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.486e+03 1.709e+03 -3.210 0.00133 **
## rto -1.595e+01 6.439e+00 -2.477 0.01326 *
## clolA 1.932e+01 6.007e+00 3.217 0.00130 **
## rto:clolA -2.126e-02 1.798e-02 -1.182 0.23710
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.18 on 999 degrees of freedom
## Residual deviance: 25.15 on 996 degrees of freedom
## AIC: 33.15
##
## Number of Fisher Scoring iterations: 15\[No~ existe~ interacción~ entre~ rto~ y~ cloA,~ así~ que~ no~ es~ necesario~ analizarlas~ de~ manera~ conjunta\]
dfa<- dfa|>
mutate(rto_c = ifelse(rto > mean(rto), 'mayor', 'menor'),
cloA_c = ifelse(clolA < mean(clolA), 'baja', 'alta'))
print('rto abortos')## [1] "rto abortos"rto_abortos <- table(dfa$rto_c, dfa$abortos); rto_abortos##
## No Si
## mayor 176 330
## menor 279 215print('cloA abortos')## [1] "cloA abortos"cloa_abortos <- table(dfa$cloA_c, dfa$abortos);cloa_abortos##
## No Si
## alta 79 418
## baja 376 127print("rto aciertos:")## [1] "rto aciertos:"((330+279)/1000)*100## [1] 60.9print("cloA aciertos:")## [1] "cloA aciertos:"((418+376)/1000)*100## [1] 79.4\[cloA~ es~ una~ mejor~ variable~ para~ describir~ los~ abortos~ respecto~ a~ rto~ según~ los~ datos~ observados~,\\ para~ las~ gráficas~ del~ modelo~ ese~ comportamiento~ fue~ menos~ evidente\]
model_inter2=glm(abortos ~rto + clolA + rto:clolA, family = 'binomial', data = dfa)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(model_inter2)##
## Call:
## glm(formula = abortos ~ rto + clolA + rto:clolA, family = "binomial",
## data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.569 0.000 0.000 0.000 2.143
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.486e+03 1.709e+03 -3.210 0.00133 **
## rto -1.595e+01 6.439e+00 -2.477 0.01326 *
## clolA 1.932e+01 6.007e+00 3.217 0.00130 **
## rto:clolA -2.126e-02 1.798e-02 -1.182 0.23710
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.18 on 999 degrees of freedom
## Residual deviance: 25.15 on 996 degrees of freedom
## AIC: 33.15
##
## Number of Fisher Scoring iterations: 15\[El~ modelo~ con~ todas~ las~ interacciones~ no~ muestra~ valores~ estadísticamente~ significativos~ \]
model_inter <- glm(abortos ~ rto + cloA , family = binomial, data = dfa)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(model_inter)##
## Call:
## glm(formula = abortos ~ rto + cloA, family = binomial, data = dfa)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.761 0.000 0.000 0.000 1.896
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4675.199 1311.436 -3.565 0.000364 ***
## rto -20.058 5.611 -3.575 0.000350 ***
## cloA 16.530 4.635 3.566 0.000362 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1378.18 on 999 degrees of freedom
## Residual deviance: 26.54 on 997 degrees of freedom
## AIC: 32.54
##
## Number of Fisher Scoring iterations: 14lrtest(model_inter,model_inter2)## Likelihood ratio test
##
## Model 1: abortos ~ rto + cloA
## Model 2: abortos ~ rto + clolA + rto:clolA
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 3 -13.270
## 2 4 -12.575 1 1.389 0.2386\[Observando~ que~ no~ hay~ diferencias,~ se~ toma~ el~ modelo~ más~ simple\\ siguiendo~ el~ proceso~ de~ eliminación~ del~ paso~ 2\]
#Extrayendo la s predicciones de abortos
rta= model_inter2$fitted.values
prop_ab <- rta*100
cat_rto <- cut(dfa$rto,breaks = 4)
cat_cloA <- cut(dfa$clolA,breaks=4)
data_2 <- data.frame(cat_rto, cat_cloA, prop_ab)
tips2 <- data_2 %>%
group_by(cat_cloA, cat_rto) %>%
summarise(media_prop_abortos = mean(prop_ab))## `summarise()` has grouped output by 'cat_cloA'. You can override using the
## `.groups` argument.# Graficando las dos variables
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.1.2tips2$tip_groups## Warning: Unknown or uninitialised column: `tip_groups`.## NULLggplot(data = tips2) +
aes(x = cat_rto, y = media_prop_abortos, color = cat_cloA) +
geom_line(aes(group = cat_cloA))
\[Las~ líneas~ no~ llegan~ a~ coincidir,~
indicando~ la~ ausencia~ de~ interacción\\ La~ clorofila~ más~ alta~
tiene~ mayor~ probabilidad~ de~ aborto,~ manteniendo~ una~ relación~
proporcional~ con~ la~ probabilidad~ de~ aborto.\\ Entre~ mayor~ sea~
la~ cantidad~ de~ clorofila~ y~ el~ valor~ de~ rto~ mayor~ es~ la~
posibilidad~ de~ que~ la~ planta~ aborte\]
Matriz de confusión valores observados de abortos con valores predichos de aborto
library(ResourceSelection)## Warning: package 'ResourceSelection' was built under R version 4.1.3## ResourceSelection 0.3-5 2019-07-22cut_prob <- ifelse(fitted(model_inter) > 0.5, 1, 0)
table(model_inter$y, cut_prob)## cut_prob
## 0 1
## 0 452 3
## 1 3 542hoslem.test(model_inter$y, fitted(model_inter))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: model_inter$y, fitted(model_inter)
## X-squared = 6.0315e-05, df = 8, p-value = 1\[El~ modelo~ se~ desvía~ en~ tan~ sólo~ 6~ datos~ de~ 1000~ (0,6\%).\\Como~ el~ p.value~ es~ mayor~ a~ 0.05~ no~ hay~ diferencias~ significativas~ entre~ valores~ observados~ y~ predichos\]
Predprob<-predict(model_inter,type="response")
plot(Predprob,jitter(as.numeric(dfa$abortos),0.5), cex=0.5, ylab="Abortos")
abline(v = 0.5, col = 'red')
text(x = 0.8, y = 1.8, 'alta probabilidad de abortos, \n predicha y observada')
text(x = 0.2, y = 1.2, 'alta probabilidad de no abortos, \n predicha y observada')
#library(Deducer)
#rocplot(model2)
#library(lattice)\[Conclusión:~ El~ modelo~ es~ bueno~ para~ predecir~ abortos~ y~ no~ abortos,~ las~ variables~ que~ más~ peso~ parecen~ tener~ según~ los~ modelos~ y~ datos~ generados~ son~ rto~ y~ clorofila~\\ en~ dónde~ contrario~ a~ lo~ que~ es~ normalmente~ en~ la~ realidad~, el~ aumento~ en~ la~ clorofila~ y~ el~ rto~ representa~ una~ mayor~ probabilidad~ de~ aborto\]