title: “ANN-” author: “leela” date: “June 1, 2016” output: html_document
#install.packages("neuralnet")
library("neuralnet")## Warning: package 'neuralnet' was built under R version 3.2.5## Loading required package: grid## Loading required package: MASSdim(infert)## [1] 248 8?infert## starting httpd help server ...## done#uses Resilian propagation
nn=neuralnet(case~age+parity+induced+spontaneous,data=infert,hidden=2,err.fct = "ce",linear.output = FALSE)
plot(nn)
nn$net.result## [[1]]
## [,1]
## 1 0.9999994796
## 2 0.6468901108
## 3 0.1360079275
## 4 0.1439510491
## 5 0.3252413541
## 6 0.4290431427
## 7 0.1427168948
## 8 0.1389927058
## 9 0.2931382097
## 10 0.1377186808
## 11 0.1559078956
## 12 0.4615821111
## 13 0.3040587530
## 14 0.1641065131
## 15 0.6900969156
## 16 0.2814344776
## 17 0.1579575723
## 18 0.4180609931
## 19 0.5790256456
## 20 0.9008557819
## 21 0.8473752783
## 22 0.4964319167
## 23 0.8520110025
## 24 0.7568495617
## 25 0.5482314480
## 26 0.9472100700
## 27 0.9405907319
## 28 0.5906747428
## 29 0.9873252753
## 30 0.1408065127
## 31 0.6273669965
## 32 0.1727420035
## 33 0.6468901108
## 34 0.9034188474
## 35 0.5385865265
## 36 0.9261493685
## 37 0.7568495617
## 38 0.9357534309
## 39 0.1360407121
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## 41 0.7563884578
## 42 0.1653926614
## 43 0.1975959852
## 44 0.8441384962
## 45 0.1519384372
## 46 0.4682768975
## 47 0.8520110025
## 48 0.1392561104
## 49 0.2645551234
## 50 0.7271545835
## 51 1.0000000000
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## 66 0.6273669965
## 67 0.2931382097
## 68 0.9999606816
## 69 0.6955497199
## 70 0.2159723978
## 71 0.3256158338
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## 74 0.1389795480
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## 83 0.3360459679
## 84 0.1356824551
## 85 0.2057418306
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## 94 0.1379833226
## 95 0.1726675112
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## 114 0.1563573283
## 115 0.1727420035
## 116 0.6468901108
## 117 0.1389927058
## 118 0.1840837732
## 119 0.1404309270
## 120 0.1692300084
## 121 0.1880111466
## 122 0.4493650942
## 123 0.2521658375
## 124 0.1464161009
## 125 0.1653926614
## 126 0.1975959852
## 127 0.1417163223
## 128 0.2847319465
## 129 0.1735761520
## 130 0.1536155447
## 131 0.2360983541
## 132 0.1497890704
## 133 0.1452064357
## 134 0.5461073372
## 135 0.1409094449
## 136 0.2064720486
## 137 0.2521658375
## 138 0.1559078956
## 139 0.1830119058
## 140 0.5846341256
## 141 0.3003691881
## 142 0.2521658375
## 143 0.1515862694
## 144 0.4964319167
## 145 0.2159723978
## 146 0.1620028202
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## 153 0.1446167111
## 154 0.3256158338
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## 190 0.8341235407
## 191 0.1443197696
## 192 0.1424830159
## 193 0.1363088037
## 194 0.2493353882
## 195 0.1515548088
## 196 0.1563573283
## 197 0.1398938527
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## 200 0.1840837732
## 201 0.1404309270
## 202 0.1692300084
## 203 0.1880111466
## 204 0.1470080371
## 205 0.1484731717
## 206 0.1368794011
## 207 0.1653926614
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## 213 0.1392561104
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## 224 0.1484731717
## 225 0.1515862694
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## 227 0.2159723978
## 228 0.1620028202
## 229 0.1614281391
## 230 0.8144610423
## 231 0.3256158338
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## 233 0.1608662531
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## 235 0.2159723978
## 236 0.1563573283
## 237 0.2159723978
## 238 0.1540132909
## 239 0.1389795480
## 240 0.5122683003
## 241 0.3040587530
## 242 0.2176422243
## 243 0.3883738150
## 244 0.5912748179
## 245 0.1620028202
## 246 0.4964319167
## 247 0.5187708997
## 248 0.3360459679nn$weights## [[1]]
## [[1]][[1]]
## [,1] [,2]
## [1,] -15.3376080005 5.3770334252
## [2,] -1.4466886467 -0.1168610322
## [3,] -0.6655894585 1.7859617085
## [4,] 10.6647815057 -2.1152179607
## [5,] 22.7053998652 -3.2194118987
##
## [[1]][[2]]
## [,1]
## [1,] 3.431445688
## [2,] 55.061113050
## [3,] -5.283846115nn$result.matrix## 1
## error 122.431838042538
## reached.threshold 0.008929426138
## steps 3943.000000000000
## Intercept.to.1layhid1 -15.337608000541
## age.to.1layhid1 -1.446688646682
## parity.to.1layhid1 -0.665589458545
## induced.to.1layhid1 10.664781505723
## spontaneous.to.1layhid1 22.705399865179
## Intercept.to.1layhid2 5.377033425201
## age.to.1layhid2 -0.116861032154
## parity.to.1layhid2 1.785961708453
## induced.to.1layhid2 -2.115217960712
## spontaneous.to.1layhid2 -3.219411898682
## Intercept.to.case 3.431445687646
## 1layhid.1.to.case 55.061113049651
## 1layhid.2.to.case -5.283846114669nn$covariate## [,1] [,2] [,3] [,4]
## [1,] 26 6 1 2
## [2,] 42 1 1 0
## [3,] 39 6 2 0
## [4,] 34 4 2 0
## [5,] 35 3 1 1
## [6,] 36 4 2 1
## [7,] 23 1 0 0
## [8,] 32 2 0 0
## [9,] 21 1 0 1
## [10,] 28 2 0 0
## [11,] 29 2 1 0
## [12,] 37 4 2 1
## [13,] 31 1 1 0
## [14,] 29 3 2 0
## [15,] 31 2 1 1
## [16,] 27 2 2 0
## [17,] 30 5 2 1
## [18,] 26 1 0 1
## [19,] 25 3 2 1
## [20,] 44 1 0 1
## [21,] 40 1 0 1
## [22,] 35 2 2 0
## [23,] 28 2 0 2
## [24,] 36 1 0 1
## [25,] 27 2 1 1
## [26,] 40 2 0 2
## [27,] 38 2 0 2
## [28,] 34 3 0 2
## [29,] 28 4 1 2
## [30,] 30 4 2 0
## [31,] 32 1 0 1
## [32,] 34 2 1 0
## [33,] 42 1 1 0
## [34,] 32 2 0 2
## [35,] 39 1 1 0
## [36,] 35 2 0 2
## [37,] 36 1 0 1
## [38,] 34 3 1 2
## [39,] 30 3 0 0
## [40,] 28 1 0 1
## [41,] 39 3 0 2
## [42,] 35 1 0 0
## [43,] 41 1 0 0
## [44,] 37 2 1 1
## [45,] 30 1 0 0
## [46,] 37 1 1 0
## [47,] 28 2 0 2
## [48,] 27 4 2 0
## [49,] 26 2 2 0
## [50,] 38 3 0 2
## [51,] 24 3 1 2
## [52,] 36 5 1 2
## [53,] 27 3 1 1
## [54,] 28 1 0 1
## [55,] 29 2 0 2
## [56,] 36 2 0 2
## [57,] 28 2 1 0
## [58,] 28 2 0 2
## [59,] 28 1 0 1
## [60,] 27 2 0 2
## [61,] 35 2 0 2
## [62,] 25 1 0 1
## [63,] 34 1 0 1
## [64,] 31 2 0 2
## [65,] 26 2 1 0
## [66,] 32 1 0 1
## [67,] 21 1 0 1
## [68,] 28 3 1 2
## [69,] 37 3 0 2
## [70,] 25 1 1 0
## [71,] 32 1 1 0
## [72,] 25 1 0 1
## [73,] 31 1 0 1
## [74,] 38 6 0 2
## [75,] 26 2 0 2
## [76,] 31 1 0 1
## [77,] 31 2 0 1
## [78,] 25 1 1 0
## [79,] 31 1 0 1
## [80,] 34 1 0 1
## [81,] 35 2 2 0
## [82,] 29 1 0 1
## [83,] 23 1 0 1
## [84,] 26 6 2 0
## [85,] 42 1 0 0
## [86,] 39 6 2 0
## [87,] 34 4 0 1
## [88,] 35 3 2 0
## [89,] 36 4 1 1
## [90,] 23 1 0 0
## [91,] 32 2 2 0
## [92,] 21 1 0 1
## [93,] 28 2 0 1
## [94,] 29 2 0 0
## [95,] 37 4 1 1
## [96,] 31 1 0 0
## [97,] 29 3 0 1
## [98,] 31 2 1 0
## [99,] 27 2 1 0
## [100,] 30 5 0 2
## [101,] 26 1 0 0
## [102,] 25 3 0 1
## [103,] 44 1 0 0
## [104,] 40 1 0 0
## [105,] 35 2 0 0
## [106,] 28 2 0 0
## [107,] 36 1 0 0
## [108,] 27 2 0 1
## [109,] 40 2 0 0
## [110,] 38 2 0 0
## [111,] 34 3 0 0
## [112,] 28 4 0 2
## [113,] 30 4 1 1
## [114,] 32 1 0 0
## [115,] 34 2 1 0
## [116,] 42 1 1 0
## [117,] 32 2 0 0
## [118,] 39 1 0 0
## [119,] 35 2 0 0
## [120,] 36 1 0 0
## [121,] 34 3 2 0
## [122,] 30 3 0 2
## [123,] 28 1 1 0
## [124,] 39 3 1 0
## [125,] 35 1 0 0
## [126,] 41 1 0 0
## [127,] 37 2 0 0
## [128,] 30 1 1 0
## [129,] 37 1 0 0
## [130,] 28 2 1 0
## [131,] 27 4 2 1
## [132,] 26 2 1 0
## [133,] 38 3 1 0
## [134,] 24 3 2 1
## [135,] 36 5 1 1
## [136,] 27 3 1 1
## [137,] 28 1 1 0
## [138,] 29 2 1 0
## [139,] 36 2 1 0
## [140,] 28 2 1 1
## [141,] 28 2 2 0
## [142,] 28 1 1 0
## [143,] 27 2 1 0
## [144,] 35 2 2 0
## [145,] 25 1 1 0
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## [147,] 31 2 0 0
## [148,] 26 2 0 1
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## [150,] 21 1 0 1
## [151,] 28 3 2 0
## [152,] 37 3 1 1
## [153,] 25 1 0 0
## [154,] 32 1 1 0
## [155,] 25 1 0 0
## [156,] 31 1 0 1
## [157,] 26 2 0 2
## [158,] 31 1 0 0
## [159,] 31 2 2 0
## [160,] 25 1 0 0
## [161,] 31 1 0 0
## [162,] 34 1 0 0
## [163,] 35 2 0 0
## [164,] 29 1 0 1
## [165,] 23 1 0 1
## [166,] 26 6 2 0
## [167,] 42 1 0 0
## [168,] 39 6 2 0
## [169,] 34 4 0 2
## [170,] 35 3 0 0
## [171,] 36 4 0 2
## [172,] 23 1 0 0
## [173,] 32 2 0 1
## [174,] 21 1 1 0
## [175,] 28 2 0 1
## [176,] 29 2 0 1
## [177,] 37 4 0 1
## [178,] 31 1 0 0
## [179,] 29 3 0 2
## [180,] 31 2 1 0
## [181,] 27 2 0 0
## [182,] 30 5 1 2
## [183,] 26 1 1 0
## [184,] 25 3 1 1
## [185,] 44 1 1 0
## [186,] 40 1 0 0
## [187,] 35 2 0 0
## [188,] 28 2 2 0
## [189,] 36 1 0 1
## [190,] 27 2 0 2
## [191,] 40 2 0 0
## [192,] 38 2 0 0
## [193,] 34 3 0 0
## [194,] 28 4 2 1
## [195,] 30 4 1 1
## [196,] 32 1 0 0
## [197,] 34 2 0 0
## [198,] 42 1 0 0
## [199,] 32 2 2 0
## [200,] 39 1 0 0
## [201,] 35 2 0 0
## [202,] 36 1 0 0
## [203,] 34 3 2 0
## [204,] 30 3 0 1
## [205,] 28 1 0 0
## [206,] 39 3 0 0
## [207,] 35 1 0 0
## [208,] 41 1 0 0
## [209,] 37 2 0 0
## [210,] 30 1 0 0
## [211,] 37 1 0 1
## [212,] 28 2 1 0
## [213,] 27 4 2 0
## [214,] 26 2 1 0
## [215,] 38 3 1 0
## [216,] 24 3 2 0
## [217,] 36 5 2 1
## [218,] 27 3 2 0
## [219,] 28 1 0 1
## [220,] 29 2 1 1
## [221,] 36 2 0 1
## [222,] 28 2 2 0
## [223,] 28 2 1 0
## [224,] 28 1 0 0
## [225,] 27 2 1 0
## [226,] 35 2 1 0
## [227,] 25 1 1 0
## [228,] 34 1 0 0
## [229,] 31 2 1 0
## [230,] 26 2 0 2
## [231,] 32 1 1 0
## [232,] 21 1 0 0
## [233,] 28 3 2 0
## [234,] 37 3 0 2
## [235,] 25 1 1 0
## [236,] 32 1 0 0
## [237,] 25 1 1 0
## [238,] 31 1 0 0
## [239,] 38 6 0 2
## [240,] 26 2 1 1
## [241,] 31 1 1 0
## [242,] 31 2 0 1
## [243,] 25 1 0 1
## [244,] 31 1 0 1
## [245,] 34 1 0 0
## [246,] 35 2 2 0
## [247,] 29 1 0 1
## [248,] 23 1 0 1infert$case## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [106] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [141] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [176] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [211] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [246] 0 0 0nn$net.result[[1]]## [,1]
## 1 0.9999994796
## 2 0.6468901108
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## 4 0.1439510491
## 5 0.3252413541
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## 190 0.8341235407
## 191 0.1443197696
## 192 0.1424830159
## 193 0.1363088037
## 194 0.2493353882
## 195 0.1515548088
## 196 0.1563573283
## 197 0.1398938527
## 198 0.2057418306
## 199 0.3990560253
## 200 0.1840837732
## 201 0.1404309270
## 202 0.1692300084
## 203 0.1880111466
## 204 0.1470080371
## 205 0.1484731717
## 206 0.1368794011
## 207 0.1653926614
## 208 0.1975959852
## 209 0.1417163223
## 210 0.1519384372
## 211 0.7834497078
## 212 0.1536155447
## 213 0.1392561104
## 214 0.1497890704
## 215 0.1452064357
## 216 0.1512418209
## 217 0.1818708075
## 218 0.1580018411
## 219 0.4836110879
## 220 0.6208336470
## 221 0.2877915668
## 222 0.3003691881
## 223 0.1536155447
## 224 0.1484731717
## 225 0.1515862694
## 226 0.1775555507
## 227 0.2159723978
## 228 0.1620028202
## 229 0.1614281391
## 230 0.8144610423
## 231 0.3256158338
## 232 0.1412199315
## 233 0.1608662531
## 234 0.6955497199
## 235 0.2159723978
## 236 0.1563573283
## 237 0.2159723978
## 238 0.1540132909
## 239 0.1389795480
## 240 0.5122683003
## 241 0.3040587530
## 242 0.2176422243
## 243 0.3883738150
## 244 0.5912748179
## 245 0.1620028202
## 246 0.4964319167
## 247 0.5187708997
## 248 0.3360459679nn1=ifelse(nn$net.result[[1]]>0.5,1,0)
nn1## [,1]
## 1 1
## 2 1
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 1
## 16 0
## 17 0
## 18 0
## 19 1
## 20 1
## 21 1
## 22 0
## 23 1
## 24 1
## 25 1
## 26 1
## 27 1
## 28 1
## 29 1
## 30 0
## 31 1
## 32 0
## 33 1
## 34 1
## 35 1
## 36 1
## 37 1
## 38 1
## 39 0
## 40 0
## 41 1
## 42 0
## 43 0
## 44 1
## 45 0
## 46 0
## 47 1
## 48 0
## 49 0
## 50 1
## 51 1
## 52 0
## 53 0
## 54 0
## 55 1
## 56 1
## 57 0
## 58 1
## 59 0
## 60 1
## 61 1
## 62 0
## 63 1
## 64 1
## 65 0
## 66 1
## 67 0
## 68 1
## 69 1
## 70 0
## 71 0
## 72 0
## 73 1
## 74 0
## 75 1
## 76 1
## 77 0
## 78 0
## 79 1
## 80 1
## 81 0
## 82 1
## 83 0
## 84 0
## 85 0
## 86 0
## 87 0
## 88 0
## 89 0
## 90 0
## 91 0
## 92 0
## 93 0
## 94 0
## 95 0
## 96 0
## 97 0
## 98 0
## 99 0
## 100 0
## 101 0
## 102 0
## 103 0
## 104 0
## 105 0
## 106 0
## 107 0
## 108 0
## 109 0
## 110 0
## 111 0
## 112 0
## 113 0
## 114 0
## 115 0
## 116 1
## 117 0
## 118 0
## 119 0
## 120 0
## 121 0
## 122 0
## 123 0
## 124 0
## 125 0
## 126 0
## 127 0
## 128 0
## 129 0
## 130 0
## 131 0
## 132 0
## 133 0
## 134 1
## 135 0
## 136 0
## 137 0
## 138 0
## 139 0
## 140 1
## 141 0
## 142 0
## 143 0
## 144 0
## 145 0
## 146 0
## 147 0
## 148 0
## 149 0
## 150 0
## 151 0
## 152 0
## 153 0
## 154 0
## 155 0
## 156 1
## 157 1
## 158 0
## 159 0
## 160 0
## 161 0
## 162 0
## 163 0
## 164 1
## 165 0
## 166 0
## 167 0
## 168 0
## 169 0
## 170 0
## 171 0
## 172 0
## 173 0
## 174 0
## 175 0
## 176 0
## 177 0
## 178 0
## 179 0
## 180 0
## 181 0
## 182 0
## 183 0
## 184 0
## 185 1
## 186 0
## 187 0
## 188 0
## 189 1
## 190 1
## 191 0
## 192 0
## 193 0
## 194 0
## 195 0
## 196 0
## 197 0
## 198 0
## 199 0
## 200 0
## 201 0
## 202 0
## 203 0
## 204 0
## 205 0
## 206 0
## 207 0
## 208 0
## 209 0
## 210 0
## 211 1
## 212 0
## 213 0
## 214 0
## 215 0
## 216 0
## 217 0
## 218 0
## 219 0
## 220 1
## 221 0
## 222 0
## 223 0
## 224 0
## 225 0
## 226 0
## 227 0
## 228 0
## 229 0
## 230 1
## 231 0
## 232 0
## 233 0
## 234 1
## 235 0
## 236 0
## 237 0
## 238 0
## 239 0
## 240 1
## 241 0
## 242 0
## 243 0
## 244 1
## 245 0
## 246 0
## 247 1
## 248 0misClassificationError=mean(infert$case !=nn1)
misClassificationError## [1] 0.2338709677outputvspred=cbind(infert$case,nn1)
outputvspred## [,1] [,2]
## 1 1 1
## 2 1 1
## 3 1 0
## 4 1 0
## 5 1 0
## 6 1 0
## 7 1 0
## 8 1 0
## 9 1 0
## 10 1 0
## 11 1 0
## 12 1 0
## 13 1 0
## 14 1 0
## 15 1 1
## 16 1 0
## 17 1 0
## 18 1 0
## 19 1 1
## 20 1 1
## 21 1 1
## 22 1 0
## 23 1 1
## 24 1 1
## 25 1 1
## 26 1 1
## 27 1 1
## 28 1 1
## 29 1 1
## 30 1 0
## 31 1 1
## 32 1 0
## 33 1 1
## 34 1 1
## 35 1 1
## 36 1 1
## 37 1 1
## 38 1 1
## 39 1 0
## 40 1 0
## 41 1 1
## 42 1 0
## 43 1 0
## 44 1 1
## 45 1 0
## 46 1 0
## 47 1 1
## 48 1 0
## 49 1 0
## 50 1 1
## 51 1 1
## 52 1 0
## 53 1 0
## 54 1 0
## 55 1 1
## 56 1 1
## 57 1 0
## 58 1 1
## 59 1 0
## 60 1 1
## 61 1 1
## 62 1 0
## 63 1 1
## 64 1 1
## 65 1 0
## 66 1 1
## 67 1 0
## 68 1 1
## 69 1 1
## 70 1 0
## 71 1 0
## 72 1 0
## 73 1 1
## 74 1 0
## 75 1 1
## 76 1 1
## 77 1 0
## 78 1 0
## 79 1 1
## 80 1 1
## 81 1 0
## 82 1 1
## 83 1 0
## 84 0 0
## 85 0 0
## 86 0 0
## 87 0 0
## 88 0 0
## 89 0 0
## 90 0 0
## 91 0 0
## 92 0 0
## 93 0 0
## 94 0 0
## 95 0 0
## 96 0 0
## 97 0 0
## 98 0 0
## 99 0 0
## 100 0 0
## 101 0 0
## 102 0 0
## 103 0 0
## 104 0 0
## 105 0 0
## 106 0 0
## 107 0 0
## 108 0 0
## 109 0 0
## 110 0 0
## 111 0 0
## 112 0 0
## 113 0 0
## 114 0 0
## 115 0 0
## 116 0 1
## 117 0 0
## 118 0 0
## 119 0 0
## 120 0 0
## 121 0 0
## 122 0 0
## 123 0 0
## 124 0 0
## 125 0 0
## 126 0 0
## 127 0 0
## 128 0 0
## 129 0 0
## 130 0 0
## 131 0 0
## 132 0 0
## 133 0 0
## 134 0 1
## 135 0 0
## 136 0 0
## 137 0 0
## 138 0 0
## 139 0 0
## 140 0 1
## 141 0 0
## 142 0 0
## 143 0 0
## 144 0 0
## 145 0 0
## 146 0 0
## 147 0 0
## 148 0 0
## 149 0 0
## 150 0 0
## 151 0 0
## 152 0 0
## 153 0 0
## 154 0 0
## 155 0 0
## 156 0 1
## 157 0 1
## 158 0 0
## 159 0 0
## 160 0 0
## 161 0 0
## 162 0 0
## 163 0 0
## 164 0 1
## 165 0 0
## 166 0 0
## 167 0 0
## 168 0 0
## 169 0 0
## 170 0 0
## 171 0 0
## 172 0 0
## 173 0 0
## 174 0 0
## 175 0 0
## 176 0 0
## 177 0 0
## 178 0 0
## 179 0 0
## 180 0 0
## 181 0 0
## 182 0 0
## 183 0 0
## 184 0 0
## 185 0 1
## 186 0 0
## 187 0 0
## 188 0 0
## 189 0 1
## 190 0 1
## 191 0 0
## 192 0 0
## 193 0 0
## 194 0 0
## 195 0 0
## 196 0 0
## 197 0 0
## 198 0 0
## 199 0 0
## 200 0 0
## 201 0 0
## 202 0 0
## 203 0 0
## 204 0 0
## 205 0 0
## 206 0 0
## 207 0 0
## 208 0 0
## 209 0 0
## 210 0 0
## 211 0 1
## 212 0 0
## 213 0 0
## 214 0 0
## 215 0 0
## 216 0 0
## 217 0 0
## 218 0 0
## 219 0 0
## 220 0 1
## 221 0 0
## 222 0 0
## 223 0 0
## 224 0 0
## 225 0 0
## 226 0 0
## 227 0 0
## 228 0 0
## 229 0 0
## 230 0 1
## 231 0 0
## 232 0 0
## 233 0 0
## 234 0 1
## 235 0 0
## 236 0 0
## 237 0 0
## 238 0 0
## 239 0 0
## 240 0 1
## 241 0 0
## 242 0 0
## 243 0 0
## 244 0 1
## 245 0 0
## 246 0 0
## 247 0 1
## 248 0 0## using back propagation algorithm
nn.bp=neuralnet(case~age+parity+induced+spontaneous,data=infert,hidden=2,learningrate = 0.01,algorithm = "backprop",err.fct = "ce",linear.output = FALSE)
nn.bp## Call: neuralnet(formula = case ~ age + parity + induced + spontaneous, data = infert, hidden = 2, learningrate = 0.01, algorithm = "backprop", err.fct = "ce", linear.output = FALSE)
##
## 1 repetition was calculated.
##
## Error Reached Threshold Steps
## 1 158.0855556 0.004832906488 12plot(nn.bp)
nn # error rate is less when compared to back propagation## Call: neuralnet(formula = case ~ age + parity + induced + spontaneous, data = infert, hidden = 2, err.fct = "ce", linear.output = FALSE)
##
## 1 repetition was calculated.
##
## Error Reached Threshold Steps
## 1 122.431838 0.008929426138 3943# using nn for prediction
new.output=compute(nn,covariate = matrix(c(22,1,0,0,
22,1,0,0,
22,1,0,1,
22,1,1,1),
byrow=TRUE,ncol=4))
new.output$net.result## [,1]
## [1,] 0.1419239639
## [2,] 0.1419239639
## [3,] 0.3134496668
## [4,] 0.8491240207##############################
#### confidence interval######
#############################
ci=confidence.interval(nn,alpha = 0.05)
ci## $lower.ci
## $lower.ci[[1]]
## $lower.ci[[1]][[1]]
## [,1] [,2]
## [1,] -15.563094198 1.627916766
## [2,] -1.975123522 -0.293346237
## [3,] -1.915324422 -2.259790376
## [4,] 7.017020197 -6.086122813
## [5,] 15.345787369 -10.085124185
##
## $lower.ci[[1]][[2]]
## [,1]
## [1,] -12.02392121
## [2,] 53.77197102
## [3,] -20.92319733
##
##
##
## $upper.ci
## $upper.ci[[1]]
## $upper.ci[[1]][[1]]
## [,1] [,2]
## [1,] -15.1121218031 9.12615008422
## [2,] -0.9182537715 0.05962417266
## [3,] 0.5841455046 5.83171379300
## [4,] 14.3125428148 1.85568689118
## [5,] 30.0650123615 3.64630038745
##
## $upper.ci[[1]][[2]]
## [,1]
## [1,] 18.88681258
## [2,] 56.35025508
## [3,] 10.35550510
##
##
##
## $nic
## [1] 130.8731172##############################
### VIsualize the results ###
#############################
par(mfrow=c(2,2))
gwplot(nn,selected.covariate="age",min=-2.5,max = 5)
gwplot(nn,selected.covariate="parity",min=-2.5,max = 5)
gwplot(nn,selected.covariate="induced",min=-2.5,max = 5)
gwplot(nn,selected.covariate="spontaneous",min=-2.5,max = 5)