Method1: Neuralnet
library(neuralnet)## Loading required package: grid
## Loading required package: MASS#?infert #Data source
dim(infert)## [1] 248 8#?neuralnet
nn=neuralnet(case~age+parity+induced+spontaneous,data=infert,hidden=2,err.fct="ce",linear.output=FALSE) #Because the outcome is logistical not linear
#hidden mean the constant intercept here(blue)
nn## 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
## 1 123.8188988 0.009886243403 14194plot(nn)
nn$net.result #Classification## [[1]]
## [,1]
## 1 0.15393231441329
## 2 0.59566834982867
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## 247 0.53012992683176
## 248 0.35989137902396nn$weights ## [[1]]
## [[1]][[1]]
## [,1] [,2]
## [1,] 3.79222177306 5.4812931836
## [2,] -2.98696789505 -0.1135839746
## [3,] 2.22424173705 1.9375378074
## [4,] 27.89550162397 -2.4983429111
## [5,] 0.02273745222 -3.7423648008
##
## [[1]][[2]]
## [,1]
## [1,] 2.900003138
## [2,] -1080.193861320
## [3,] -4.675214309nn$result.matrix #summary## 1
## error 123.818898815850
## reached.threshold 0.009886243403
## steps 14194.000000000000
## Intercept.to.1layhid1 3.792221773056
## age.to.1layhid1 -2.986967895048
## parity.to.1layhid1 2.224241737047
## induced.to.1layhid1 27.895501623966
## spontaneous.to.1layhid1 0.022737452216
## Intercept.to.1layhid2 5.481293183636
## age.to.1layhid2 -0.113583974648
## parity.to.1layhid2 1.937537807408
## induced.to.1layhid2 -2.498342911082
## spontaneous.to.1layhid2 -3.742364800794
## Intercept.to.case 2.900003137905
## 1layhid.1.to.case -1080.193861319759
## 1layhid.2.to.case -4.675214308834nn$covariate #Show the variables ## [,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
## [146,] 34 1 0 0
## [147,] 31 2 0 0
## [148,] 26 2 0 1
## [149,] 32 1 0 0
## [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$case #Real outcome## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [71] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [106] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [141] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [176] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [211] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [246] 0 0 0nn$net.result[[1]] #Predict outcome using the train data## [,1]
## 1 0.15393231441329
## 2 0.59566834982867
## 3 0.14516313577733
## 4 0.15226184852471
## 5 0.35708627802210
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## 118 0.17491849181837
## 119 0.14757015027667
## 120 0.16608615640953
## 121 0.19800903126944
## 122 0.52476676684681
## 123 0.25403200575170
## 124 0.15231485980226
## 125 0.16377064775903
## 126 0.18281127400204
## 127 0.14825484117184
## 128 0.28265322088846
## 129 0.16869054650633
## 130 0.15977161320932
## 131 0.29421846939633
## 132 0.15671280241912
## 133 0.15151261733332
## 134 0.01706085484216
## 135 0.14950270352728
## 136 0.22948575603004
## 137 0.25403200575170
## 138 0.16159021458046
## 139 0.18253458543536
## 140 0.65429664172827
## 141 0.33721427355886
## 142 0.25403200575170
## 143 0.15815328914271
## 144 0.52964908037439
## 145 0.22159237700764
## 146 0.16171135856014
## 147 0.14659169205973
## 148 0.18697273766904
## 149 0.15824921851621
## 150 0.31712746099080
## 151 0.17123008214726
## 152 0.40712127548404
## 153 0.15087971906469
## 154 0.31805467543904
## 155 0.15087971906469
## 156 0.59415563841970
## 157 0.84763534459698
## 158 0.15679820060897
## 159 0.40997406366950
## 160 0.15087971906469
## 161 0.15679820060897
## 162 0.16171135856014
## 163 0.14757015027667
## 164 0.53012992683176
## 165 0.35989137902396
## 166 0.00001364255274
## 167 0.18751945701512
## 168 0.14516313577733
## 169 0.23887270469096
## 170 0.14527978734780
## 171 0.26369723266552
## 172 0.14965737188372
## 173 0.23011197802549
## 174 0.19279788405130
## 175 0.19810326593083
## 176 0.20474477503670
## 177 0.14783597452023
## 178 0.15679820060897
## 179 0.49328987759573
## 180 0.16593299479476
## 181 0.14597159820370
## 182 0.24980859269390
## 183 0.23117967816182
## 184 0.21172899268451
## 185 0.65759404992524
## 186 0.17863093337206
## 187 0.14757015027667
## 188 0.33721427355886
## 189 0.73843863709379
## 190 0.85981444618755
## 191 0.14962583672582
## 192 0.14866060285144
## 193 0.14523850631656
## 194 0.31352813598057
## 195 0.16126890519138
## 196 0.15824921851621
## 197 0.14728220476893
## 198 0.18751945701512
## 199 0.43789300757506
## 200 0.17491849181837
## 201 0.14757015027667
## 202 0.16608615640953
## 203 0.19800903126944
## 204 0.15417492151812
## 205 0.15333114951615
## 206 0.14550097315762
## 207 0.16377064775903
## 208 0.18281127400204
## 209 0.14825484117184
## 210 0.15550633042136
## 211 0.76197664635892
## 212 0.15977161320932
## 213 0.14750027529159
## 214 0.15671280241912
## 215 0.15151261733332
## 216 0.00173456469897
## 217 0.20346633699350
## 218 0.16826228502981
## 219 0.49853573503472
## 220 0.68356704883408
## 221 0.28097837242691
## 222 0.33721427355886
## 223 0.15977161320932
## 224 0.15333114951615
## 225 0.15815328914271
## 226 0.17838523965602
## 227 0.22159237700764
## 228 0.16171135856014
## 229 0.16593299479476
## 230 0.84763534459698
## 231 0.31805467543904
## 232 0.14868567778885
## 233 0.17123008214726
## 234 0.73427025518650
## 235 0.22159237700764
## 236 0.15824921851621
## 237 0.22159237700764
## 238 0.15679820060897
## 239 0.14778008821079
## 240 0.59216148759090
## 241 0.29944921753995
## 242 0.22064264734628
## 243 0.41038248732414
## 244 0.59415563841970
## 245 0.16171135856014
## 246 0.52964908037439
## 247 0.53012992683176
## 248 0.35989137902396#check the error rate
nn1= ifelse(nn$net.result[[1]]>0.5,1,0)
nn1## [,1]
## 1 0
## 2 1
## 3 0
## 4 0
## 5 0
## 6 1
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 1
## 13 0
## 14 0
## 15 1
## 16 0
## 17 0
## 18 0
## 19 1
## 20 1
## 21 1
## 22 1
## 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 1
## 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 1
## 123 0
## 124 0
## 125 0
## 126 0
## 127 0
## 128 0
## 129 0
## 130 0
## 131 0
## 132 0
## 133 0
## 134 0
## 135 0
## 136 0
## 137 0
## 138 0
## 139 0
## 140 1
## 141 0
## 142 0
## 143 0
## 144 1
## 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 1
## 247 1
## 248 0misClsificationError= mean(infert$case!=nn1)
misClsificationError #23.39%## [1] 0.2298387097OutPutVsPred=cbind(infert$case,nn1)
OutPutVsPred## [,1] [,2]
## 1 1 0
## 2 1 1
## 3 1 0
## 4 1 0
## 5 1 0
## 6 1 1
## 7 1 0
## 8 1 0
## 9 1 0
## 10 1 0
## 11 1 0
## 12 1 1
## 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 1
## 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 1
## 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 1
## 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 0
## 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 1
## 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 1
## 247 0 1
## 248 0 0###Using Backpropagation Algorithm and playing with learningrate and entorpy
nn.bp=neuralnet(
formula= 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
## 1 158.0855558 0.008284001727 20nn ##nn did a better job## 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
## 1 123.8188988 0.009886243403 14194##Prediction
new.output=compute(nn,covariate=matrix(c(22,1,0,0,22,1,1,0,22,1,0,1,22,1,1,1),byrow=TRUE,ncol=4))
new.output$net.result #From the result, we can find the 4th person who are 22,parity, induced and spontaneous has a 85.19% change to be control in term of case.## [,1]
## [1,] 0.1491437113
## [2,] 0.1987688654
## [3,] 0.3375371732
## [4,] 0.8715737411#We make the outcome as 1 if the percentage is more than 50%
nn2= ifelse(new.output$net.result>0.5,1,0)
nn2 #Only the 4th person is 1## [,1]
## [1,] 0
## [2,] 0
## [3,] 0
## [4,] 1###Confidence Intervals
ci=confidence.interval(nn,alpha=0.05) #it does not work for the model with BackPropagation algorithm
ci## $lower.ci
## $lower.ci[[1]]
## $lower.ci[[1]][[1]]
## [,1] [,2]
## [1,] -2.782284630 -0.8527047096
## [2,] -4.444022524 -0.4072937480
## [3,] 1.238925075 -7.0327039521
## [4,] 14.746489356 -12.4066059982
## [5,] -1.154477148 -20.5144604437
##
## $lower.ci[[1]][[2]]
## [,1]
## [1,] -17.89673156
## [2,] -1081.86542931
## [3,] -26.03144144
##
##
##
## $upper.ci
## $upper.ci[[1]]
## $upper.ci[[1]][[1]]
## [,1] [,2]
## [1,] 10.366728176 11.8152910769
## [2,] -1.529913266 0.1801257987
## [3,] 3.209558399 10.9077795669
## [4,] 41.044513892 7.4099201761
## [5,] 1.199952053 13.0297308421
##
## $upper.ci[[1]][[2]]
## [,1]
## [1,] 23.69673784
## [2,] -1078.52229333
## [3,] 16.68101282
##
##
##
## $nic
## [1] 134.833922###Visualize the Results
##Split the network into different plot
par(mfrow=c(2,2))
gwplot(nn,selected.covariate="age",min=-2.5,max=5)
gwplot(nn,selected.covariate="parity",min=-2.5,max=5)
gwplot(nn,selected.covariate="induced",min=-2.5,max=5)
gwplot(nn,selected.covariate="spontaneous",min=-2.5,max=5)Method2:Multinomial Log-linear Models
library(nnet)
data(mtcars)
library(caret)## Loading required package: lattice
## Loading required package: ggplot2trainindex<- createDataPartition(y=mtcars$cyl,p=0.7,list=FALSE)
training<- mtcars[trainindex,]
testing<- mtcars[-trainindex,]
cylModel<- multinom(cyl~.,data=training,maxit=500)## # weights: 36 (22 variable)
## initial value 25.268083
## iter 10 value 2.466670
## iter 20 value 0.036268
## iter 30 value 0.000230
## final value 0.000050
## convergedtopModels<- varImp(cylModel)
topModels$Variables<- row.names(topModels)
topModels<- topModels[order(-topModels$Overall),]
##Importance
#Make the order decreasing and check the most 5 important variables
head(topModels,5)## Overall Variables
## vs 51.06146293 vs
## drat 49.51192817 drat
## gear 34.53692949 gear
## carb 29.44117979 carb
## am 23.46641667 am##Prediction
#Both methods are the same, athe difference is the way how they present
#1.Probs
pred1<- predict(cylModel,type="probs",newdata=testing)
head(pred1,5)## 4
## Mazda RX4 0.99999503622827012883078623417532071471214294433593750000000000000
## Mazda RX4 Wag 0.99994340432097927884314003676990978419780731201171875000000000000
## Hornet 4 Drive 0.00000000000120664757750777263827110897783340224143772134723917588
## Duster 360 0.00000000000000000000000000000000000000000000000000000006669009651
## Merc 450SLC 0.00000000000000000000000000000000000224537238180362712685656155907
## 6
## Mazda RX4 0.0000044022970556656173209456560380
## Mazda RX4 Wag 0.0000294426508037891794544466811390
## Hornet 4 Drive 0.9999999999987934096168373798718676
## Duster 360 0.0000000000000000000000001966360747
## Merc 450SLC 0.0000000000000000000094164298609383
## 8
## Mazda RX4 0.00000056147467421563783176
## Mazda RX4 Wag 0.00002715302821698180665974
## Hornet 4 Drive 0.00000000000000001650297471
## Duster 360 1.00000000000000000000000000
## Merc 450SLC 1.00000000000000000000000000#2.class (Recommend)
pred2<- predict(cylModel,type="class",newdata=testing)
head(pred2,5)## [1] 4 4 6 8 8
## Levels: 4 6 8##Evaluation (caret)
postResample(testing$cyl,pred2) #88.89%## Accuracy Kappa
## 0.7777777778 0.6666666667