Machine Learning Lynam methods

ライブラリのロード

earth がインストールできなかったので、省略。

データを読み込む

まず、データを読みこみます。 なお、この Dyslexia 研究は、課題数が32あり、それぞれ５列、合計160列が wide フォーマットで並んでいます。 そこで、今回は、“Gender”, “Nativelang”, “Age”, “Accuracy1”,“Accuracy2”,“Accuracy3”, “Accuracy4”, “Dyslexia” だけの小さい列に変換します。

─ Column specification ─────────────────────────────────
cols(
  .default = col_double(),
  Gender = col_character(),
  Nativelang = col_character(),
  Otherlang = col_character(),
  Dyslexia = col_character()
)
ℹ Use `spec()` for the full column specifications.

3644行のデータがあるため、Lynam 論文に準じて、最初の960行をトレーニング (data_train)、最後の504行を評価用データ (dataset_val) とします。 なお、これらのデータフレーム名は命名規則から外れています。 これは、Lynam のスクリプトを使うためです。

data_train <- head(dfDyslexia, n = 960)
dataset_val <- tail(dfDyslexia, n = 504)

ここから先は、 Lynam 2021 の公開スクリプトです。

named list()
named list()

モデルを作成します。 モデルの形式は、ロジスティック回帰と同じものを他でも使用します。

formula.model <- formula(Dyslexia ~ Gender + Age + Nativelang + Accuracy1 + Accuracy2 + Accuracy3 + Accuracy4) ### 変更

ここからの処理はよくわかっていません。 (論文に解説あり)

Youroutcomevariable <- as.character(formula.model)[2]

# standardized data sets
standardized <-recipe(formula.model , data = data_train) %>% 
  step_center(all_numeric()) %>%
  step_scale(all_numeric())

trained_rec <- prep(standardized, training = data_train)
data_train <- bake(trained_rec, new_data = data_train)
dataset_val  <- bake(trained_rec, new_data = dataset_val)

sampling <- NULL

control <- trainControl(method = "repeatedcv", number = 10, repeats = 5,classProbs = TRUE,summaryFunction = twoClassSummary, sampling = sampling, savePredictions = TRUE)
control_Rand_Search <- trainControl(method = "repeatedcv", number = 10, repeats = 5,classProbs = TRUE,summaryFunction = twoClassSummary, sampling = sampling, savePredictions = TRUE, search = "random")

ここから、機械学習のモデルが始まります。

Gradient Boostring Machine

[1] 2

サポートベクトルマシン

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iterations reached -0.001722855 -0.001729188maximum number of iterations reached 0.0001406191 0.0001405464maximum number of iterations reached 0.0009031354 0.0009026762maximum number of iterations reached -6.025606e-05 -6.027055e-05maximum number of iterations reached -0.001539986 -0.00154919maximum number of iterations reached 0.0002080495 0.0002080398maximum number of iterations reached 0.0008112951 0.0008065253maximum number of iterations reached 0.000145958 0.0001459584maximum number of iterations reached 0.0001843556 0.0001843165maximum number of iterations reached 0.0001920325 0.0001920306maximum number of iterations reached -0.0005869265 -0.0005877769maximum number of iterations reached 0.0001763175 0.0001763085maximum number of iterations reached 0.0002304341 0.0002303919maximum number of iterations reached 0.0001767154 0.0001767164maximum number of iterations reached -0.0008470839 -0.0008494292maximum number of iterations reached -0.0003246184 -0.00032536maximum number of iterations reached 0.001126447 0.001126067maximum number of iterations reached -0.0002640303 -0.0002640344maximum number of iterations reached -0.001380371 -0.001393135maximum number of iterations reached -1.121943e-05 -1.12199e-05maximum number of iterations reached -0.0001797446 -0.0001797451maximum number of iterations reached -0.001118264 -0.001131426maximum number of iterations reached -0.001119427 -0.001120675maximum number of iterations reached -0.0003912239 -0.000391253maximum number of iterations reached 0.0002938128 0.0002921162maximum number of iterations reached 0.000453857 0.000453847maximum number of iterations reached 8.647624e-05 8.64751e-05maximum number of iterations reached -0.0001868557 -0.0001875329maximum number of iterations reached -8.030402e-05 -8.032011e-05maximum number of iterations reached -0.0004177092 -0.000417833maximum number of iterations reached 0.0002905898 0.0002879995maximum number of iterations reached 0.001946201 0.001933794maximum number of iterations reached -0.001279094 -0.001280326maximum number of iterations reached 3.696014e-05 3.688182e-05maximum number of iterations reached 0.000425687 0.0004254952maximum number of iterations reached 0.0006132699 0.0006126978maximum number of iterations reached 0.0001013238 0.0001013235maximum number of iterations reached -0.0005844103 -0.0005846779maximum number of iterations reached 0.0001497495 0.0001488526maximum number of iterations reached 0.000505361 0.0005052702maximum number of iterations reached -0.0004236487 -0.0004240799maximum number of iterations reached 0.0006973682 0.0006969284maximum number of iterations reached 0.0008887416 0.0008883368maximum number of iterations reached -0.0002034148 -0.0002034344maximum number of iterations reached -0.0005512281 -0.0005518355maximum number of iterations reached 0.001877953 0.001869412maximum number of iterations reached 0.0002415949 0.0002413904maximum number of iterations reached 0.0006966529 0.0006959719maximum number of iterations reached -6.702714e-05 -6.703376e-05maximum number of iterations reached 5.6721e-05 5.654772e-05maximum number of iterations reached -0.0004618317 -0.0004622717maximum number of iterations reached -0.0003627259 -0.0003628913maximum number of iterations reached -7.342523e-05 -7.342543e-05maximum number of iterations reached -0.0009746854 -0.0009762277maximum number of iterations reached 0.00133039 0.001329136maximum number of iterations reached -6.074122e-05 -6.07491e-05maximum number of iterations reached 0.0004317529 0.0004315809maximum number of iterations reached 0.0009296411 0.0009270207maximum number of iterations reached 0.0004008115 0.0004005895maximum number of iterations reached 0.0004071398 0.0004067453maximum number of iterations reached -3.413227e-05 -3.413563e-05maximum number of iterations reached 0.0001130774 0.0001130289maximum number of iterations reached 0.0001054065 0.0001054066maximum number of iterations reached 0.0002115474 0.0002115409maximum number of iterations reached -0.0009067089 -0.0009089652maximum number of iterations reached 0.00160077 0.001599464maximum number of iterations reached -0.0006378514 -0.0006380055maximum number of iterations reached -0.001328991 -0.001337552maximum number of iterations reached 0.0006334311 0.000633409maximum number of iterations reached -0.0003470436 -0.0003470661maximum number of iterations reached -0.0005210556 -0.0005265626maximum number of iterations reached 0.001066495 0.00106495maximum number of iterations reached 8.666884e-05 8.666757e-05maximum number of iterations reached 0.0009152824 0.0009054721maximum number of iterations reached 0.001812339 0.001807205maximum number of iterations reached -0.0004361956 -0.0004362731maximum number of iterations reached 8.497195e-05 8.463688e-05maximum number of iterations reached -0.000894191 -0.0008960451maximum number of iterations reached 0.0005572331 0.0005570366maximum number of iterations reached 0.0005471541 0.0005394596maximum number of iterations reached 0.00116935 0.001164671maximum number of iterations reached 0.0007749388 0.0007746835maximum number of iterations reached 1.93756e-05 1.934861e-05maximum number of iterations reached -0.0005163952 -0.00051713maximum number of iterations reached -0.0002259717 -0.0002260046maximum number of iterations reached -0.000198188 -0.0001983702maximum number of iterations reached 0.001068323 0.001065249maximum number of iterations reached 0.0004466418 0.0004466199maximum number of iterations reached 0.0005232555 0.0005226556maximum number of iterations reached -0.0001125981 -0.0001126539maximum number of iterations reached -0.00107828 -0.001080092maximum number of iterations reached 8.122612e-05 8.087083e-05maximum number of iterations reached 0.0002777852 0.000277736maximum number of iterations reached 0.001358788 0.001355352maximum number of iterations reached 0.002167167 0.002159537maximum number of iterations reached -1.158664e-05 -1.158689e-05maximum number of iterations reached 2.233056e-05 2.230307e-05maximum number of iterations reached -1.185845e-05 -1.185844e-05maximum number of iterations reached 0.0007311455 0.0007299909maximum number of iterations reached 0.001314611 0.001313031maximum number of iterations reached -0.0004777766 -0.0004779816maximum number of iterations reached 9.374057e-06 9.368989e-06maximum number of iterations reached 0.000100838 0.0001008376maximum number of iterations reached 0.001412886 0.001407614maximum number of iterations reached -0.0008038537 -0.0008042095maximum number of iterations reached 0.0001422475 0.000142211maximum number of iterations reached 0.001719963 0.001716332maximum number of iterations reached 0.0007062564 0.0007048905maximum number of iterations reached 0.002076726 0.002065254maximum number of iterations reached 4.146446e-05 4.146382e-05maximum number of iterations reached 0.0001503254 0.0001502247maximum number of iterations reached 7.207654e-05 7.207725e-05maximum number of iterations reached -0.0009672059 -0.0009675573maximum number of iterations reached 0.0008850439 0.0008823033maximum number of iterations reached -0.0003588701 -0.0003589017maximum number of iterations reached 0.0005460966 0.0005458692maximum number of iterations reached -0.0008653259 -0.0008699488maximum number of iterations reached -0.002070983 -0.002080459maximum number of iterations reached 0.0004153143 0.0004150422maximum number of iterations reached 0.0004086595 0.0004063049maximum number of iterations reached 0.002528523 0.002522931maximum number of iterations reached 0.0004434402 0.0004434033maximum number of iterations reached 0.0006734681 0.0006729037maximum number of iterations reached 7.912071e-05 7.91152e-05maximum number of iterations reached 5.953679e-05 5.94917e-05maximum number of iterations reached 0.000655045 0.0006549641maximum number of iterations reached -0.0004137656 -0.0004140973maximum number of iterations reached 1.219684e-05 1.218692e-05maximum number of iterations reached 0.003796742 0.003757337maximum number of iterations reached -0.0004055093 -0.0004057627maximum number of iterations reached 0.000120937 0.000120317maximum number of iterations reached 0.002658574 0.002645531maximum number of iterations reached 0.0003507003 0.0003506157maximum number of iterations reached 0.001077564 0.001076074maximum number of iterations reached 0.001265432 0.001264423maximum number of iterations reached 0.001528333 0.001523882maximum number of iterations reached -0.0001763132 -0.0001763526maximum number of iterations reached 1.784946e-05 1.782522e-05maximum number of iterations reached 0.000524419 0.0005243172maximum number of iterations reached -0.0009696064 -0.0009706426maximum number of iterations reached -5.51334e-05 -5.51464e-05maximum number of iterations reached -0.001795477 -0.001808605maximum number of iterations reached 0.0005310954 0.0005306746maximum number of iterations reached -0.002124915 -0.002132864maximum number of iterations reached 2.065368e-05 2.062566e-05maximum number of iterations reached 0.0001119698 0.0001119496maximum number of iterations reached 0.0002865156 0.0002864517maximum number of iterations reached 0.0002246452 0.0002246285maximum number of iterations reached 0.0003909708 0.0003909168maximum number of iterations reached 0.0006059142 0.0006055891maximum number of iterations reached -0.002109082 -0.002113775maximum number of iterations reached -0.000911914 -0.0009126637maximum number of iterations reached -0.0007147831 -0.0007167626maximum number of iterations reached 0.0005859915 0.0005857537maximum number of iterations reached 0.0007132545 0.0007122008maximum number of iterations reached 9.910293e-05 9.91001e-05maximum number of iterations reached -0.0001443869 -0.000144454maximum number of iterations reached 5.539387e-05 5.539357e-05maximum number of iterations reached 0.0001208786 0.0001207808maximum number of iterations reached -5.581871e-05 -5.581908e-05maximum number of iterations reached 0.0008825779 0.0008821737maximum number of iterations reached -0.0002973214 -0.0002984283maximum number of iterations reached 3.654493e-05 3.654485e-05maximum number of iterations reached 0.000323786 0.0003237796maximum number of iterations reached -0.00085755 -0.0008681339maximum number of iterations reached 0.002275265 0.0022725maximum number of iterations reached -0.000489681 -0.0004898004maximum number of iterations reached 0.001078989 0.001062055maximum number of iterations reached 0.002050958 0.002046744maximum number of iterations reached -1.355441e-05 -1.355509e-05maximum number of iterations reached 0.0003055362 0.0003038336maximum number of iterations reached 0.0009734942 0.0009720849maximum number of iterations reached -0.0002210651 -0.0002210868maximum number of iterations reached 1.426922e-05 1.426426e-05maximum number of iterations reached -0.001025285 -0.001026858maximum number of iterations reached 0.000367293 0.000367226maximum number of iterations reached 0.0007061232 0.0006958586maximum number of iterations reached -0.00101652 -0.00102021maximum number of iterations reached 0.000513707 0.0005135537maximum number of iterations reached 2.193914e-05 2.190525e-05maximum number of iterations reached 0.000506111 0.0005055355maximum number of iterations reached 0.0006261395 0.0006259888maximum number of iterations reached 0.0007583053 0.000757265maximum number of iterations reached 0.001069765 0.001068144maximum number of iterations reached 1.22575e-05 1.224544e-05maximum number of iterations reached -0.0001404629 -0.0001404729maximum number of iterations reached -0.001660143 -0.001664073maximum number of iterations reached -0.000560297 -0.0005604609maximum number of iterations reached -0.0005977732 -0.0005984934maximum number of iterations reached 0.0001506421 0.0001506253maximum number of iterations reached 0.0002544303 0.0002543885maximum number of iterations reached -1.219509e-05 -1.219534e-05maximum number of iterations reached -0.0004759074 -0.0004770027maximum number of iterations reached 0.000940791 0.000940436maximum number of iterations reached 0.0006023204 0.0006008943maximum number of iterations reached 1.338217e-05 1.338217e-05maximum number of iterations reached 4.987238e-05 4.98757e-05maximum number of iterations reached 3.94772e-05 3.940467e-05maximum number of iterations reached 7.640413e-05 7.640398e-05maximum number of iterations reached -0.0001938009 -0.0001941122maximum number of iterations reached -0.0009052624 -0.0009061622maximum number of iterations reached 0.0004962633 0.0004958063maximum number of iterations reached 0.000800136 0.000799707maximum number of iterations reached 0.002433526 0.002418778maximum number of iterations reached -9.372793e-05 -9.373439e-05maximum number of iterations reached 3.543961e-05 3.543959e-05maximum number of iterations reached 0.0002613947 0.0002612773maximum number of iterations reached 0.0004159521 0.0004159136maximum number of iterations reached 0.001004172 0.001003349maximum number of iterations reached -0.0006378518 -0.0006382051maximum number of iterations reached 0.001570094 0.001569211maximum number of iterations reached -0.001753771 -0.001774956maximum number of iterations reached 0.00230826 0.002305298maximum number of iterations reached -0.0002821108 -0.000282154maximum number of iterations reached -0.0008920405 -0.0009007396maximum number of iterations reached -0.0001110857 -0.0001110884maximum number of iterations reached -8.930151e-05 -8.934154e-05maximum number of iterations reached 0.0003562633 0.0003556192maximum number of iterations reached 0.003400786 0.003382608maximum number of iterations reached 0.0001601013 0.0001600954maximum number of iterations reached -0.000858861 -0.0008684938maximum number of iterations reached 0.0004983614 0.0004981796maximum number of iterations reached 3.120828e-05 3.120912e-05maximum number of iterations reached -0.0002834106 -0.0002855975maximum number of iterations reached -0.0003378264 -0.0003379061maximum number of iterations reached 0.0002786864 0.0002786089maximum number of iterations reached 0.0002602207 0.0002592237maximum number of iterations reached 0.003514255 0.003458508maximum number of iterations reached -4.365417e-05 -4.36646e-05maximum number of iterations reached -0.0001792737 -0.0001803522maximum number of iterations reached 1.393722e-05 1.39369e-05maximum number of iterations reached -3.710954e-05 -3.712246e-05maximum number of iterations reached 0.0006076818 0.0006074352maximum number of iterations reached -0.0004087178 -0.000408899maximum number of iterations reached 0.0001479847 0.0001476604maximum number of iterations reached -0.0003712157 -0.0003713797maximum number of iterations reached 8.703157e-05 8.701999e-05maximum number of iterations reached 0.0007370671 0.0007365608maximum number of iterations reached -0.000515879 -0.0005161404maximum number of iterations reached 5.597309e-05 5.580801e-05maximum number of iterations reached 0.001142865 0.001141261maximum number of iterations reached 0.0004344089 0.0004343331maximum number of iterations reached -0.0006018111 -0.0006019459maximum number of iterations reached 0.0007713792 0.0007707742maximum number of iterations reached -0.0007779799 -0.0007786623maximum number of iterations reached -0.0005824336 -0.0005837119maximum number of iterations reached -2.96402e-05 -2.968899e-05maximum number of iterations reached 0.001292508 0.001288261maximum number of iterations reached 0.0005425635 0.0005419881maximum number of iterations reached 6.665088e-05 6.665204e-05maximum number of iterations reached 0.0008334817 0.0008332565maximum number of iterations reached 0.000245807 0.0002444129maximum number of iterations reached 0.001583978 0.001580917maximum number of iterations reached -0.001105747 -0.001108554maximum number of iterations reached 0.001179093 0.001177354maximum number of iterations reached 0.0007313345 0.0007310335maximum number of iterations reached 1.823496e-05 1.821803e-05maximum number of iterations reached -3.656706e-05 -3.656668e-05maximum number of iterations reached -0.0001621948 -0.0001622622maximum number of iterations reached 1.60971e-05 1.609717e-05maximum number of iterations reached 0.0004760047 0.0004758562maximum number of iterations reached 0.0003653296 0.0003650199maximum number of iterations reached 0.001722898 0.00172197maximum number of iterations reached 0.0006460426 0.0006447286maximum number of iterations reached 0.001102407 0.001096002maximum number of iterations reached 0.0002920445 0.0002920406maximum number of iterations reached -0.0001768006 -0.0001768033maximum number of iterations reached 0.0005857012 0.0005856663maximum number of iterations reached -0.0003040309 -0.000304053maximum number of iterations reached 0.0009465818 0.0009398857maximum number of iterations reached 0.001408512 0.001405959maximum number of iterations reached -0.0002212577 -0.0002213033maximum number of iterations reached 0.0001395978 0.0001389314maximum number of iterations reached -0.0006054524 -0.0006065854maximum number of iterations reached 1.21918e-05 1.219272e-05maximum number of iterations reached 0.0001500759 0.0001493093maximum number of iterations reached 0.0006919316 0.0006912728maximum number of iterations reached -0.0006067476 -0.0006072466maximum number of iterations reached 0.0004842141 0.0004785865maximum number of iterations reached 0.001090943 0.001087087maximum number of iterations reached -0.0003174421 -0.0003175582maximum number of iterations reached 5.116599e-05 5.101782e-05maximum number of iterations reached -0.0001038772 -0.0001038848maximum number of iterations reached -0.0005211654 -0.0005215351maximum number of iterations reached 0.0001710348 0.0001698435maximum number of iterations reached 0.0015126 0.001510116maximum number of iterations reached 0.000990934 0.0009898444maximum number of iterations reached 9.754146e-05 9.730152e-05maximum number of iterations reached 0.003114935 0.003087246maximum number of iterations reached -0.0002934772 -0.0002936382maximum number of iterations reached 0.003917272 0.003887683maximum number of iterations reached -0.000193039 -0.0001930893maximum number of iterations reached 0.0006534205 0.0006529039maximum number of iterations reached 0.00016914 0.0001690337maximum number of iterations reached 1.248335e-05 1.2472e-05maximum number of iterations reached -0.0002502848 -0.0002503439maximum number of iterations reached 0.0004399885 0.0004397114maximum number of iterations reached 1.966706e-05 1.964076e-05maximum number of iterations reached 0.002259999 0.002250029maximum number of iterations reached 0.0008711687 0.0008700309maximum number of iterations reached 0.001210383 0.001206384maximum number of iterations reached 0.00114322 0.001138414maximum number of iterations reached -0.0001401661 -0.0001402477maximum number of iterations reached 0.0001103081 0.0001102935maximum number of iterations reached -0.0003917084 -0.0003921029maximum number of iterations reached -0.0004804619 -0.0004805718maximum number of iterations reached -0.0012048 -0.001209442maximum number of iterations reached 0.001292183 0.001290978maximum number of iterations reached -0.0008113735 -0.0008134529maximum number of iterations reached 0.0003246363 0.0003246281maximum number of iterations reached 0.001746887 0.001739maximum number of iterations reached 0.0005614573 0.0005608696maximum number of iterations reached -0.000232245 -0.0002322923maximum number of iterations reached 0.0001287303 0.0001287228maximum number of iterations reached 0.0007862294 0.0007815667maximum number of iterations reached 0.002337412 0.002333983maximum number of iterations reached -0.0002398224 -0.0002398405maximum number of iterations reached -0.0001180212 -0.0001183015maximum number of iterations reached 0.001781471 0.001779209maximum number of iterations reached 0.0004222253 0.0004222012maximum number of iterations reached 0.000545634 0.0005388763maximum number of iterations reached 0.001241325 0.001238863maximum number of iterations reached 0.001404149 0.001403393maximum number of iterations reached 4.392161e-05 4.38557e-05maximum number of iterations reached 0.0005145678 0.0005145553maximum number of iterations reached -0.0002279073 -0.00022794maximum number of iterations reached 0.0002549139 0.0002528393maximum number of iterations reached -0.0005416508 -0.00054273maximum number of iterations reached -1.778739e-05 -1.778742e-05maximum number of iterations reached 5.932668e-05 5.916395e-05maximum number of iterations reached 0.0004862834 0.0004858472maximum number of iterations reached 0.0006828026 0.0006819481maximum number of iterations reached 3.6244e-05 3.615237e-05maximum number of iterations reached 3.257929e-05 3.257976e-05maximum number of iterations reached -0.001877181 -0.001882111maximum number of iterations reached 0.00111222 0.001107876maximum number of iterations reached 0.00024892 0.0002487339maximum number of iterations reached 0.0007448575 0.0007441939maximum number of iterations reached -0.000247811 -0.0002478757maximum number of iterations reached 2.316863e-05 2.312617e-05maximum number of iterations reached -0.0004215093 -0.0004215571maximum number of iterations reached -0.0009154122 -0.0009170261maximum number of iterations reached 0.001385513 0.001354983maximum number of iterations reached 0.001339016 0.001336667maximum number of iterations reached -0.001015827 -0.00101808maximum number of iterations reached 0.00239215 0.002379771maximum number of iterations reached -0.000480055 -0.0004808379maximum number of iterations reached -0.001002354 -0.001002828maximum number of iterations reached -0.0007943865 -0.0007986615maximum number of iterations reached 0.001239243 0.001237776maximum number of iterations reached 0.001836798 0.001825102maximum number of iterations reached 0.001025829 0.001025202maximum number of iterations reached 0.0007707228 0.0007654634maximum number of iterations reached 0.0001244661 0.0001244652maximum number of iterations reached 0.002271553 0.002249931maximum number of iterations reached 7.674062e-05 7.674035e-05maximum number of iterations reached 0.0009281203 0.000926672maximum number of iterations reached -2.515106e-05 -2.515109e-05maximum number of iterations reached 0.001146029 0.001136226

ランダムフォレスト

ロジスティック回帰

ニューラルネットワーク

# weights:  19
initial  value 933.997337 
iter  10 value 300.178552
iter  20 value 277.683055
iter  30 value 275.413400
final  value 275.390132 
converged
...
# weights:  73
initial  value 425.217856 
iter  10 value 217.175696
iter  20 value 206.790641
iter  30 value 201.110413
iter  40 value 196.924896
iter  50 value 194.350197
iter  60 value 192.038326
iter  70 value 191.243415
iter  80 value 190.156316
iter  90 value 189.236498
iter 100 value 188.731044
final  value 188.731044 
stopped after 100 iterations

glm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredglm.fit: fitted probabilities numerically 0 or 1 occurredSetting levels: control = X1, case = X2
Setting direction: controls < cases
Setting levels: control = X1, case = X2
Setting direction: controls > cases
Setting levels: control = X1, case = X2
Setting direction: controls < cases
Setting levels: control = X1, case = X2
Infinite values(s) in predictor, cannot build a valid ROC curve. NaN returned instead. roc.default(response, predictor, auc = TRUE, ...) でエラー: 
  No valid data provided.

まだコード解読中。

#for the Gbm grid search model (validation)
probsTestGbmGridVal = predict(modelGbm_CC_GADA_Lr, newdata=dataset_val, type = "prob")
dataset_val =data.frame(dataset_val,probsTestGbmGridVal$X2)
predTestGbmGridVal = log(as.numeric(probsTestGbmGridVal$X2)/(1-as.numeric(probsTestGbmGridVal$X2)))
#then create a roc object and calculate the ROC on the validation dataset
roc_objTestGbmGridval <- roc(dataset_val[,Youroutcomevariable], predTestGbmGridVal)

Setting levels: control = X1, case = X2
Setting direction: controls < cases

AUC_objTestGbmGridval = auc(roc_objTestGbmGridval)
AUC_objTestGbmGridval

Area under the curve: 0.7143

ci.auc(roc_objTestGbmGridval)

95% CI: 0.6535-0.7751 (DeLong)

#for the Svm grid search model (validation)
probsTestSvmGridVal = predict(modelSvm_CC_GADA_Lr, newdata=dataset_val, type = "prob")
dataset_val =data.frame(dataset_val,probsTestSvmGridVal$X2)
predTestSvmGridVal = log(as.numeric(probsTestSvmGridVal$X2)/(1-as.numeric(probsTestSvmGridVal$X2)))
#then create a roc object and calculate the ROC on the validation dataset
roc_objTestSvmGridval <- roc(dataset_val[,Youroutcomevariable], predTestSvmGridVal)

Setting levels: control = X1, case = X2
Setting direction: controls < cases

AUC_objTestSvmGridval = auc(roc_objTestSvmGridval)
AUC_objTestSvmGridval

Area under the curve: 0.5737

ci.auc(roc_objTestSvmGridval)

95% CI: 0.5092-0.6382 (DeLong)

#for the knn grid search model (validation)
probsTestknnGridVal = predict(modelknn_CC_GADA_Lr, newdata=dataset_val, type = "prob")
dataset_val =data.frame(dataset_val,probsTestknnGridVal$X2)
probsTestknnGridVal$X2[probsTestknnGridVal$X2 == 1] <- 0.999999
probsTestknnGridVal$X2[probsTestknnGridVal$X2 == 0] <- 0.000001
predTestknnGridVal = log(as.numeric(probsTestknnGridVal$X2)/(1-as.numeric(probsTestknnGridVal$X2)))
#then create a roc object and calculate the ROC on the validation dataset
roc_objTestknnGridval <- roc(dataset_val[,Youroutcomevariable], predTestknnGridVal)

Setting levels: control = X1, case = X2
Setting direction: controls < cases

AUC_objTestknnGridval = auc(roc_objTestknnGridval)
AUC_objTestknnGridval

Area under the curve: 0.6588

ci.auc(roc_objTestknnGridval)

95% CI: 0.5925-0.7251 (DeLong)

#for the nnet grid search model (validation)
probsTestnnetGridVal = predict(modelnnet_CC_GADA_Lr, newdata=dataset_val, type = "prob")
dataset_val =data.frame(dataset_val,probsTestnnetGridVal$X2)
predTestnnetGridVal = log(as.numeric(probsTestnnetGridVal$X2)/(1-as.numeric(probsTestnnetGridVal$X2)))
#then create a roc object and calculate the ROC on the validation dataset
roc_objTestnnetGridval <- roc(dataset_val[,Youroutcomevariable], predTestnnetGridVal)

Setting levels: control = X1, case = X2
Setting direction: controls < cases

AUC_objTestnnetGridval = auc(roc_objTestnnetGridval)
AUC_objTestnnetGridval

Area under the curve: 0.6493

ci.auc(roc_objTestnnetGridval)

95% CI: 0.5809-0.7176 (DeLong)

#for the rf model (validation)
probsTestRfVal = predict(modelRf_CC_GADA, newdata=dataset_val, type = "prob")
dataset_val =data.frame(dataset_val,probsTestRfVal$X2)
probsTestRfVal$X2[probsTestRfVal$X2 == 1] <- 0.999999
probsTestRfVal$X2[probsTestRfVal$X2 == 0] <- 0.000001
predTestRfVal = log(as.numeric(probsTestRfVal$X2)/(1-as.numeric(probsTestRfVal$X2)))
#then create a roc object and calculate the ROC on the validation dataset
roc_objTestRfval <- roc(dataset_val[,Youroutcomevariable], predTestRfVal)

Setting levels: control = X1, case = X2
Setting direction: controls < cases

AUC_objTestRfval = auc(roc_objTestRfval)
AUC_objTestRfval

Area under the curve: 0.65

ci.auc(roc_objTestRfval)

95% CI: 0.5806-0.7194 (DeLong)

#for the logistic regression model (validation)
probsTestlgVal = predict(modelLG_CC_GADA, newdata=dataset_val, type = "prob")
dataset_val =data.frame(dataset_val,probsTestlgVal$X2)
predTestLGVal = log(as.numeric(probsTestlgVal$X2)/(1-as.numeric(probsTestlgVal$X2)))
#then create a roc object and calculate the ROC on the validation dataset
roc_objTestLGval <- roc(dataset_val[,Youroutcomevariable], predTestLGVal)

Setting levels: control = X1, case = X2
Setting direction: controls < cases

AUC_objTestLGval = auc(roc_objTestLGval)
AUC_objTestLGval

Area under the curve: 0.646

ci.auc(roc_objTestLGval)

95% CI: 0.5773-0.7148 (DeLong)

#for the logistic regression model MARS (validation)
probsTestmarsVal = predict(modelMARS_CC_GADA_Lr, newdata=dataset_val, type = "prob")
dataset_val =data.frame(dataset_val,probsTestmarsVal$X2)
# predTestMARSVal = log(as.numeric(probsTestmarsVal$X2)/(1-as.numeric(probsTestmarsVal$X2)))
#then create a roc object and calculate the ROC on the validation dataset
roc_objTesMARSGval <- roc(dataset_val[,Youroutcomevariable], probsTestmarsVal$X2)

Setting levels: control = X1, case = X2
Setting direction: controls < cases

AUC_objTestMARSval = auc(roc_objTesMARSGval)
AUC_objTestMARSval

Area under the curve: 0.6707

ci.auc(roc_objTesMARSGval)

95% CI: 0.6102-0.7313 (DeLong)

#plot the roc curves
plot(roc_objTestRfval, col = "gray85",main = "",add=FALSE)
plot(roc_objTestLGval, col = "gray45", add = TRUE)

plot(roc_objTestSvmGridval, co = "black", add = TRUE)
plot(roc_objTestGbmGridval, col = "gray85",  lty = 3, add = TRUE)

plot(roc_objTestnnetGridval, col = "black",lty = 3, add = TRUE)
plot(roc_objTestknnGridval, col = "gray45", lty = 3, add = TRUE)

plot(roc_objTesMARSGval, col = "gray99", lty = 3, add = TRUE)

model=c('LR','GBM','SVM','RF','Nnet','Knn','LGMARS')
AUC = c(AUC_objTestLGval, AUC_objTestGbmGridval, AUC_objTestSvmGridval, AUC_objTestRfval, AUC_objTestnnetGridval, AUC_objTestknnGridval,AUC_objTestMARSval)
ValResults = data.frame(model, AUC)

myft <- regulartable(ValResults)
myft <- theme_vanilla(myft)
myft <- autofit(myft)
doc <- read_docx()
doc <- body_add_flextable(doc, value = myft)
#print(doc, target = paste0("extAUC_sampling", sampling, "formula_",as.character(formula.model)[3],".docx"))
print(doc) ### 外部ファイルではなく

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        table_template    Light List Accent 2            Titre 1 Car 
               "table"                "table"            "character" 
           Titre 2 Car            Titre 3 Car          Image Caption 
           "character"            "character"            "paragraph" 
         Table Caption     Table Professional                  toc 1 
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          reference_id          graphic title            table title 
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#use prediction-recall curve to validate the models########################################################
#calculate the AUPRC for the validation dataset
prRFval = pr.curve(1-dataset_val$probsTestRfVal.X2,dataset_val$probsTestRfVal.X2, curve = TRUE)
prLGval =  pr.curve(1-dataset_val$probsTestlgVal.X2,dataset_val$probsTestlgVal.X2, curve = TRUE) 
prSVMval = pr.curve(1-dataset_val$probsTestSvmGridVal.X2,dataset_val$probsTestSvmGridVal.X2, curve = TRUE) 
prGBMval = pr.curve(1-dataset_val$probsTestGbmGridVal.X2,dataset_val$probsTestGbmGridVal.X2, curve = TRUE) 
prNNval = pr.curve(1-dataset_val$probsTestnnetGridVal.X2,dataset_val$probsTestnnetGridVal.X2, curve = TRUE)
prKNNval = pr.curve(1-dataset_val$probsTestknnGridVal.X2,dataset_val$probsTestknnGridVal.X2, curve = TRUE)
prMARSval = pr.curve(1-dataset_val$probsTestmarsVal,dataset_val$probsTestmarsVal.X2, curve = TRUE)
#return the AUPRC
prRFval

  Precision-recall curve

    Area under curve (Integral):
     0.9993581 

    Area under curve (Davis & Goadrich):
     0.9993581 

    Curve for scores from  0  to  1 
    ( can be plotted with plot(x) )

prLGval

  Precision-recall curve

    Area under curve (Integral):
     1 

    Area under curve (Davis & Goadrich):
     1 

    Curve for scores from  1.320738e-06  to  0.9999987 
    ( can be plotted with plot(x) )

prSVMval

  Precision-recall curve

    Area under curve (Integral):
     1 

    Area under curve (Davis & Goadrich):
     1 

    Curve for scores from  0.1025672  to  0.8974328 
    ( can be plotted with plot(x) )

prGBMval

  Precision-recall curve

    Area under curve (Integral):
     0.9999021 

    Area under curve (Davis & Goadrich):
     0.9999021 

    Curve for scores from  0.01101329  to  0.9889867 
    ( can be plotted with plot(x) )

prNNval

  Precision-recall curve

    Area under curve (Integral):
     1 

    Area under curve (Davis & Goadrich):
     1 

    Curve for scores from  0.004511938  to  0.9954881 
    ( can be plotted with plot(x) )

prKNNval

  Precision-recall curve

    Area under curve (Integral):
     1 

    Area under curve (Davis & Goadrich):
     1 

    Curve for scores from  0.01769912  to  0.9823009 
    ( can be plotted with plot(x) )

prMARSval

  Precision-recall curve

    Area under curve (Integral):
     0.9983392 

    Area under curve (Davis & Goadrich):
     0.9983392 

    Curve for scores from  0.02699495  to  0.9730051 
    ( can be plotted with plot(x) )

model=c('LG','GBM','SVM','RF','Nnet','Knn','LGMARS')
pr = c(prLGval$auc.integral, prGBMval$auc.integral, prSVMval$auc.integral, prRFval$auc.integral, prNNval$auc.integral, prKNNval$auc.integral,prMARSval$auc.integral)
ValResults = data.frame(model, pr)

myft <- regulartable(ValResults)
myft <- theme_vanilla(myft)
myft <- autofit(myft)
doc <- read_docx()
doc <- body_add_flextable(doc, value = myft)
#print(doc, target = paste0("extpr_sampling", sampling, "formula_",as.character(formula.model)[3],".docx"))
print(doc)

rdocx document with 2 element(s)

* styles:
                Normal              heading 1              heading 2 
           "paragraph"            "paragraph"            "paragraph" 
             heading 3 Default Paragraph Font           Normal Table 
           "paragraph"            "character"                "table" 
               No List                 strong               centered 
           "numbering"            "character"            "paragraph" 
        table_template    Light List Accent 2            Titre 1 Car 
               "table"                "table"            "character" 
           Titre 2 Car            Titre 3 Car          Image Caption 
           "character"            "character"            "paragraph" 
         Table Caption     Table Professional                  toc 1 
           "paragraph"                "table"            "paragraph" 
                 toc 2           Balloon Text    Texte de bulles Car 
           "paragraph"            "paragraph"            "character" 
          reference_id          graphic title            table title 
           "character"            "paragraph"            "paragraph" 

* Content at cursor location:

# plot PR curve for the test curve in red, without legend
### pdf(paste0("prcurve", sampling, "formula_",as.character(formula.model)[3],".pdf"))
plot(prRFval, color = "gray85",auc.main=FALSE, main = "") 
plot( prLGval, color = "gray45", add = TRUE)

plot( prSVMval, color = "black", add = TRUE)
plot( prGBMval, color = "gray85", lty = 3, add = TRUE)

plot( prNNval, color = "black", lty = 3, add = TRUE)
plot( prKNNval, color = "gray45", lty = 3,add = TRUE)

plot(prMARSval, col = "gray99", lty = 3, add = TRUE)

### dev.off()

#######plot the calibration plots with loess smoother #########################################
#for logistic regression
#create 10 risk groups
dataset_val %>% mutate(quintile=ntile(dataset_val$probsTestlgVal.X2,10)) -> dataset_val_10
Youroutcomevariable_num <- paste(Youroutcomevariable,"num")
dataset_val_10[as.numeric(dataset_val_10[,Youroutcomevariable])== 1,Youroutcomevariable_num] <- 0
dataset_val_10[as.numeric(dataset_val_10[,Youroutcomevariable])== 2,Youroutcomevariable_num] <- 1

#average the observed and expected probabilities of patients in each risk group 
obs = aggregate(as.numeric(dataset_val_10[,Youroutcomevariable_num]), list(dataset_val_10$quintile),mean)
exptd = aggregate(dataset_val_10$probsTestlgVal.X2, list(dataset_val_10$quintile),mean)
obsn =aggregate(as.formula(paste0(Youroutcomevariable ,"~ quintile")), dataset_val_10, length)

#CIs for scatter points
lci = obs - (1.96*(((obs*(1-obs))/obsn[,Youroutcomevariable])^.5))
lci[lci<0]<-0

uci = obs + (1.96*(((obs*(1-obs))/obsn[,Youroutcomevariable])^.5))
uci[uci>1]<-1

LR_Cali_Plot = data.frame(exptd$x,obs$x, uci$x, lci$x)

calLG <- ggplot(LR_Cali_Plot, aes(x= exptd$x, y=obs$x)) +
  geom_point(size = 2) + 
  geom_smooth(method=lm, se=FALSE, col = "black", lwd = 1) +
  geom_abline(slope=1, intercept=0, lty=2 ) +
  scale_x_continuous(name = "Expected", breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) +
  scale_y_continuous(name = "Observed", breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) +
  geom_errorbar(aes(ymin=lci$x, ymax=uci$x), width=0.02) +
  theme_bw()
calLG

#for SVM
#create 10 risk groups
dataset_val %>% mutate(quintile=ntile(dataset_val$probsTestSvmGridVal.X2,10)) -> dataset_val_10_SVM
dataset_val_10_SVM[as.numeric(dataset_val_10_SVM[,Youroutcomevariable])== 1,Youroutcomevariable_num] <- 0
dataset_val_10_SVM[as.numeric(dataset_val_10_SVM[,Youroutcomevariable])== 2,Youroutcomevariable_num] <- 1
#average the observed and expected probabilities of patients in each risk group 
obs_SVM = aggregate(as.numeric(dataset_val_10_SVM[,Youroutcomevariable_num]), list(dataset_val_10_SVM$quintile),mean)
exptd_SVM = aggregate(dataset_val_10_SVM$probsTestSvmGridVal.X2, list(dataset_val_10_SVM$quintile),mean)
obsn_SVM =aggregate(as.formula(paste0(Youroutcomevariable ,"~ quintile")), dataset_val_10_SVM, length)


#CIs for scatter points
lci_SVM = obs_SVM- (1.96*(((obs_SVM*(1-obs_SVM))/obsn_SVM[,Youroutcomevariable])^.5))
lci_SVM[lci_SVM<0]<-0

uci_SVM = obs_SVM + (1.96*(((obs_SVM*(1-obs_SVM))/obsn_SVM[,Youroutcomevariable])^.5))
uci_SVM[uci_SVM>1]<-1
SVM_Cali_Plot = data.frame(exptd_SVM$x,obs_SVM$x, uci_SVM$x, lci_SVM$x)

calSVM <- ggplot(SVM_Cali_Plot, aes(x= exptd_SVM$x, y=obs_SVM$x)) +
  geom_point(size = 2) + 
  geom_smooth(method=lm, se=FALSE, col = "black", lwd = 1) +
  geom_abline(slope=1, intercept=0, lty=2 ) +
  scale_x_continuous(name = "Expected", breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) +
  scale_y_continuous(name = "Observed", breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) +
  geom_errorbar(aes(ymin=lci_SVM$x, ymax=uci_SVM$x), width=0.02) +
  theme_bw()
calSVM

#for Random Forest    
#create 10 risk groups
dataset_val %>% mutate(quintile=ntile(dataset_val$probsTestRfVal.X2,10)) -> dataset_val_10_RF
dataset_val_10_RF[as.numeric(dataset_val_10_RF[,Youroutcomevariable])== 1,Youroutcomevariable_num] <- 0
dataset_val_10_RF[as.numeric(dataset_val_10_RF[,Youroutcomevariable])== 2,Youroutcomevariable_num] <- 1
#average the observed and expected probabilities of patients in each risk group 
obs_RF = aggregate(as.numeric(dataset_val_10_RF[,Youroutcomevariable_num]), list(dataset_val_10_RF$quintile),mean)
exptd_RF = aggregate(dataset_val_10_RF$probsTestRfVal.X2, list(dataset_val_10_RF$quintile),mean)
obsn_RF =aggregate(as.formula(paste0(Youroutcomevariable ,"~ quintile")), dataset_val_10_RF, length)


#CIs for scatter points
lci_RF = obs_RF- (1.96*(((obs_RF*(1-obs_RF))/obsn_RF[,Youroutcomevariable])^.5))
lci_RF[lci_RF<0]<-0

uci_RF = obs_RF + (1.96*(((obs_RF*(1-obs_RF))/obsn_RF[,Youroutcomevariable])^.5))
uci_RF[uci_RF>1]<-1
RF_Cali_Plot = data.frame(exptd_RF$x,obs_RF$x, uci_RF$x, lci_RF$x)

calRF <- ggplot(RF_Cali_Plot, aes(x= exptd_RF$x, y=obs_RF$x)) +
  geom_point(size = 2) + 
  geom_smooth(method=lm, se=FALSE, col = "black", lwd = 1) +
  geom_abline(slope=1, intercept=0, lty=2 ) +
  scale_x_continuous(name = "Expected", breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) +
  scale_y_continuous(name = "Observed", breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) +
  geom_errorbar(aes(ymin=lci_RF$x, ymax=uci_RF$x), width=0.02) +
  theme_bw()
calRF

#for GBM
#create 10 risk groups dataset_val %>% mutate(quintile=ntile(dataset_val\(probsTestGbmGridVal.X2,10)) -> dataset_val_10_GBM dataset_val_10_GBM[as.numeric(dataset_val_10_GBM[,Youroutcomevariable])== 1,Youroutcomevariable_num] <- 0 dataset_val_10_GBM[as.numeric(dataset_val_10_GBM[,Youroutcomevariable])== 2,Youroutcomevariable_num] <- 1 #average the observed and expected probabilities of patients in each risk group obs_GBM = aggregate(as.numeric(dataset_val_10_GBM[,Youroutcomevariable_num]), list(dataset_val_10_GBM\)quintile),mean) exptd_GBM = aggregate(dataset_val_10_GBM\(probsTestGbmGridVal.X2, list(dataset_val_10_GBM\)quintile),mean) obsn_GBM =aggregate(as.formula(paste0(Youroutcomevariable ,“~ quintile”)), dataset_val_10_GBM, length)

#CIs for scatter points lci_GBM = obs_GBM- (1.96(((obs_GBM(1-obs_GBM))/obsn_GBM[,Youroutcomevariable])^.5)) lci_GBM[lci_GBM<0]<-0

uci_GBM = obs_GBM + (1.96(((obs_GBM(1-obs_GBM))/obsn_GBM[,Youroutcomevariable])^.5)) uci_GBM[uci_GBM>1]<-1 GBM_Cali_Plot = data.frame(exptd_GBM\(x,obs_GBM\)x, uci_GBM\(x, lci_GBM\)x)

calGBM <-ggplot(GBM_Cali_Plot, aes(x= exptd_GBM\(x, y=obs_GBM\)x)) + geom_point(size = 2) + geom_smooth(method=lm, se=FALSE, col = “black”, lwd = 1) + geom_abline(slope=1, intercept=0, lty=2 ) + scale_x_continuous(name = “Expected”, breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) + scale_y_continuous(name = “Observed”, breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) + geom_errorbar(aes(ymin=lci_GBM\(x, ymax=uci_GBM\)x), width=0.02) + theme_bw() calGBM #for KNN
#create 10 risk groups dataset_val %>% mutate(quintile=ntile(dataset_val\(probsTestknnGridVal.X2,10)) -> dataset_val_10_KNN dataset_val_10_KNN[as.numeric(dataset_val_10_KNN[,Youroutcomevariable])== 1,Youroutcomevariable_num] <- 0 dataset_val_10_KNN[as.numeric(dataset_val_10_KNN[,Youroutcomevariable])== 2,Youroutcomevariable_num] <- 1 #average the observed and expected probabilities of patients in each risk group obs_KNN = aggregate(as.numeric(dataset_val_10_KNN[,Youroutcomevariable_num]), list(dataset_val_10_KNN\)quintile),mean) exptd_KNN = aggregate(dataset_val_10_KNN\(probsTestknnGridVal.X2, list(dataset_val_10_KNN\)quintile),mean) obsn_KNN =aggregate(as.formula(paste0(Youroutcomevariable ,“~ quintile”)), dataset_val_10_KNN, length)

#CIs for scatter points lci_KNN = obs_KNN- (1.96(((obs_KNN(1-obs_KNN))/obsn_KNN[,Youroutcomevariable])^.5)) lci_KNN[lci_KNN<0]<-0

uci_KNN = obs_KNN + (1.96(((obs_KNN(1-obs_KNN))/obsn_KNN[,Youroutcomevariable])^.5)) uci_KNN[uci_KNN>1]<-1 KNN_Cali_Plot = data.frame(exptd_KNN\(x,obs_KNN\)x, uci_KNN\(x, lci_KNN\)x)

calKNN <- ggplot(KNN_Cali_Plot, aes(x= exptd_KNN\(x, y=obs_KNN\)x)) + geom_point(size = 2) + geom_smooth(method=lm, se=FALSE, col = “black”, lwd = 1) + geom_abline(slope=1, intercept=0, lty=2 ) + scale_x_continuous(name = “Expected”, breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) + scale_y_continuous(name = “Observed”, breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) + geom_errorbar(aes(ymin=lci_KNN\(x, ymax=uci_KNN\)x), width=0.02) + theme_bw() calKNN #for NN
#create 10 risk groups dataset_val %>% mutate(quintile=ntile(dataset_val\(probsTestnnetGridVal.X2,10)) -> dataset_val_10_NN dataset_val_10_NN[as.numeric(dataset_val_10_NN[,Youroutcomevariable])== 1,Youroutcomevariable_num] <- 0 dataset_val_10_NN[as.numeric(dataset_val_10_NN[,Youroutcomevariable])== 2,Youroutcomevariable_num] <- 1 #average the observed and expected probabilities of patients in each risk group obs_NN = aggregate(as.numeric(dataset_val_10_NN[,Youroutcomevariable_num]), list(dataset_val_10_NN\)quintile),mean) exptd_NN = aggregate(dataset_val_10_NN\(probsTestnnetGridVal.X2, list(dataset_val_10_NN\)quintile),mean) obsn_NN =aggregate(as.formula(paste0(Youroutcomevariable ,“~ quintile”)), dataset_val_10_NN, length)

#CIs for scatter points lci_NN = obs_NN- (1.96(((obs_NN(1-obs_NN))/obsn_NN[,Youroutcomevariable])^.5)) lci_NN[lci_NN<0]<-0

uci_NN = obs_NN + (1.96(((obs_NN(1-obs_NN))/obsn_NN[,Youroutcomevariable])^.5)) uci_NN[uci_NN>1]<-1 NN_Cali_Plot = data.frame(exptd_NN\(x,obs_NN\)x, uci_NN\(x, lci_NN\)x)

calNN <- ggplot(NN_Cali_Plot, aes(x= exptd_NN\(x, y=obs_NN\)x)) + geom_point(size = 2) + geom_smooth(method=lm, se=FALSE, col = “black”, lwd = 1) + geom_abline(slope=1, intercept=0, lty=2 ) + scale_x_continuous(name = “Expected”, breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) + scale_y_continuous(name = “Observed”, breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) + geom_errorbar(aes(ymin=lci_NN\(x, ymax=uci_NN\)x), width=0.02) + theme_bw() calNN

#for LGMARS #create 10 risk groups dataset_val %>% mutate(quintile=ntile(dataset_val\(probsTestmarsVal.X2,10)) -> dataset_val_10_MARS dataset_val_10_MARS[as.numeric(dataset_val_10_MARS[,Youroutcomevariable])== 1,Youroutcomevariable_num] <- 0 dataset_val_10_MARS[as.numeric(dataset_val_10_MARS[,Youroutcomevariable])== 2,Youroutcomevariable_num] <- 1 #average the observed and expected probabilities of patients in each risk group obs_MARS = aggregate(as.numeric(dataset_val_10_MARS[,Youroutcomevariable_num]), list(dataset_val_10_MARS\)quintile),mean) exptd_MARS = aggregate(dataset_val_10_MARS\(probsTestmarsVal.X2, list(dataset_val_10_MARS\)quintile),mean) obsn_MARS =aggregate(as.formula(paste0(Youroutcomevariable ,“~ quintile”)), dataset_val_10_MARS, length)

#CIs for scatter points lci_MARS = obs_MARS- (1.96(((obs_MARS(1-obs_MARS))/obsn_MARS[,Youroutcomevariable])^.5)) lci_MARS[lci_MARS<0]<-0

uci_MARS = obs_MARS + (1.96(((obs_MARS(1-obs_MARS))/obsn_MARS[,Youroutcomevariable])^.5)) uci_MARS[uci_MARS>1]<-1 MARS_Cali_Plot = data.frame(exptd_MARS\(x,obs_MARS\)x, uci_MARS\(x, lci_MARS\)x)

calMARS <- ggplot(MARS_Cali_Plot, aes(x= exptd_MARS\(x, y=obs_MARS\)x)) + geom_point(size = 2) + geom_smooth(method=lm, se=FALSE, col = “black”, lwd = 1) + geom_abline(slope=1, intercept=0, lty=2 ) + scale_x_continuous(name = “Expected”, breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) + scale_y_continuous(name = “Observed”, breaks = c(0.0, 0.2,0.4,0.6,0.8,1.0),limits = c(0,1)) + geom_errorbar(aes(ymin=lci_MARS\(x, ymax=uci_MARS\)x), width=0.02) + theme_bw() calMARS

plot_grid(calGBM,calKNN,calLG,calMARS,calNN,calRF,calSVM, labels = c(‘A’, ‘B’,‘C’,‘D’,‘E’,‘F’,‘G’), label_size = 12) ggsave(paste0(“calibration_sampling”, sampling, “formula_”,as.character(formula.model)[3],“.pdf”)) #calcluate Calibration slope for each model

dataset_val2 <- dataset_val dataset_val2[,Youroutcomevariable] <- as.numeric(dataset_val2[,Youroutcomevariable])-1

calslopeGBM <- glm(formula(paste0(Youroutcomevariable," ~ probsTestGbmGridVal.X2“)), family=gaussian, data=dataset_val2)\(coefficients calslopeSVM <-glm(formula(paste0(Youroutcomevariable," ~ probsTestSvmGridVal.X2")), family=gaussian, data=dataset_val2)\)coefficients calslopeKNN <-glm(formula(paste0(Youroutcomevariable,” ~ probsTestknnGridVal.X2“)), family=gaussian, data=dataset_val2)\(coefficients calslopeNN <-glm(formula(paste0(Youroutcomevariable," ~ probsTestnnetGridVal.X2")), family=gaussian, data=dataset_val2)\)coefficients calslopeRF <-glm(formula(paste0(Youroutcomevariable,” ~ probsTestRfVal.X2“)), family=gaussian, data=dataset_val2)\(coefficients calslopeLG <-glm(formula(paste0(Youroutcomevariable," ~ probsTestlgVal.X2")), family=gaussian, data=dataset_val2)\)coefficients calslopeMARS <-glm(formula(paste0(Youroutcomevariable,” ~ probsTestmarsVal.X2")), family=gaussian, data=dataset_val2)$coefficients

#calcluate Calibration in the large for each model########################################################### #predicted risks are understated if _b[_cons] > 0 or overstated if _b[_cons] < 0 callargeGBM <- summary(glm(formula(paste0(Youroutcomevariable," ~ offset(probsTestGbmGridVal.X2)“)), family=gaussian, data=dataset_val2))\(coefficients[1] callargeSVM <- summary(glm(formula(paste0(Youroutcomevariable," ~ offset(probsTestSvmGridVal.X2)")), family=gaussian, data=dataset_val2))\)coefficients[1] callargeKNN <- summary(glm(formula(paste0(Youroutcomevariable,” ~ offset(probsTestknnGridVal.X2)“)), family=gaussian, data=dataset_val2))\(coefficients[1] callargeNN <- summary(glm(formula(paste0(Youroutcomevariable," ~ offset(probsTestnnetGridVal.X2)")), family=gaussian, data=dataset_val2))\)coefficients[1] callargeRF <- summary(glm(formula(paste0(Youroutcomevariable,” ~ offset(probsTestRfVal.X2)“)), family=gaussian, data=dataset_val2))\(coefficients[1] callargeLG <- summary(glm(formula(paste0(Youroutcomevariable," ~ offset(probsTestlgVal.X2)")), family=gaussian, data=dataset_val2))\)coefficients[1] callargeMARS <- summary(glm(formula(paste0(Youroutcomevariable,” ~ offset(probsTestmarsVal.X2)")), family=gaussian, data=dataset_val2))$coefficients[1]

#calcluate overall misCalibration for each model############################################################# #the slope coefficient beta of the linear predictors reflects the deviations from the ideal slope of 1. #If p is significant then there is deviation from zero mc1 = glm(formula(paste0(Youroutcomevariable," ~ probsTestGbmGridVal.X2+ offset(probsTestGbmGridVal.X2)“)), family=gaussian, data=dataset_val2) mc2 = glm(formula(paste0(Youroutcomevariable,” ~ probsTestSvmGridVal.X2 + offset(probsTestSvmGridVal.X2)“)), family=gaussian, data=dataset_val2) mc3 = glm(formula(paste0(Youroutcomevariable,” ~ probsTestknnGridVal.X2 + offset(probsTestknnGridVal.X2)“)), family=gaussian, data=dataset_val2) mc4 = glm(formula(paste0(Youroutcomevariable,” ~ probsTestnnetGridVal.X2 + offset(probsTestnnetGridVal.X2)“)), family=gaussian, data=dataset_val2) mc5 = glm(formula(paste0(Youroutcomevariable,” ~ probsTestRfVal.X2 + offset(probsTestRfVal.X2)“)), family=gaussian, data=dataset_val2) mc6 = glm(formula(paste0(Youroutcomevariable,” ~ probsTestlgVal.X2 + offset(probsTestlgVal.X2)“)), family=gaussian, data=dataset_val2) mc7 = glm(formula(paste0(Youroutcomevariable,” ~ probsTestmarsVal.X2 + offset(probsTestmarsVal.X2)")), family=gaussian, data=dataset_val2)

calpvalueGBM <- summary(mc1)\(coefficients[2,c(4)] calpvalueSVM <-summary(mc2)\)coefficients[2,c(4)] calpvalueKNN <-summary(mc3)\(coefficients[2,c(4)] calpvalueNN <-summary(mc4)\)coefficients[2,c(4)] calpvalueRF <- summary(mc5)\(coefficients[2,c(4)] calpvalueLG <- summary(mc6)\)coefficients[2,c(4)] calpvalueMARS <- summary(mc7)$coefficients[2,c(4)]

model=c(‘LR’,‘GBM’,‘SVM’,‘RF’,‘Nnet’,‘Knn’,‘LGMARS’) intercept = c(calslopeLG[1], calslopeGBM[1], calslopeSVM[1], calslopeRF[1], calslopeNN[1], calslopeKNN[1],calslopeMARS[1]) slope = c(calslopeLG[2], calslopeGBM[2], calslopeSVM[2], calslopeRF[2], calslopeNN[2], calslopeKNN[2],calslopeMARS[2]) inthelarge = c(callargeLG, callargeGBM, callargeSVM, callargeRF, callargeNN, callargeKNN,callargeMARS) pvalue = c(calpvalueLG, calpvalueGBM, calpvalueSVM, calpvalueRF, calpvalueNN, calpvalueKNN,calpvalueMARS) ValResults = data.frame(model, intercept, slope, inthelarge, pvalue)

myft <- regulartable(ValResults) myft <- theme_vanilla(myft) myft <- autofit(myft) doc <- read_docx() doc <- body_add_flextable(doc, value = myft) print(doc, target = paste0(“calibrationplot_sampling”, sampling, “formula_”,as.character(formula.model)[3],“.docx”))

#Decision curve analysis dataset_val_10\(probsTestlgVal.X1 = 1-dataset_val_10\)probsTestlgVal.X2

dcaLR = dca(data = dataset_val_10, outcome = “insulinRequire num”, predictors = c(“probsTestlgVal.X2”)) dcaSVM = dca(data = dataset_val_10, outcome = “insulinRequire num”, predictors = c(“probsTestSvmGridVal.X2”)) dcaRF = dca(data = dataset_val_10, outcome = “insulinRequire num”, predictors = c(“probsTestRfVal.X2”)) dcaGBM = dca(data = dataset_val_10, outcome = “insulinRequire num”, predictors = c(“probsTestGbmGridVal.X2”)) dcaKNN = dca(data = dataset_val_10, outcome = “insulinRequire num”, predictors = c(“probsTestknnGridVal.X2”)) dcaNN = dca(data = dataset_val_10, outcome = “insulinRequire num”, predictors = c(“probsTestnnetGridVal.X2”)) dcaMARS = dca(data = dataset_val_10, outcome = “insulinRequire num”, predictors = c(“probsTestmarsVal.X2”))

#https://www.mskcc.org/sites/default/files/node/4511/documents/v3-worked-example-of-decision-curve-analysis-using-r.pdf

threshold <- dcaLR\(net.benefit\)threshold100 probtest <- dcaLR\(net.benefit\)probsTestlgVal.X2 model <- rep(“LR”,length(probtest)) dcadata <- data.frame(model,probtest,threshold) threshold <- dcaSVM\(net.benefit\)threshold100 probtest <- dcaSVM\(net.benefit\)probsTestSvmGridVal.X2 model <- rep(“SVM”,length(probtest)) dcadata <- rbind(dcadata, data.frame(model,probtest,threshold)) threshold <- dcaRF\(net.benefit\)threshold100 probtest <- dcaRF\(net.benefit\)probsTestRfVal.X2 model <- rep(“RF”,length(probtest)) dcadata <- rbind(dcadata, data.frame(model,probtest,threshold)) threshold <- dcaGBM\(net.benefit\)threshold100 probtest <- dcaGBM\(net.benefit\)probsTestGbmGridVal.X2 model <- rep(“GBM”,length(probtest)) dcadata <- rbind(dcadata, data.frame(model,probtest,threshold)) threshold <- dcaKNN\(net.benefit\)threshold100 probtest <- dcaKNN\(net.benefit\)probsTestknnGridVal.X2 model <- rep(“KNN”,length(probtest)) dcadata <- rbind(dcadata, data.frame(model,probtest,threshold)) threshold <- dcaNN\(net.benefit\)threshold100 probtest <- dcaNN\(net.benefit\)probsTestnnetGridVal.X2 model <- rep(“NN”,length(probtest)) dcadata <- rbind(dcadata, data.frame(model,probtest,threshold)) threshold <- dcaMARS\(net.benefit\)threshold100 probtest <- dcaMARS\(net.benefit\)probsTestmarsVal.X2 model <- rep(“MARS”,length(probtest)) dcadata <- rbind(dcadata, data.frame(model,probtest,threshold)) threshold <- dcaMARS\(net.benefit\)threshold100 probtest <- dcaMARS\(net.benefit\)all model <- rep(“all”,length(probtest)) dcadata <- rbind(dcadata, data.frame(model,probtest,threshold)) threshold <- dcaMARS\(net.benefit\)threshold*100 probtest <- dcaMARS\(net.benefit\)none model <- rep(“none”,length(probtest)) dcadata <- rbind(dcadata, data.frame(model,probtest,threshold))

ggplot(dcadata, aes(x = threshold, y = probtest, group = model, linetype = model, colour = model)) + geom_line() + coord_cartesian(ylim =c(-0.25,0.20)) + theme(legend.key.width = unit(1.5,“cm”)) + xlab(“Probability threshold”) + ylab(“Net benefit”) + theme(legend.title=element_blank()) + theme(legend.position = c(0.7, 0.8)) ggsave(paste0(“decision_curve_analysis”,sampling, “formula_”,as.character(formula.model)[3],“.pdf”), units = “in”, height = 6.5, width = 6.5)

#correlation matrix of predictions - validation dataset predMatrixVal = data.frame(dataset_val\(probsTestGbmGridVal.X2 ,dataset_val\)probsTestSvmGridVal.X2, dataset_val\(probsTestknnGridVal.X2 , dataset_val\)probsTestnnetGridVal.X2 , dataset_val\(probsTestRfVal.X2, dataset_val\)probsTestlgVal.X2, dataset_val$probsTestmarsVal.X2) names(predMatrixVal)[1] <-“GBM” names(predMatrixVal)[2] <-“SVM” names(predMatrixVal)[3] <-“KNN” names(predMatrixVal)[4] <-“NN” names(predMatrixVal)[5] <-“RF” names(predMatrixVal)[6] <-“LR” names(predMatrixVal)[7] <-“LRMARS”

MVal = cor(predMatrixVal) pdf(paste0(“corrplot”, sampling, “formula_”,as.character(formula.model)[3],“.pdf”)) corrplot(MVal, method=“number”,tl.cex = 1) dev.off()

library(GGally) ggpairs(predMatrixVal,labeller = “label_parsed”, axisLabels = “none”) ggsave(paste0(“corrplot_scatter”, sampling, “formula_”,as.character(formula.model)[3],“.pdf”),units = “in”,height = 9, width = 9) #create a variable importance dataframe########################################################### # Svm and KNN do not have built-in variable importance score Model = c(‘Logistic Regression’,‘Stochastic Gradient Boosting’, ‘Neural Network’, ‘Random Forest’, ‘GLM MARS’) # calculate the variable importance scores # varImp function provides the variable importance LGImp <- varImp(modelLG_CC_GADA, scale = FALSE) LGImp

gmbImp <- varImp(modelGbm_CC_GADA_Lr, scale = FALSE) gmbImp

nnetImp <- varImp(modelnnet_CC_GADA_Lr, scale = FALSE) nnetImp

rfImp <- varImp(modelRf_CC_GADA, scale = FALSE) rfImp

MarsImp <- varImp(modelMARS_CC_GADA_Lr, scale = FALSE) MarsImp

impdata <- data.frame(LG = LGImp\(importance,GBM = gmbImp\)importance, NNET = nnetImp\(importance,LGMARS = MarsImp\)importance) impdata <- cbind(rownames(impdata),impdata) names(impdata) <- c(“variables”,“LR”,“GBM”,“NNET”,“LGMARS”) impdata <- impdata %>% gather(models,values, -variables) %>% dplyr::group_by(models) %>% dplyr::mutate(values =values/sum(values) * 100) %>% dplyr::ungroup()

ggplot(impdata,aes(x = variables, y = values)) + geom_bar(stat=“identity”,width=0.06) + coord_flip()+ ylab(“Scaled variable importance score”) + xlab("") + scale_y_continuous(expand = c(0, 0)) + theme(axis.text.y = element_blank(),axis.ticks.y = element_blank()) + facet_grid(models~.)

ggsave(paste0(“parameters_importance”, sampling, “formula_”,as.character(formula.model)[3],“.pdf”)) #build the DF with the scaled variable importance scores #divide each variable importance scores by max to scale # Yourcovariate1 = c(1.00,0.99,0.43,0.97) # Yourcovariate2 = c(0.73, 0.53, 0.65, 1.00) # Yourcovariate3 = c(0.99,1.00,1.00, 0.91) # varImpDF = data.frame(Model,Yourcovariate1,Yourcovariate2,Yourcovariate3) # # #build the plots of the variable importance ranks # plotVarImp1 = ggplot(data = varImpDF, aes(x = Model, y = varImpDF\(Yourcovariate1))+geom_bar(stat="identity",width=0.06)+ coord_flip()+ ylab("Scaled variable importance score")+ # xlab("") + ggtitle("Your covariate 1") + scale_y_continuous(expand = c(0, 0)) +theme(axis.text.y = element_blank(),axis.ticks.y = element_blank() ) # # plotVarImp2 = ggplot(data = varImpDF, aes(x = Model, y = varImpDF\)Yourcovariate2))+geom_bar(stat=“identity”,width=0.06)+ coord_flip()+ ylab(“Scaled variable importance score”)+ # xlab("“) + ggtitle(”Your covariate 2“) + scale_y_continuous(expand = c(0, 0)) +theme(axis.text.y = element_blank(),axis.ticks.y = element_blank() ) # # plotVarImp3 = ggplot(data = varImpDF, aes(x = Model, y = varImpDF\(Yourcovariate3))+geom_bar(stat="identity",width=0.06)+ coord_flip()+ ylab("Scaled variable importance score")+ # xlab("") + ggtitle("Your covariate 3") + scale_y_continuous(expand = c(0, 0)) +theme(axis.text.y = element_blank(),axis.ticks.y = element_blank() ) # # plotVarImp3 = ggplot(data = varImpDF, aes(x = Model, y = varImpDF\)Yourcovariate3))+geom_bar(stat=”identity“,width=0.06)+ coord_flip()+ ylab(”Scaled variable importance score“)+ # xlab(”“) + ggtitle(”Your covariate 3") + scale_y_continuous(expand = c(0, 0)) +theme(axis.text.y = element_blank(),axis.ticks.y = element_blank() ) # # #plot the charts on one row # par(mfrow=c(1,3)) # plot(plotVarImp1) # plot(plotVarImp2) # plot(plotVarImp3) # grid.arrange(plotVarImp1, plotVarImp2, plotVarImp3,ncol = 3)

5 - save the objects for future use

save(data_train,dataset_val,ValResults,control,modelLG_CC_GADA, modelRf_CC_GADA, modelSvm_CC_GADA_Lr, modelGbm_CC_GADA_Lr, modelnnet_CC_GADA_Lr,modelknn_CC_GADA_Lr,impdata, results_grid_CC_GADA,gmbImp, rfImp, nnetImp,dataset_val_10, file = paste0(“results_sampling”, sampling, “formula_”,as.character(formula.model)[3],“.Rdata”))