library(car)## Loading required package: carDatalibrary(caret)## Loading required package: lattice## Loading required package: ggplot2library(cluster)
library(dummies)## dummies-1.5.6 provided by Decision Patternslibrary(data.table)
library(dplyr)##
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## intersect, setdiff, setequal, unionlibrary(e1071)
library(epitools)
library(effects)## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car## Use the command
## lattice::trellis.par.set(effectsTheme())
## to customize lattice options for effects plots.
## See ?effectsTheme for details.library(ggplot2)
library(ggthemes)
library(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
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## marginlibrary(ranger)##
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## importancelibrary(rgl)
library(rattle)## Rattle: A free graphical interface for data science with R.
## バージョン 5.3.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
## 'rattle()' と入力して、データを多角的に分析します。##
## Attaching package: 'rattle'## The following object is masked from 'package:ranger':
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library(readr)
library(reshape)##
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library(reshape2)##
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## expand, smithslibrary(xtable)
library(nnet)
library(stargazer)##
## Please cite as:## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.2. https://CRAN.R-project.org/package=stargazerlibrary(tidyverse)## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.3.0 --## √ tibble 3.0.0 √ stringr 1.4.0
## √ purrr 0.3.3 √ forcats 0.5.0## -- Conflicts ----------------------------------------------------------------------------------- tidyverse_conflicts() --
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## x purrr::transpose() masks data.table::transpose()require(ranger)
library(makedummies)-下水道データ読み込み# 基本統計量表示 gesui # 教科書ではlogit
#gesui = read_csv("osui2.csv")
gesui = read_csv("enbi.csv")## Parsed with column specification:
## cols(
## OBJECTID = col_double(),
## sys_name = col_double(),
## slope = col_double(),
## uedokaburi = col_double(),
## masuhonsuu = col_double(),
## long = col_double(),
## kubun = col_double(),
## did = col_double(),
## kouhou = col_double(),
## nendo = col_double(),
## ekijyouka = col_double(),
## kyouyounensuu = col_double(),
## kansyu = col_double(),
## kei = col_double(),
## kinkyuudo = col_double(),
## taisyo = col_double()
## )gesui <- data.frame(gesui) # 教科書ではlogit
#testデータの行番号取得
#randomgesui<-sample(282,200)
#train <- gesui[randomgesui,]
#test <-gesui[-randomgesui,]
#cat(test$sys_name, file = "testrow.txt",append=FALSE)
#write.table(test,"testoutput.txt", quote=F,
# col.names=T, append=T)
gesui <- gesui[-1:-2] #OBJECTID,sys_name列をデータから削除
gesui <- gesui[-13]
gesui <- gesui[-8]
gesui <- gesui[-10]
gesui2 <- gesui
randomgesui<-sample(282,200)
train <- gesui[randomgesui,]
test <-gesui[-randomgesui,]stargazer(as.data.frame(gesui),type = "html")| Statistic | N | Mean | St. Dev. | Min | Pctl(25) | Pctl(75) | Max |
| slope | 282 | 3.309 | 2.017 | 0.000 | 1.900 | 4.100 | 9.900 |
| uedokaburi | 282 | 4.218 | 2.570 | 1.009 | 2.462 | 5.397 | 13.385 |
| masuhonsuu | 282 | 1.284 | 1.765 | 0 | 0 | 2 | 11 |
| long | 282 | 31.300 | 15.309 | 0.970 | 21.325 | 40.492 | 96.820 |
| kubun | 282 | 1.209 | 0.407 | 1 | 1 | 1 | 2 |
| did | 282 | 0.766 | 0.424 | 0 | 1 | 1 | 1 |
| kouhou | 282 | 0.337 | 0.473 | 0 | 0 | 1 | 1 |
| ekijyouka | 282 | 0.202 | 0.402 | 0 | 0 | 0 | 1 |
| kyouyounensuu | 282 | 27.514 | 5.204 | 10 | 25 | 27 | 40 |
| kei | 282 | 390.248 | 162.287 | 200 | 250 | 600 | 900 |
| taisyo | 282 | 0.312 | 0.464 | 0 | 0 | 1 | 1 |
gesui$taisyo <- as.factor(gesui$taisyo)
#gesui$kansyu <- as.factor(gesui$kansyu)
gesui$kubun <- as.factor(gesui$kubun)
gesui$did <- as.factor(gesui$did)
gesui$ekijyouka <- as.factor(gesui$ekijyouka)
#gesui$kinkyuudo <- as.factor(gesui$kinkyuudo)
cordata <- gesui
# ダミー化したい変数をセレクト
dum <- cordata %>% select( kubun, did, ekijyouka, kouhou)
# ダミー化しない変数をセレクト
not_dum <- cordata %>% select(slope, uedokaburi, masuhonsuu, long, kyouyounensuu, kei, taisyo)
# makedummies()を使用してダミー変数を作成
dummy_var <- makedummies(dum, basal_level = FALSE)
# 結合する
gesui <- cbind(dummy_var, not_dum)
sapply(gesui, class)## kubun did ekijyouka kouhou slope
## "integer" "integer" "integer" "numeric" "numeric"
## uedokaburi masuhonsuu long kyouyounensuu kei
## "numeric" "numeric" "numeric" "numeric" "numeric"
## taisyo
## "factor"summary(gesui)## kubun did ekijyouka kouhou
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.000 Median :0.0000 Median :0.0000
## Mean :0.2092 Mean :0.766 Mean :0.2021 Mean :0.3369
## 3rd Qu.:0.0000 3rd Qu.:1.000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## slope uedokaburi masuhonsuu long
## Min. :0.000 Min. : 1.009 Min. : 0.000 Min. : 0.97
## 1st Qu.:1.900 1st Qu.: 2.462 1st Qu.: 0.000 1st Qu.:21.32
## Median :2.685 Median : 3.402 Median : 1.000 Median :30.06
## Mean :3.309 Mean : 4.218 Mean : 1.284 Mean :31.30
## 3rd Qu.:4.100 3rd Qu.: 5.397 3rd Qu.: 2.000 3rd Qu.:40.49
## Max. :9.900 Max. :13.385 Max. :11.000 Max. :96.82
## kyouyounensuu kei taisyo
## Min. :10.00 Min. :200.0 0:194
## 1st Qu.:25.00 1st Qu.:250.0 1: 88
## Median :25.00 Median :250.0
## Mean :27.51 Mean :390.2
## 3rd Qu.:27.00 3rd Qu.:600.0
## Max. :40.00 Max. :900.0cordata <- gesui
# ダミー化したい変数をセレクト
dum <- cordata %>% select( kubun, did, ekijyouka, kouhou)
# ダミー化しない変数をセレクト
not_dum <- cordata %>% select(slope, uedokaburi, masuhonsuu, long, kyouyounensuu, kei, taisyo)
# makedummies()を使用してダミー変数を作成
dummy_var <- makedummies(dum, basal_level = FALSE)
# 結合する
gesui <- cbind(dummy_var, not_dum)
head(gesui)## kubun did ekijyouka kouhou slope uedokaburi masuhonsuu long kyouyounensuu
## 1 1 1 1 0 1.22 1.054575 1 3.39 12
## 2 1 1 0 0 2.50 1.533001 0 7.78 28
## 3 1 1 1 0 4.71 1.414000 0 5.02 24
## 4 1 1 1 0 1.10 1.544714 3 13.17 24
## 5 1 1 1 1 1.80 4.412133 1 5.56 24
## 6 1 1 1 0 8.90 1.738222 0 15.72 24
## kei taisyo
## 1 200 0
## 2 250 1
## 3 250 0
## 4 250 0
## 5 250 1
## 6 250 0https://shohei-doi.github.io/notes/posts/2019-05-27-cross-validation/
vote_logit3 <- train(
taisyo ~ .,
data = gesui,
method = "rf",
trControl = trainControl(method = "cv")
)
vote_logit3## Random Forest
##
## 282 samples
## 10 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 254, 254, 254, 254, 254, 254, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.8123153 0.5052759
## 6 0.8157635 0.5394256
## 10 0.8051724 0.5132297
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 6.vote_logit1 <- train(
taisyo ~ slope + uedokaburi + masuhonsuu + long + kyouyounensuu + kei,
data = gesui,
method = "rf",
trControl = trainControl(method = "cv")
)
vote_logit1## Random Forest
##
## 282 samples
## 6 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 254, 254, 253, 254, 254, 254, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.7912197 0.4656561
## 4 0.7840677 0.4456567
## 6 0.7840586 0.4467893
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 2.# 乱数の設定
# ランダムフォレストによる分類に寄与した変数の分析
library(kernlab)##
## Attaching package: 'kernlab'## The following object is masked from 'package:purrr':
##
## cross## The following object is masked from 'package:ggplot2':
##
## alphaset.seed(1)
rf.model <- randomForest(taisyo ~ .,
data = test, ntree = 100, proximity = TRUE)## Warning in randomForest.default(m, y, ...): The response has five or fewer
## unique values. Are you sure you want to do regression?# 個体間の類似度を多次元尺度法で視覚化
head(test)## slope uedokaburi masuhonsuu long kubun did kouhou ekijyouka kyouyounensuu
## 4 1.10 1.544714 3 13.17 2 1 0 1 24
## 6 8.90 1.738222 0 15.72 2 1 0 1 24
## 7 9.10 2.981000 0 14.26 1 1 0 0 34
## 11 1.30 3.583047 0 10.86 2 1 0 0 40
## 12 0.00 2.633001 0 5.00 2 1 0 0 32
## 15 1.74 1.436539 3 9.25 2 1 0 1 39
## kei taisyo
## 4 250 0
## 6 250 0
## 7 250 1
## 11 250 0
## 12 250 0
## 15 250 0MDSplot(rf.model, taisyo$test)
https://shohei-doi.github.io/notes/posts/2019-05-27-cross-validation/
vote_logit3 <- train(
taisyo ~ .,
data = gesui,
method = "nnet",
trControl = trainControl(method = "cv")
)## # weights: 13
## initial value 220.305672
## final value 157.458978
## converged
## # weights: 37
## initial value 233.468827
## final value 157.458978
## converged
## # weights: 61
## initial value 280.330064
## final value 157.458978
## converged
## # weights: 13
## initial value 205.407386
## iter 10 value 157.516642
## iter 20 value 156.478475
## iter 30 value 145.592762
## iter 40 value 134.932329
## iter 50 value 132.081047
## final value 131.990037
## converged
## # weights: 37
## initial value 313.855636
## iter 10 value 157.938910
## iter 20 value 152.195567
## iter 30 value 147.330341
## iter 40 value 146.156325
## iter 50 value 134.401657
## iter 60 value 120.319626
## iter 70 value 118.606977
## iter 80 value 115.277081
## iter 90 value 111.543000
## iter 100 value 110.142800
## final value 110.142800
## stopped after 100 iterations
## # weights: 61
## initial value 158.557116
## iter 10 value 156.574491
## iter 20 value 153.901075
## iter 30 value 134.838431
## iter 40 value 131.053861
## iter 50 value 128.000563
## iter 60 value 118.729363
## iter 70 value 112.206128
## iter 80 value 108.934500
## iter 90 value 108.208971
## iter 100 value 106.957181
## final value 106.957181
## stopped after 100 iterations
## # weights: 13
## initial value 197.861177
## final value 157.459198
## converged
## # weights: 37
## initial value 164.628644
## final value 157.459681
## converged
## # weights: 61
## initial value 166.600477
## final value 157.460095
## converged
## # weights: 13
## initial value 220.821655
## final value 158.245151
## converged
## # weights: 37
## initial value 208.914530
## final value 158.245151
## converged
## # weights: 61
## initial value 182.965039
## final value 158.245151
## converged
## # weights: 13
## initial value 167.914554
## iter 10 value 158.208612
## iter 20 value 145.679240
## iter 30 value 128.424190
## iter 40 value 125.008921
## iter 50 value 122.669216
## final value 122.666157
## converged
## # weights: 37
## initial value 230.952902
## iter 10 value 158.280807
## iter 20 value 158.244203
## iter 30 value 154.592689
## iter 40 value 148.889196
## iter 50 value 139.473333
## iter 60 value 134.421348
## iter 70 value 124.084708
## iter 80 value 118.594503
## iter 90 value 115.730343
## iter 100 value 115.184109
## final value 115.184109
## stopped after 100 iterations
## # weights: 61
## initial value 187.453299
## iter 10 value 156.384685
## iter 20 value 140.369877
## iter 30 value 128.954685
## iter 40 value 122.657432
## iter 50 value 115.672256
## iter 60 value 110.308108
## iter 70 value 109.568747
## iter 80 value 108.922464
## iter 90 value 107.305186
## iter 100 value 106.079319
## final value 106.079319
## stopped after 100 iterations
## # weights: 13
## initial value 173.062162
## final value 158.245414
## converged
## # weights: 37
## initial value 179.079115
## final value 158.246398
## converged
## # weights: 61
## initial value 166.711735
## final value 158.246151
## converged
## # weights: 13
## initial value 184.595891
## final value 157.458978
## converged
## # weights: 37
## initial value 229.032551
## final value 157.458978
## converged
## # weights: 61
## initial value 165.246326
## final value 157.458978
## converged
## # weights: 13
## initial value 243.075811
## iter 10 value 152.350171
## iter 20 value 133.333804
## iter 30 value 125.977655
## iter 40 value 124.760495
## iter 50 value 124.250893
## final value 124.218001
## converged
## # weights: 37
## initial value 175.775841
## iter 10 value 135.100999
## iter 20 value 130.911889
## iter 30 value 129.571618
## iter 40 value 124.463165
## iter 50 value 121.434443
## iter 60 value 121.196781
## iter 70 value 121.177650
## final value 121.177604
## converged
## # weights: 61
## initial value 165.189519
## iter 10 value 157.443100
## iter 20 value 156.099166
## iter 30 value 140.062272
## iter 40 value 135.646839
## iter 50 value 131.504986
## iter 60 value 126.855571
## iter 70 value 122.536824
## iter 80 value 115.705112
## iter 90 value 115.074511
## iter 100 value 114.973888
## final value 114.973888
## stopped after 100 iterations
## # weights: 13
## initial value 200.036420
## final value 157.459235
## converged
## # weights: 37
## initial value 211.874516
## final value 157.459708
## converged
## # weights: 61
## initial value 168.935511
## final value 157.459876
## converged
## # weights: 13
## initial value 195.278564
## final value 157.085538
## converged
## # weights: 37
## initial value 303.792169
## final value 157.085538
## converged
## # weights: 61
## initial value 157.153667
## final value 157.085538
## converged
## # weights: 13
## initial value 245.511554
## iter 10 value 156.531063
## iter 20 value 131.075950
## iter 30 value 120.638446
## iter 40 value 120.609069
## final value 120.603103
## converged
## # weights: 37
## initial value 161.774247
## iter 10 value 156.967731
## iter 20 value 142.954930
## iter 30 value 134.189239
## iter 40 value 127.172076
## iter 50 value 121.675431
## iter 60 value 115.501887
## iter 70 value 112.876876
## iter 80 value 111.047935
## iter 90 value 108.580508
## iter 100 value 107.974755
## final value 107.974755
## stopped after 100 iterations
## # weights: 61
## initial value 188.509343
## iter 10 value 153.721478
## iter 20 value 150.725366
## iter 30 value 141.813210
## iter 40 value 136.273273
## iter 50 value 133.801656
## iter 60 value 127.091322
## iter 70 value 119.574343
## iter 80 value 112.495671
## iter 90 value 110.226923
## iter 100 value 110.065102
## final value 110.065102
## stopped after 100 iterations
## # weights: 13
## initial value 163.595281
## final value 157.085717
## converged
## # weights: 37
## initial value 173.723090
## final value 157.086137
## converged
## # weights: 61
## initial value 268.188237
## final value 157.086584
## converged
## # weights: 13
## initial value 242.516740
## final value 157.085538
## converged
## # weights: 37
## initial value 159.744332
## final value 157.085538
## converged
## # weights: 61
## initial value 161.001250
## final value 157.085538
## converged
## # weights: 13
## initial value 163.931577
## iter 10 value 157.117134
## iter 20 value 153.280966
## iter 30 value 149.428068
## iter 40 value 149.247614
## iter 50 value 147.594086
## iter 60 value 140.977640
## iter 70 value 131.833872
## iter 80 value 123.182638
## iter 90 value 122.143359
## final value 122.143334
## converged
## # weights: 37
## initial value 198.595850
## iter 10 value 157.117562
## iter 20 value 137.692356
## iter 30 value 122.551078
## iter 40 value 119.230535
## iter 50 value 113.027376
## iter 60 value 111.247326
## iter 70 value 109.952960
## iter 80 value 109.938103
## iter 90 value 109.936736
## iter 100 value 109.734062
## final value 109.734062
## stopped after 100 iterations
## # weights: 61
## initial value 158.677711
## iter 10 value 152.583894
## iter 20 value 130.021785
## iter 30 value 120.140078
## iter 40 value 114.281081
## iter 50 value 112.594837
## iter 60 value 104.099795
## iter 70 value 98.633909
## iter 80 value 95.710260
## iter 90 value 94.529339
## iter 100 value 94.386682
## final value 94.386682
## stopped after 100 iterations
## # weights: 13
## initial value 168.697731
## final value 157.085823
## converged
## # weights: 37
## initial value 274.999159
## final value 157.086254
## converged
## # weights: 61
## initial value 165.165846
## final value 157.086536
## converged
## # weights: 13
## initial value 246.525457
## final value 157.458978
## converged
## # weights: 37
## initial value 158.901373
## final value 157.458978
## converged
## # weights: 61
## initial value 157.770178
## final value 157.458978
## converged
## # weights: 13
## initial value 172.951644
## iter 10 value 156.880339
## iter 20 value 151.379536
## iter 30 value 149.662348
## iter 40 value 144.191647
## iter 50 value 138.143989
## iter 60 value 128.770358
## iter 70 value 119.389572
## iter 80 value 118.781009
## iter 80 value 118.781008
## iter 80 value 118.781008
## final value 118.781008
## converged
## # weights: 37
## initial value 229.770967
## iter 10 value 156.350985
## iter 20 value 151.875414
## iter 30 value 143.669537
## iter 40 value 128.788828
## iter 50 value 124.783895
## iter 60 value 119.976044
## iter 70 value 116.493378
## iter 80 value 115.177512
## iter 90 value 113.749749
## iter 100 value 110.445240
## final value 110.445240
## stopped after 100 iterations
## # weights: 61
## initial value 160.344224
## iter 10 value 157.509817
## iter 20 value 137.546612
## iter 30 value 122.965566
## iter 40 value 118.372360
## iter 50 value 118.166284
## iter 60 value 116.092670
## iter 70 value 114.018448
## iter 80 value 113.226058
## iter 90 value 113.225102
## iter 100 value 113.224155
## final value 113.224155
## stopped after 100 iterations
## # weights: 13
## initial value 285.182023
## final value 157.459142
## converged
## # weights: 37
## initial value 178.708002
## final value 157.459565
## converged
## # weights: 61
## initial value 159.541864
## final value 157.460112
## converged
## # weights: 13
## initial value 177.529437
## final value 157.458978
## converged
## # weights: 37
## initial value 191.260091
## final value 157.458978
## converged
## # weights: 61
## initial value 174.733119
## iter 10 value 152.691185
## iter 20 value 152.177760
## final value 151.839846
## converged
## # weights: 13
## initial value 189.622473
## iter 10 value 157.110551
## iter 20 value 126.139320
## iter 30 value 123.082871
## iter 40 value 122.772513
## final value 122.688402
## converged
## # weights: 37
## initial value 221.414117
## iter 10 value 154.854231
## iter 20 value 149.136005
## iter 30 value 147.063575
## iter 40 value 137.780878
## iter 50 value 124.906031
## iter 60 value 123.693786
## iter 70 value 121.898507
## iter 80 value 114.822006
## iter 90 value 114.087050
## iter 100 value 114.080287
## final value 114.080287
## stopped after 100 iterations
## # weights: 61
## initial value 226.032229
## iter 10 value 158.109367
## iter 20 value 151.335246
## iter 30 value 150.271988
## iter 40 value 133.234907
## iter 50 value 123.946746
## iter 60 value 119.843628
## iter 70 value 113.035121
## iter 80 value 104.268237
## iter 90 value 101.219911
## iter 100 value 98.340891
## final value 98.340891
## stopped after 100 iterations
## # weights: 13
## initial value 184.755813
## final value 157.459194
## converged
## # weights: 37
## initial value 204.519355
## final value 157.460271
## converged
## # weights: 61
## initial value 239.024217
## final value 157.460924
## converged
## # weights: 13
## initial value 169.826189
## final value 157.458978
## converged
## # weights: 37
## initial value 157.479903
## final value 157.458978
## converged
## # weights: 61
## initial value 164.043220
## final value 157.458978
## converged
## # weights: 13
## initial value 173.549445
## iter 10 value 156.085704
## iter 20 value 135.372310
## iter 30 value 125.642341
## iter 40 value 124.801494
## final value 124.633251
## converged
## # weights: 37
## initial value 158.671564
## iter 10 value 156.698360
## iter 20 value 142.701645
## iter 30 value 135.405119
## iter 40 value 135.311305
## iter 50 value 129.911933
## iter 60 value 125.433014
## iter 70 value 124.001828
## iter 80 value 123.933621
## final value 123.933504
## converged
## # weights: 61
## initial value 195.318593
## iter 10 value 156.203321
## iter 20 value 144.990541
## iter 30 value 133.908887
## iter 40 value 128.853959
## iter 50 value 127.984961
## iter 60 value 127.593645
## iter 70 value 127.163081
## iter 80 value 126.633832
## iter 90 value 123.962449
## iter 100 value 122.212162
## final value 122.212162
## stopped after 100 iterations
## # weights: 13
## initial value 179.909448
## final value 157.459275
## converged
## # weights: 37
## initial value 158.516798
## final value 157.459672
## converged
## # weights: 61
## initial value 220.974682
## final value 157.459899
## converged
## # weights: 13
## initial value 192.003548
## final value 158.622528
## converged
## # weights: 37
## initial value 184.188255
## final value 158.622528
## converged
## # weights: 61
## initial value 172.873142
## final value 158.622528
## converged
## # weights: 13
## initial value 162.999541
## iter 10 value 158.292142
## iter 20 value 138.358008
## iter 30 value 124.874233
## iter 40 value 124.635571
## final value 124.604374
## converged
## # weights: 37
## initial value 178.604990
## iter 10 value 156.477009
## iter 20 value 149.621212
## iter 30 value 146.520336
## iter 40 value 141.592668
## iter 50 value 135.409743
## iter 60 value 123.455156
## iter 70 value 120.688451
## iter 80 value 117.835351
## iter 90 value 115.233089
## iter 100 value 114.988901
## final value 114.988901
## stopped after 100 iterations
## # weights: 61
## initial value 368.120009
## iter 10 value 160.354027
## iter 20 value 158.840727
## iter 30 value 134.154770
## iter 40 value 122.958425
## iter 50 value 118.913058
## iter 60 value 117.342464
## iter 70 value 113.732335
## iter 80 value 111.337124
## iter 90 value 111.194529
## iter 100 value 111.142239
## final value 111.142239
## stopped after 100 iterations
## # weights: 13
## initial value 177.116837
## final value 158.622764
## converged
## # weights: 37
## initial value 166.637109
## final value 158.623093
## converged
## # weights: 61
## initial value 214.519818
## final value 158.623665
## converged
## # weights: 13
## initial value 159.286219
## final value 157.085538
## converged
## # weights: 37
## initial value 186.791405
## final value 157.085538
## converged
## # weights: 61
## initial value 196.367667
## final value 157.085538
## converged
## # weights: 13
## initial value 157.789974
## iter 10 value 157.146780
## iter 20 value 156.549833
## iter 30 value 149.347379
## iter 40 value 146.173878
## iter 50 value 143.800073
## final value 143.710412
## converged
## # weights: 37
## initial value 157.747543
## iter 10 value 155.500412
## iter 20 value 151.392181
## iter 30 value 151.113455
## iter 40 value 149.603577
## iter 50 value 121.470524
## iter 60 value 119.552575
## iter 70 value 117.639031
## iter 80 value 106.328411
## iter 90 value 103.790436
## iter 100 value 103.660055
## final value 103.660055
## stopped after 100 iterations
## # weights: 61
## initial value 201.340432
## iter 10 value 153.068361
## iter 20 value 139.272068
## iter 30 value 124.551822
## iter 40 value 119.113957
## iter 50 value 118.041490
## iter 60 value 113.635577
## iter 70 value 112.986528
## iter 80 value 110.171751
## iter 90 value 105.680967
## iter 100 value 103.995043
## final value 103.995043
## stopped after 100 iterations
## # weights: 13
## initial value 197.044232
## final value 157.085808
## converged
## # weights: 37
## initial value 290.050048
## final value 157.086120
## converged
## # weights: 61
## initial value 158.954647
## final value 157.086359
## converged
## # weights: 37
## initial value 178.030222
## iter 10 value 162.737352
## iter 20 value 144.621125
## iter 30 value 135.992616
## iter 40 value 130.141928
## iter 50 value 129.082258
## iter 60 value 123.363737
## iter 70 value 117.272266
## iter 80 value 117.059003
## iter 90 value 117.055575
## iter 90 value 117.055575
## iter 90 value 117.055575
## final value 117.055575
## convergedvote_logit3## Neural Network
##
## 282 samples
## 10 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 254, 254, 254, 253, 253, 254, ...
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 1 0e+00 0.6879812 0.0000000
## 1 1e-04 0.6879812 0.0000000
## 1 1e-01 0.7524083 0.3814798
## 3 0e+00 0.6879812 0.0000000
## 3 1e-04 0.6879812 0.0000000
## 3 1e-01 0.7730980 0.4231230
## 5 0e+00 0.6879812 0.0000000
## 5 1e-04 0.6879812 0.0000000
## 5 1e-01 0.7660874 0.4215595
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 0.1.http://ushi-goroshi.hatenablog.com/entry/2019/02/06/153859
vote_logit3 <- train(
taisyo ~ .,
data = gesui,
method = "nnet",
trControl = trainControl(method = "cv")
)## # weights: 13
## initial value 205.690088
## final value 157.458978
## converged
## # weights: 37
## initial value 198.698424
## final value 157.458978
## converged
## # weights: 61
## initial value 160.064483
## final value 157.458978
## converged
## # weights: 13
## initial value 157.681889
## iter 10 value 143.512750
## iter 20 value 128.843530
## iter 30 value 124.970775
## final value 124.970568
## converged
## # weights: 37
## initial value 251.595406
## iter 10 value 157.492178
## iter 20 value 143.353770
## iter 30 value 129.401317
## iter 40 value 123.955362
## iter 50 value 123.562872
## iter 60 value 123.401658
## iter 70 value 122.637367
## iter 80 value 116.376610
## iter 90 value 107.614951
## iter 100 value 106.029074
## final value 106.029074
## stopped after 100 iterations
## # weights: 61
## initial value 189.608520
## iter 10 value 157.462729
## iter 20 value 154.548126
## iter 30 value 153.379751
## iter 40 value 135.623384
## iter 50 value 134.381387
## iter 60 value 131.533431
## iter 70 value 124.965788
## iter 80 value 111.276828
## iter 90 value 104.780049
## iter 100 value 101.661978
## final value 101.661978
## stopped after 100 iterations
## # weights: 13
## initial value 158.563194
## final value 157.459189
## converged
## # weights: 37
## initial value 157.787508
## final value 157.459629
## converged
## # weights: 61
## initial value 181.085169
## final value 157.459848
## converged
## # weights: 13
## initial value 169.029712
## final value 157.458978
## converged
## # weights: 37
## initial value 264.419218
## final value 157.458978
## converged
## # weights: 61
## initial value 186.724248
## final value 157.458978
## converged
## # weights: 13
## initial value 197.614701
## iter 10 value 156.102287
## iter 20 value 132.201319
## iter 30 value 119.688085
## iter 40 value 119.671369
## final value 119.670463
## converged
## # weights: 37
## initial value 172.350760
## iter 10 value 157.427776
## iter 20 value 153.523438
## iter 30 value 152.024540
## iter 40 value 135.369655
## iter 50 value 125.813156
## iter 60 value 122.554541
## iter 70 value 119.131069
## iter 80 value 114.796116
## iter 90 value 114.317114
## iter 100 value 113.983854
## final value 113.983854
## stopped after 100 iterations
## # weights: 61
## initial value 259.332695
## iter 10 value 157.409301
## iter 20 value 156.912670
## iter 30 value 140.930734
## iter 40 value 135.085302
## iter 50 value 131.876073
## iter 60 value 120.201975
## iter 70 value 117.943574
## iter 80 value 117.846489
## iter 90 value 117.582177
## iter 100 value 114.528026
## final value 114.528026
## stopped after 100 iterations
## # weights: 13
## initial value 163.795195
## final value 157.459245
## converged
## # weights: 37
## initial value 185.424913
## final value 157.459640
## converged
## # weights: 61
## initial value 164.107672
## final value 157.459963
## converged
## # weights: 13
## initial value 179.289641
## iter 10 value 134.514034
## iter 20 value 131.316280
## iter 30 value 130.443928
## final value 130.442001
## converged
## # weights: 37
## initial value 233.453105
## final value 157.458978
## converged
## # weights: 61
## initial value 238.558846
## final value 157.458978
## converged
## # weights: 13
## initial value 158.096385
## iter 10 value 156.445755
## iter 20 value 133.462726
## iter 30 value 131.100503
## iter 40 value 126.464473
## iter 50 value 124.546442
## final value 124.546106
## converged
## # weights: 37
## initial value 219.568292
## iter 10 value 138.239938
## iter 20 value 129.604966
## iter 30 value 124.499736
## iter 40 value 124.286928
## iter 50 value 119.438280
## iter 60 value 114.340941
## iter 70 value 114.000717
## iter 80 value 113.963847
## iter 90 value 113.828279
## iter 100 value 110.079551
## final value 110.079551
## stopped after 100 iterations
## # weights: 61
## initial value 223.662662
## iter 10 value 155.427028
## iter 20 value 133.943805
## iter 30 value 127.009119
## iter 40 value 126.122764
## final value 126.121355
## converged
## # weights: 13
## initial value 157.760541
## final value 157.459237
## converged
## # weights: 37
## initial value 158.751100
## iter 10 value 131.340713
## iter 20 value 120.887418
## iter 30 value 120.402490
## iter 40 value 120.328157
## iter 50 value 120.295641
## iter 60 value 120.290431
## iter 70 value 120.282069
## iter 80 value 120.273337
## iter 90 value 120.271791
## iter 100 value 120.269591
## final value 120.269591
## stopped after 100 iterations
## # weights: 61
## initial value 165.272424
## final value 157.460128
## converged
## # weights: 13
## initial value 185.138412
## final value 157.085538
## converged
## # weights: 37
## initial value 188.084548
## final value 157.085538
## converged
## # weights: 61
## initial value 190.793943
## final value 157.085538
## converged
## # weights: 13
## initial value 157.981154
## iter 10 value 149.408145
## iter 20 value 124.034179
## iter 30 value 119.166691
## iter 40 value 118.949633
## final value 118.942356
## converged
## # weights: 37
## initial value 284.764702
## iter 10 value 155.652824
## iter 20 value 144.863134
## iter 30 value 122.143110
## iter 40 value 116.896693
## iter 50 value 113.567859
## iter 60 value 111.062373
## iter 70 value 109.868420
## iter 80 value 109.854390
## iter 90 value 107.753980
## iter 100 value 97.790745
## final value 97.790745
## stopped after 100 iterations
## # weights: 61
## initial value 164.790790
## iter 10 value 153.586278
## iter 20 value 127.588358
## iter 30 value 120.074384
## iter 40 value 117.933279
## iter 50 value 116.076582
## iter 60 value 115.303330
## iter 70 value 113.125178
## iter 80 value 108.820690
## iter 90 value 105.002929
## iter 100 value 100.382019
## final value 100.382019
## stopped after 100 iterations
## # weights: 13
## initial value 158.861685
## iter 10 value 128.817067
## iter 20 value 121.196026
## iter 30 value 118.786749
## iter 40 value 118.703485
## iter 50 value 118.563329
## final value 118.562512
## converged
## # weights: 37
## initial value 235.449133
## final value 157.086213
## converged
## # weights: 61
## initial value 304.506616
## final value 157.086520
## converged
## # weights: 13
## initial value 199.423542
## final value 157.085538
## converged
## # weights: 37
## initial value 160.134828
## final value 157.085537
## converged
## # weights: 61
## initial value 196.091171
## final value 157.085538
## converged
## # weights: 13
## initial value 206.628631
## iter 10 value 156.907871
## iter 20 value 150.663720
## iter 30 value 146.222592
## iter 40 value 146.024506
## final value 146.024494
## converged
## # weights: 37
## initial value 180.631859
## iter 10 value 156.850332
## iter 20 value 140.666883
## iter 30 value 128.240012
## iter 40 value 123.275300
## iter 50 value 123.254669
## iter 60 value 122.068439
## iter 70 value 114.392605
## iter 80 value 113.866784
## iter 90 value 112.803980
## iter 100 value 108.888206
## final value 108.888206
## stopped after 100 iterations
## # weights: 61
## initial value 171.247070
## iter 10 value 157.899261
## iter 20 value 157.243094
## iter 30 value 138.423606
## iter 40 value 131.914417
## iter 50 value 124.551554
## iter 60 value 123.696106
## iter 70 value 120.988659
## iter 80 value 115.615506
## iter 90 value 110.968683
## iter 100 value 109.124351
## final value 109.124351
## stopped after 100 iterations
## # weights: 13
## initial value 165.679142
## final value 157.085856
## converged
## # weights: 37
## initial value 157.554215
## iter 10 value 137.996953
## iter 20 value 134.141840
## iter 30 value 133.607595
## iter 40 value 133.600932
## iter 50 value 133.599044
## iter 60 value 133.590642
## iter 70 value 133.590466
## iter 70 value 133.590465
## iter 70 value 133.590465
## final value 133.590465
## converged
## # weights: 61
## initial value 268.283400
## final value 157.091389
## converged
## # weights: 13
## initial value 208.711557
## final value 157.458978
## converged
## # weights: 37
## initial value 199.607073
## final value 157.458978
## converged
## # weights: 61
## initial value 283.320863
## final value 157.458978
## converged
## # weights: 13
## initial value 157.789881
## iter 10 value 153.785006
## iter 20 value 151.353897
## iter 30 value 135.957460
## iter 40 value 124.675004
## iter 50 value 118.563685
## final value 118.360488
## converged
## # weights: 37
## initial value 190.784383
## iter 10 value 145.786830
## iter 20 value 135.561164
## iter 30 value 115.649132
## iter 40 value 113.644086
## iter 50 value 112.857424
## iter 60 value 112.213246
## final value 112.205163
## converged
## # weights: 61
## initial value 178.466555
## iter 10 value 152.769041
## iter 20 value 145.710603
## iter 30 value 138.204130
## iter 40 value 132.338982
## iter 50 value 126.567033
## iter 60 value 118.605499
## iter 70 value 116.690516
## iter 80 value 112.792361
## iter 90 value 109.409144
## iter 100 value 105.356239
## final value 105.356239
## stopped after 100 iterations
## # weights: 13
## initial value 159.317121
## final value 157.459275
## converged
## # weights: 37
## initial value 160.424207
## final value 157.460313
## converged
## # weights: 61
## initial value 214.350869
## final value 157.460152
## converged
## # weights: 13
## initial value 182.520188
## final value 157.458978
## converged
## # weights: 37
## initial value 157.564882
## final value 157.458978
## converged
## # weights: 61
## initial value 324.996909
## final value 157.458978
## converged
## # weights: 13
## initial value 168.032832
## iter 10 value 157.220318
## iter 20 value 152.191374
## iter 30 value 137.690493
## iter 40 value 137.062636
## iter 50 value 133.292525
## iter 60 value 124.261820
## iter 70 value 123.878464
## final value 123.878440
## converged
## # weights: 37
## initial value 158.201401
## iter 10 value 155.467996
## iter 20 value 135.202150
## iter 30 value 125.282259
## iter 40 value 124.615168
## iter 50 value 124.384525
## iter 60 value 123.411035
## iter 70 value 113.397387
## iter 80 value 108.901722
## iter 90 value 108.646587
## iter 100 value 108.628776
## final value 108.628776
## stopped after 100 iterations
## # weights: 61
## initial value 162.241636
## iter 10 value 157.281268
## iter 20 value 153.576902
## iter 30 value 150.953144
## iter 40 value 137.773798
## iter 50 value 126.689525
## iter 60 value 113.587840
## iter 70 value 109.823435
## iter 80 value 106.213947
## iter 90 value 103.834536
## iter 100 value 99.295122
## final value 99.295122
## stopped after 100 iterations
## # weights: 13
## initial value 174.092556
## final value 157.459214
## converged
## # weights: 37
## initial value 179.047178
## final value 157.459660
## converged
## # weights: 61
## initial value 177.139241
## final value 157.460358
## converged
## # weights: 13
## initial value 157.257589
## final value 157.085538
## converged
## # weights: 37
## initial value 203.172258
## final value 157.085538
## converged
## # weights: 61
## initial value 157.255544
## final value 157.085538
## converged
## # weights: 13
## initial value 167.037240
## iter 10 value 156.751765
## iter 20 value 137.387338
## iter 30 value 136.992545
## iter 40 value 130.088050
## iter 50 value 126.013935
## iter 60 value 125.591135
## iter 70 value 125.384721
## final value 125.384714
## converged
## # weights: 37
## initial value 190.409650
## iter 10 value 154.252434
## iter 20 value 143.170769
## iter 30 value 127.210651
## iter 40 value 124.114751
## iter 50 value 122.551380
## iter 60 value 121.687828
## iter 70 value 121.435652
## iter 80 value 119.803084
## iter 90 value 116.680235
## iter 100 value 115.796028
## final value 115.796028
## stopped after 100 iterations
## # weights: 61
## initial value 168.790758
## iter 10 value 156.088936
## iter 20 value 136.182449
## iter 30 value 125.929448
## iter 40 value 122.269114
## iter 50 value 120.649924
## iter 60 value 118.187240
## iter 70 value 117.059275
## iter 80 value 114.066184
## iter 90 value 111.080808
## iter 100 value 105.667183
## final value 105.667183
## stopped after 100 iterations
## # weights: 13
## initial value 170.642404
## final value 157.085774
## converged
## # weights: 37
## initial value 171.632667
## final value 157.086202
## converged
## # weights: 61
## initial value 166.542678
## iter 10 value 156.145333
## iter 20 value 146.152843
## iter 30 value 142.804498
## iter 40 value 142.113873
## iter 50 value 140.527337
## iter 60 value 139.078442
## iter 70 value 138.841188
## iter 80 value 136.337412
## iter 90 value 136.191806
## iter 100 value 136.179798
## final value 136.179798
## stopped after 100 iterations
## # weights: 13
## initial value 170.273868
## final value 158.245151
## converged
## # weights: 37
## initial value 158.533213
## final value 158.245151
## converged
## # weights: 61
## initial value 159.303888
## final value 158.245151
## converged
## # weights: 13
## initial value 165.213410
## iter 10 value 158.372415
## iter 20 value 158.274203
## iter 30 value 152.364033
## iter 40 value 151.504172
## iter 50 value 151.503072
## final value 151.503053
## converged
## # weights: 37
## initial value 159.899899
## iter 10 value 156.514986
## iter 20 value 141.838719
## iter 30 value 135.316815
## iter 40 value 134.638261
## iter 50 value 118.946405
## iter 60 value 111.914656
## iter 70 value 110.524258
## iter 80 value 108.910189
## iter 90 value 107.952472
## iter 100 value 107.378348
## final value 107.378348
## stopped after 100 iterations
## # weights: 61
## initial value 171.864990
## iter 10 value 158.026417
## iter 20 value 151.750283
## iter 30 value 137.706342
## iter 40 value 126.590943
## iter 50 value 124.739511
## iter 60 value 119.285588
## iter 70 value 116.380454
## iter 80 value 115.345695
## iter 90 value 114.379917
## iter 100 value 114.331930
## final value 114.331930
## stopped after 100 iterations
## # weights: 13
## initial value 181.571001
## final value 158.245330
## converged
## # weights: 37
## initial value 162.318135
## final value 158.245749
## converged
## # weights: 61
## initial value 173.036737
## final value 158.246112
## converged
## # weights: 13
## initial value 179.851107
## final value 158.622528
## converged
## # weights: 37
## initial value 177.034640
## final value 158.622528
## converged
## # weights: 61
## initial value 159.813096
## final value 158.622528
## converged
## # weights: 13
## initial value 173.819494
## iter 10 value 158.683733
## iter 20 value 158.487329
## iter 30 value 134.340019
## iter 40 value 127.473506
## iter 50 value 125.611516
## iter 60 value 124.045669
## final value 124.045023
## converged
## # weights: 37
## initial value 161.331073
## iter 10 value 157.855662
## iter 20 value 150.577561
## iter 30 value 145.089333
## iter 40 value 125.135746
## iter 50 value 122.338279
## iter 60 value 113.968361
## iter 70 value 110.780828
## iter 80 value 110.674559
## iter 90 value 108.913553
## iter 100 value 107.259382
## final value 107.259382
## stopped after 100 iterations
## # weights: 61
## initial value 187.128010
## iter 10 value 151.537453
## iter 20 value 145.940894
## iter 30 value 138.870289
## iter 40 value 129.065328
## iter 50 value 125.156040
## iter 60 value 124.760687
## iter 70 value 124.538540
## iter 80 value 124.521998
## iter 90 value 124.518434
## iter 100 value 124.412858
## final value 124.412858
## stopped after 100 iterations
## # weights: 13
## initial value 196.596755
## final value 158.622783
## converged
## # weights: 37
## initial value 158.927153
## iter 10 value 155.532962
## iter 20 value 151.028220
## iter 30 value 148.653189
## iter 40 value 145.873692
## iter 50 value 145.662110
## iter 60 value 145.440518
## iter 70 value 145.417060
## iter 80 value 145.393890
## iter 90 value 145.368867
## iter 100 value 145.122644
## final value 145.122644
## stopped after 100 iterations
## # weights: 61
## initial value 281.749801
## final value 158.623502
## converged
## # weights: 37
## initial value 176.756632
## iter 10 value 173.435396
## iter 20 value 148.158520
## iter 30 value 142.199348
## iter 40 value 129.617829
## iter 50 value 126.914871
## iter 60 value 126.375900
## iter 70 value 126.237550
## final value 126.236723
## convergedvote_logit3## Neural Network
##
## 282 samples
## 10 predictor
## 2 classes: '0', '1'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 254, 254, 254, 253, 253, 254, ...
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 1 0e+00 0.7022669 0.052054795
## 1 1e-04 0.6879812 0.020183486
## 1 1e-01 0.7697911 0.367751857
## 3 0e+00 0.6879812 0.000000000
## 3 1e-04 0.7161832 0.111434617
## 3 1e-01 0.7976054 0.471048172
## 5 0e+00 0.6879812 0.000000000
## 5 1e-04 0.6845329 -0.006617647
## 5 1e-01 0.7941571 0.467965203
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 0.1.nn<-nnet(taisyo ~ slope + uedokaburi + masuhonsuu + long + kyouyounensuu + kei,
data=train,size = 5, rang = .1, decay = 0.1, maxit = 200 )## # weights: 41
## initial value 52.005164
## iter 10 value 43.474070
## iter 20 value 38.064828
## iter 30 value 35.829526
## iter 40 value 33.369503
## iter 50 value 32.603099
## iter 60 value 32.304821
## iter 70 value 32.016855
## iter 80 value 31.642203
## iter 90 value 31.532014
## iter 100 value 31.506384
## final value 31.505625
## convergedpredition = predict(nn, test)
table(predition,test$taisyo)##
## predition 0 1
## 0.0295242463268687 1 0
## 0.0295848987964667 1 0
## 0.0298475547487361 1 0
## 0.0303574479733984 1 0
## 0.0314777984410032 1 0
## 0.0317463280092576 1 0
## 0.0326670304051563 0 1
## 0.0585518218908303 1 0
## 0.0877923166251347 1 0
## 0.092421602998543 1 0
## 0.0985715850512022 1 0
## 0.0993357800492 1 0
## 0.107931143019777 1 0
## 0.115890202563216 1 0
## 0.117318515716786 1 0
## 0.121641234485692 1 0
## 0.131090271302498 1 0
## 0.139292898085026 0 1
## 0.155224881339847 0 1
## 0.160665732869271 1 0
## 0.196190293178982 1 0
## 0.209117984735156 0 1
## 0.218654096224618 1 0
## 0.229112800928227 1 0
## 0.230688895085002 1 0
## 0.236979498841049 1 0
## 0.243621785460793 1 0
## 0.247646479469752 1 0
## 0.25399406320416 1 0
## 0.254268756730388 1 0
## 0.255216412954828 1 0
## 0.255628494820377 1 0
## 0.259076998324261 1 0
## 0.262867312407084 1 0
## 0.263222327317786 1 0
## 0.267168709231038 1 0
## 0.268332666042179 0 1
## 0.268504605951573 1 0
## 0.269904933076506 1 0
## 0.274441633274133 1 0
## 0.27510310250442 1 0
## 0.275147002461864 1 0
## 0.275442134068737 1 0
## 0.276303703556832 1 0
## 0.277323779661832 1 0
## 0.277589143380674 0 1
## 0.277727926512261 1 0
## 0.277750580618592 1 0
## 0.277851374509356 0 1
## 0.27786388631142 0 1
## 0.277905486684228 0 1
## 0.278054301802326 1 0
## 0.278101011993139 1 0
## 0.278342203339156 1 0
## 0.278383452876594 1 0
## 0.28860824532644 1 0
## 0.294674964170076 1 0
## 0.307986189797099 1 0
## 0.324384794057157 1 0
## 0.325164738531486 1 0
## 0.35786181806826 1 0
## 0.405104652122653 1 0
## 0.424962729683021 0 1
## 0.427394312851678 0 1
## 0.430108805096737 1 0
## 0.484801248072998 1 0
## 0.494395290304906 0 1
## 0.536409839196686 1 0
## 0.642993521547959 0 1
## 0.653464593461555 1 0
## 0.675490255559909 0 1
## 0.688656635679323 0 1
## 0.696262608629013 1 0
## 0.743357775551049 0 1
## 0.760817610572907 0 1
## 0.772815014719463 0 1
## 0.77726621862403 0 1
## 0.781733328877021 0 1
## 0.786189457683613 0 1
## 0.791901140094849 0 1
## 0.794114848787367 0 1
## 0.796691630158656 0 1