myIndex.df <- function(){ # 独自の命令の名前は変えておきましょう
topicV <- NULL # topic用
scoreV <- NULL # score用
fileV <- NULL
typeV <- NULL
tokenV <- NULL
TTRV <- NULL
GIV <- NULL
NoSV <- NULL
ASLV <- NULL
AWLV <- NULL
file.zenbu <- list.files() #
ruiseki <- "" #
for (i in file.zenbu){ #
yomikomi <- readLines(i, warn=F) #
topic.tmp <- grep("@Topic:", yomikomi, value=T) # Topicの行
topic <- gsub("@Topic:\t", "", topic.tmp) # 不要部分削除
score.tmp <- grep("@Criterion", yomikomi, value=T) # Scoreの行
score <- gsub("@Criterion:\t", "", score.tmp) # 不要部分削除
tmp1 <- grep("\\*(JPN|NS)", yomikomi, value=T) #
tmp2 <- gsub("\\*(JPN|NS)...:\t", "", tmp1) #
tmp2b <- gsub("[[:punct:]]", "", tmp2) #
tmp2c <- tolower(tmp2b) #
tmp3 <- strsplit(tmp2c, " ") #
tmp4 <- unlist(tmp3) #
tmp4 <- tmp4[tmp4 != ""] #
token.list <- sort(tmp4) #
type.list <- unique(token.list) #
token <- length(token.list) #
type <- length(type.list) #
TTR <- type/token #
GI <- type/sqrt(token)
NoS <- length(tmp1)
ASL <- token/NoS
mojiretu <- paste(token.list, collapse="") #
mojisuu <- nchar(mojiretu) #
AWL <- mojisuu/token #
score <- as.integer(score) # scoreを整数に
# 各要素の種類ごとにベクトルを作成
topicV <- c(topicV, topic) # Topicの追加
scoreV <- c(scoreV, score) # Scoreの追加
fileV <- c(fileV, i)
tokenV <- c(tokenV, token)
typeV <- c(typeV, type)
TTRV <- c(TTRV, TTR)
GIV <- c(GIV, GI)
NoSV <- c(NoSV, NoS)
ASLV <- c(ASLV, ASL)
AWLV <- c(AWLV, AWL)
}
data.frame(fileV, topicV, scoreV, tokenV, typeV, TTRV, GIV, NoSV, ASLV, AWLV) # 追加修正
}
setwd("NICER_NNS")
NNS.Index.df <- myIndex.df()
names(NNS.Index.df) <- c("ID", "Topic", "Score", "Token", "Type", "TTR", "GI", "NoS", "ASL", "AWL") # 見出しの名前も変えて
NNS.Index.df$ID <- as.factor(NNS.Index.df$ID)
NNS.Index.df$Topic <- as.factor(NNS.Index.df$Topic)
NNS.Index.df2 <- na.omit(NNS.Index.df)
summary(NNS.Index.df2)
## ID Topic Score Token
## JPN501.txt: 1 education:145 Min. :1.000 Min. : 85.0
## JPN502.txt: 1 money : 77 1st Qu.:3.000 1st Qu.:209.0
## JPN503.txt: 1 sports :157 Median :3.000 Median :262.0
## JPN504.txt: 1 Mean :3.522 Mean :275.6
## JPN505.txt: 1 3rd Qu.:4.000 3rd Qu.:322.5
## JPN506.txt: 1 Max. :5.000 Max. :728.0
## (Other) :373
## Type TTR GI NoS
## Min. : 49.0 Min. :0.2531 Min. : 4.566 Min. : 7.00
## 1st Qu.:101.0 1st Qu.:0.4232 1st Qu.: 6.952 1st Qu.:17.00
## Median :122.0 Median :0.4699 Median : 7.503 Median :21.00
## Mean :125.7 Mean :0.4698 Mean : 7.586 Mean :22.08
## 3rd Qu.:146.0 3rd Qu.:0.5137 3rd Qu.: 8.283 3rd Qu.:26.00
## Max. :251.0 Max. :0.6581 Max. :10.443 Max. :51.00
##
## ASL AWL
## Min. : 6.96 Min. :3.507
## 1st Qu.:10.81 1st Qu.:4.163
## Median :12.21 Median :4.395
## Mean :12.71 Mean :4.420
## 3rd Qu.:14.11 3rd Qu.:4.652
## Max. :24.00 Max. :5.415
##
setwd("NICER_NS")
NS.Index.df <- myIndex.df()
names(NS.Index.df) <- c("ID", "Topic", "Score", "Token", "Type", "TTR", "GI", "NoS", "ASL", "AWL") # 見出しの名前も変えて
NS.Index.df$ID <- as.factor(NS.Index.df$ID)
NS.Index.df$Topic <- as.factor(NS.Index.df$Topic)
NS.Index.df2 <- na.omit(NS.Index.df)
summary(NS.Index.df2)
## ID Topic Score Token Type
## NS501.txt: 1 education:28 Min. :4.000 Min. :451.0 Min. :223.0
## NS502.txt: 1 money : 4 1st Qu.:5.000 1st Qu.:670.5 1st Qu.:302.5
## NS503.txt: 1 sports : 8 Median :6.000 Median :826.5 Median :339.5
## NS504.txt: 1 Mean :5.625 Mean :786.8 Mean :339.6
## NS505.txt: 1 3rd Qu.:6.000 3rd Qu.:932.0 3rd Qu.:378.5
## NS506.txt: 1 Max. :6.000 Max. :990.0 Max. :470.0
## (Other) :34
## TTR GI NoS ASL
## Min. :0.3330 Min. : 9.936 Min. :22.00 Min. :15.53
## 1st Qu.:0.4062 1st Qu.:11.366 1st Qu.:29.75 1st Qu.:19.21
## Median :0.4324 Median :12.093 Median :37.50 Median :21.71
## Mean :0.4366 Mean :12.110 Mean :36.20 Mean :22.25
## 3rd Qu.:0.4705 3rd Qu.:12.891 3rd Qu.:43.25 3rd Qu.:23.81
## Max. :0.5346 Max. :15.106 Max. :55.00 Max. :37.91
##
## AWL
## Min. :4.167
## 1st Qu.:4.682
## Median :4.910
## Mean :4.892
## 3rd Qu.:5.123
## Max. :5.633
##
## 型は同じで、二種類のグループのデータフレームの構築
NNS.Index.df2$Lang <- "jp"
NS.Index.df2$Lang <- "ns"
str(NNS.Index.df2)
## 'data.frame': 379 obs. of 11 variables:
## $ ID : Factor w/ 381 levels "JPN501.txt","JPN502.txt",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Topic: Factor w/ 3 levels "education","money",..: 3 1 1 3 3 2 1 3 3 1 ...
## $ Score: int 4 4 3 4 4 3 4 3 4 3 ...
## $ Token: int 319 351 201 260 417 260 355 195 260 183 ...
## $ Type : int 134 158 121 139 174 123 149 97 103 99 ...
## $ TTR : num 0.42 0.45 0.602 0.535 0.417 ...
## $ GI : num 7.5 8.43 8.53 8.62 8.52 ...
## $ NoS : int 30 29 13 27 25 20 26 20 19 14 ...
## $ ASL : num 10.63 12.1 15.46 9.63 16.68 ...
## $ AWL : num 4.3 4.29 4.75 4.77 4.02 ...
## $ Lang : chr "jp" "jp" "jp" "jp" ...
## - attr(*, "na.action")= 'omit' Named int [1:2] 83 159
## ..- attr(*, "names")= chr [1:2] "83" "159"
str(NS.Index.df2)
## 'data.frame': 40 obs. of 11 variables:
## $ ID : Factor w/ 71 levels "NS501.txt","NS502.txt",..: 1 2 3 4 5 6 7 8 10 11 ...
## $ Topic: Factor w/ 3 levels "education","money",..: 1 1 1 1 3 1 1 1 2 3 ...
## $ Score: int 5 6 6 6 6 6 5 6 6 6 ...
## $ Token: int 736 636 834 824 898 829 597 848 760 886 ...
## $ Type : int 359 340 353 336 393 339 262 332 301 372 ...
## $ TTR : num 0.488 0.535 0.423 0.408 0.438 ...
## $ GI : num 13.2 13.5 12.2 11.7 13.1 ...
## $ NoS : int 39 26 22 30 39 31 27 43 22 45 ...
## $ ASL : num 18.9 24.5 37.9 27.5 23 ...
## $ AWL : num 4.59 5.2 5.57 5.28 4.75 ...
## $ Lang : chr "ns" "ns" "ns" "ns" ...
## - attr(*, "na.action")= 'omit' Named int [1:31] 9 15 18 19 20 22 24 25 26 30 ...
## ..- attr(*, "names")= chr [1:31] "9" "15" "18" "19" ...
Index.dat <- rbind(NNS.Index.df2, NS.Index.df2)
str(Index.dat)
## 'data.frame': 419 obs. of 11 variables:
## $ ID : Factor w/ 452 levels "JPN501.txt","JPN502.txt",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Topic: Factor w/ 3 levels "education","money",..: 3 1 1 3 3 2 1 3 3 1 ...
## $ Score: int 4 4 3 4 4 3 4 3 4 3 ...
## $ Token: int 319 351 201 260 417 260 355 195 260 183 ...
## $ Type : int 134 158 121 139 174 123 149 97 103 99 ...
## $ TTR : num 0.42 0.45 0.602 0.535 0.417 ...
## $ GI : num 7.5 8.43 8.53 8.62 8.52 ...
## $ NoS : int 30 29 13 27 25 20 26 20 19 14 ...
## $ ASL : num 10.63 12.1 15.46 9.63 16.68 ...
## $ AWL : num 4.3 4.29 4.75 4.77 4.02 ...
## $ Lang : chr "jp" "jp" "jp" "jp" ...
## - attr(*, "na.action")= 'omit' Named int [1:2] 83 159
## ..- attr(*, "names")= chr [1:2] "83" "159"
Index.dat$Lang <- as.factor(Index.dat$Lang)
library(rpart)
DT.Index.model.1 <- rpart(Lang ~ Type + Token + TTR + GI + NoS + AWL + ASL, data=Index.dat)
library(partykit)
## Warning: パッケージ 'partykit' はバージョン 4.3.2 の R の下で造られました
## 要求されたパッケージ grid をロード中です
## 要求されたパッケージ libcoin をロード中です
## Warning: パッケージ 'libcoin' はバージョン 4.3.2 の R の下で造られました
## 要求されたパッケージ mvtnorm をロード中です
plot(as.party(DT.Index.model.1))
DT.Index.model.1$variable.importance
## Type GI Token ASL NoS AWL
## 68.418046 61.576241 56.444888 41.050827 18.814963 5.131353
library(MASS)
DA.result <- lda(Lang ~ Type + Token + TTR + GI + NoS + AWL + ASL, data=Index.dat)
DA.result
## Call:
## lda(Lang ~ Type + Token + TTR + GI + NoS + AWL + ASL, data = Index.dat)
##
## Prior probabilities of groups:
## jp ns
## 0.90453461 0.09546539
##
## Group means:
## Type Token TTR GI NoS AWL ASL
## jp 125.6596 275.562 0.4697583 7.585553 22.07652 4.420320 12.70849
## ns 339.6500 786.825 0.4365574 12.109990 36.20000 4.892464 22.24680
##
## Coefficients of linear discriminants:
## LD1
## Type 0.10770827
## Token -0.00514467
## TTR 11.11730334
## GI -2.24071627
## NoS -0.10783274
## AWL 0.36322734
## ASL -0.08159226
plot(DA.result)
predict(DA.result)$class
## [1] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [26] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [51] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [76] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [101] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [126] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [151] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [176] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [201] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [226] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [251] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [276] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [301] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [326] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [351] jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp jp
## [376] jp jp jp jp ns ns ns ns ns ns ns ns ns ns jp jp ns ns ns ns ns ns ns ns ns
## [401] ns ns ns jp ns ns jp jp ns ns ns ns ns ns ns ns ns ns ns
## Levels: jp ns
Index.tbl <- table(Index.dat$Lang, predict(DA.result)$class)
Index.tbl
##
## jp ns
## jp 379 0
## ns 5 35
plot(DA.result)
http://aoki2.si.gunma-u.ac.jp/R/sdis.html
source(“http://aoki2.si.gunma-u.ac.jp/R/src/sdis.R”, encoding=“euc-jp”)
sdis.R <- source("http://aoki2.si.gunma-u.ac.jp/R/src/sdis.R", encoding="euc-jp")
sdis(言語特徴, 判別クラス)
str(Index.dat)
## 'data.frame': 419 obs. of 11 variables:
## $ ID : Factor w/ 452 levels "JPN501.txt","JPN502.txt",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Topic: Factor w/ 3 levels "education","money",..: 3 1 1 3 3 2 1 3 3 1 ...
## $ Score: int 4 4 3 4 4 3 4 3 4 3 ...
## $ Token: int 319 351 201 260 417 260 355 195 260 183 ...
## $ Type : int 134 158 121 139 174 123 149 97 103 99 ...
## $ TTR : num 0.42 0.45 0.602 0.535 0.417 ...
## $ GI : num 7.5 8.43 8.53 8.62 8.52 ...
## $ NoS : int 30 29 13 27 25 20 26 20 19 14 ...
## $ ASL : num 10.63 12.1 15.46 9.63 16.68 ...
## $ AWL : num 4.3 4.29 4.75 4.77 4.02 ...
## $ Lang : Factor w/ 2 levels "jp","ns": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "na.action")= 'omit' Named int [1:2] 83 159
## ..- attr(*, "names")= chr [1:2] "83" "159"
Index.sdis <- sdis(Index.dat[4:10], Index.dat[11])
## 有効ケース数: 419
## 群を表す変数: Lang
##
## ***** 平均値 *****
## jp ns 全体
## Token 275.5620053 786.8250000 324.3699284
## Type 125.6596306 339.6500000 146.0883055
## TTR 0.4697583 0.4365574 0.4665888
## GI 7.5855529 12.1099899 8.0174800
## NoS 22.0765172 36.2000000 23.4248210
## ASL 12.7084865 22.2467985 13.6190652
## AWL 4.4203201 4.8924642 4.4653935
##
## ***** プールされた群内相関係数行列 *****
##
## Token Type TTR GI NoS ASL
## Token 1.00000000 0.88371833 -0.5933742 0.5050770 0.7556768 0.3437528
## Type 0.88371833 1.00000000 -0.2218896 0.8337986 0.6839948 0.2864962
## TTR -0.59337420 -0.22188965 1.0000000 0.3025576 -0.4735379 -0.2244020
## GI 0.50507700 0.83379859 0.3025576 1.0000000 0.4141240 0.1609237
## NoS 0.75567680 0.68399477 -0.4735379 0.4141240 1.0000000 -0.3008152
## ASL 0.34375281 0.28649621 -0.2244020 0.1609237 -0.3008152 1.0000000
## AWL -0.09236193 0.02364751 0.2545283 0.1654066 -0.1993553 0.1751076
## AWL
## Token -0.09236193
## Type 0.02364751
## TTR 0.25452825
## GI 0.16540662
## NoS -0.19935530
## ASL 0.17510755
## AWL 1.00000000
##
## 変数編入基準 Pin: 0.05
## 変数除去基準 Pout: 0.05
## 編入候補変数: Type P : <0.001 ***** 編入されました
##
## ***** ステップ 1 ***** 編入変数: Type
##
## ***** 分類関数 *****
##
## jp ns 偏F値 P値
## Type -0.19697 -0.53239 1298.5 <0.001
## 定数項 12.37548 90.41357
## ウィルクスのΛ: 0.24308
## 等価なF値: 1298.5
## 自由度: (1, 417.00)
## P値: <0.001
##
## 除去候補変数: Type P : <0.001 ***** 除去されませんでした
## 編入候補変数: NoS P : <0.001 ***** 編入されました
##
## ***** ステップ 2 ***** 編入変数: NoS
##
## ***** 分類関数 *****
##
## jp ns 偏F値 P値
## Type -0.14717 -0.63485 1086.734 <0.001
## NoS -0.36497 0.75086 73.791 <0.001
## 定数項 13.27524 94.22198
## ウィルクスのΛ: 0.20646
## 等価なF値: 799.48
## 自由度: (2, 416.00)
## P値: <0.001
##
## 除去候補変数: NoS P : <0.001 ***** 除去されませんでした
## 編入候補変数: GI P : <0.001 ***** 編入されました
##
## ***** ステップ 3 ***** 編入変数: GI
##
## ***** 分類関数 *****
##
## jp ns 偏F値 P値
## Type 0.93647 0.20310 370.634 <0.001
## NoS -1.90610 -0.44084 106.171 <0.001
## GI -37.35904 -28.88835 34.634 <0.001
## 定数項 103.89608 148.40739
## ウィルクスのΛ: 0.19055
## 等価なF値: 587.62
## 自由度: (3, 415.00)
## P値: <0.001
##
## 除去候補変数: GI P : <0.001 ***** 除去されませんでした
## 編入候補変数: TTR P : <0.001 ***** 編入されました
##
## ***** ステップ 4 ***** 編入変数: TTR
##
## ***** 分類関数 *****
##
## jp ns 偏F値 P値
## Type 0.35512 -0.95865 327.251 <0.001
## NoS -2.24220 -1.11248 45.157 <0.001
## GI -14.53054 16.73083 98.642 <0.001
## TTR -235.78283 -471.17515 61.629 <0.001
## 定数項 112.92960 184.48165
## ウィルクスのΛ: 0.16586
## 等価なF値: 520.51
## 自由度: (4, 414.00)
## P値: <0.001
##
## 除去候補変数: NoS P : <0.001 ***** 除去されませんでした
## 編入候補変数: Token P : 0.00232 ***** 編入されました
##
## ***** ステップ 5 ***** 編入変数: Token
##
## ***** 分類関数 *****
##
## jp ns 偏F値 P値
## Type 6.1404 4.371769 126.9771 < 0.001
## NoS -1.0748 -0.036849 35.3858 < 0.001
## GI -100.1752 -62.180259 102.9779 < 0.001
## TTR -581.9016 -790.081444 41.8866 < 0.001
## Token -1.6396 -1.510710 9.3929 0.00232
## 定数項 368.5913 401.522512
## ウィルクスのΛ: 0.16217
## 等価なF値: 426.73
## 自由度: (5, 413.00)
## P値: <0.001
##
## 除去候補変数: Token P : 0.00232 ***** 除去されませんでした
## 編入候補変数: AWL P : 0.0461 ***** 編入されました
##
## ***** ステップ 6 ***** 編入変数: AWL
##
## ***** 分類関数 *****
##
## jp ns 偏F値 P値
## Type 5.9534 4.1716 127.2980 < 0.001
## NoS -2.2004 -1.2414 28.0422 < 0.001
## GI -90.3392 -51.6544 105.2775 < 0.001
## TTR -559.6857 -766.3075 40.6418 < 0.001
## Token -1.5849 -1.4521 9.8317 0.00184
## AWL -75.2047 -80.4791 4.0015 0.04612
## 定数項 508.9176 562.2224
## ウィルクスのΛ: 0.16061
## 等価なF値: 358.86
## 自由度: (6, 412.00)
## P値: <0.001
##
## 除去候補変数: AWL P : 0.0461 ***** 除去されませんでした
## 編入候補変数: ASL P : 0.178 ***** 編入されませんでした
##
## ===================== 結果 =====================
##
## ***** 分類関数 *****
##
## jp ns 偏F値 P値
## Type 5.9534 4.1716 127.2980 < 0.001
## NoS -2.2004 -1.2414 28.0422 < 0.001
## GI -90.3392 -51.6544 105.2775 < 0.001
## TTR -559.6857 -766.3075 40.6418 < 0.001
## Token -1.5849 -1.4521 9.8317 0.00184
## AWL -75.2047 -80.4791 4.0015 0.04612
## 定数項 508.9176 562.2224
##
## ***** 判別関数 *****
##
## jp と ns の判別
## マハラノビスの汎距離: 7.76094
## 理論的誤判別率: <0.001
##
## 判別係数 標準化判別係数
## Type -0.890868 -64.38981
## NoS 0.479493 3.94694
## GI 19.342368 32.16818
## TTR -103.310884 -6.65911
## Token 0.066374 11.99121
## AWL -2.637203 -0.95444
## 定数項 26.652389
##
## ***** 判別結果集計表 ****
##
## 判別された群
## 実際の群 jp ns
## jp 379 0
## ns 3 37
Index.sdis
## $分類関数
##
## jp ns 偏F値 P値
## Type 5.9534 4.1716 127.2980 < 0.001
## NoS -2.2004 -1.2414 28.0422 < 0.001
## GI -90.3392 -51.6544 105.2775 < 0.001
## TTR -559.6857 -766.3075 40.6418 < 0.001
## Token -1.5849 -1.4521 9.8317 0.00184
## AWL -75.2047 -80.4791 4.0015 0.04612
## 定数項 508.9176 562.2224
##
## $個々の判別
##
## 実際の群 判別された群 正否 二乗距離1 二乗距離2 P値1 P値2 判別値
## 1 jp jp 2.01889 68.4263 0.95879 < 0.001 33.20372
## 2 jp jp 1.56306 58.3487 0.98005 < 0.001 28.39280
## 3 jp jp 6.78651 64.3944 0.45144 < 0.001 28.80394
## 4 jp jp 4.28389 68.2152 0.74656 < 0.001 31.96568
## 5 jp jp 5.47130 50.2699 0.60265 < 0.001 22.39928
## 6 jp jp 1.63164 65.2574 0.97740 < 0.001 31.81288
## 7 jp jp 1.09772 57.0944 0.99309 < 0.001 27.99832
## 8 jp jp 1.80083 68.9347 0.97004 < 0.001 33.56693
## 9 jp jp 3.60005 69.8536 0.82452 < 0.001 33.12677
## 10 jp jp 2.38289 65.1977 0.93565 < 0.001 31.40742
## 11 jp jp 3.88002 47.4032 0.79349 < 0.001 21.76158
## 12 jp jp 5.01740 50.2027 0.65784 < 0.001 22.59266
## 13 jp jp 6.96309 78.1085 0.43273 < 0.001 35.57272
## 14 jp jp 4.40317 61.8481 0.73234 < 0.001 28.72248
## 15 jp jp 0.47024 66.2159 0.99955 < 0.001 32.87282
## 16 jp jp 8.85793 54.3579 0.26302 < 0.001 22.75000
## 17 jp jp 4.73675 73.9050 0.69205 < 0.001 34.58412
## 18 jp jp 2.41729 73.3213 0.93320 < 0.001 35.45198
## 19 jp jp 4.16853 53.4425 0.76017 < 0.001 24.63701
## 20 jp jp 5.55949 43.0989 0.59202 < 0.001 18.76970
## 21 jp jp 4.08245 62.6098 0.77023 < 0.001 29.26369
## 22 jp jp 2.04311 69.3814 0.95742 < 0.001 33.66916
## 23 jp jp 1.30570 65.4240 0.98829 < 0.001 32.05917
## 24 jp jp 1.01859 67.0822 0.99452 < 0.001 33.03183
## 25 jp jp 2.99695 69.5943 0.88528 < 0.001 33.29867
## 26 jp jp 7.20008 56.3348 0.40835 < 0.001 24.56735
## 27 jp jp 17.00304 83.7623 0.01738 < 0.001 33.37965
## 28 jp jp 3.11509 59.2119 0.87416 < 0.001 28.04843
## 29 jp jp 4.53548 60.1305 0.71644 < 0.001 27.79750
## 30 jp jp 2.87615 69.4030 0.89622 < 0.001 33.26343
## 31 jp jp 3.69681 63.3574 0.81396 < 0.001 29.83028
## 32 jp jp 4.17238 67.9130 0.75972 < 0.001 31.87030
## 33 jp jp 2.92225 72.1561 0.89210 < 0.001 34.61690
## 34 jp jp 2.13053 69.2084 0.95226 < 0.001 33.53893
## 35 jp jp 3.14754 52.1788 0.87103 < 0.001 24.51565
## 36 jp jp 1.67403 70.9171 0.97567 < 0.001 34.62156
## 37 jp jp 5.94468 61.7712 0.54622 < 0.001 27.91328
## 38 jp jp 3.32774 74.4095 0.85312 < 0.001 35.54087
## 39 jp jp 3.18674 66.1487 0.86721 < 0.001 31.48100
## 40 jp jp 2.01056 60.1499 0.95925 < 0.001 29.06969
## 41 jp jp 4.85769 71.6354 0.67733 < 0.001 33.38888
## 42 jp jp 7.98770 57.7391 0.33368 < 0.001 24.87569
## 43 jp jp 3.39350 49.8337 0.84637 < 0.001 23.22012
## 44 jp jp 2.40691 74.7229 0.93394 < 0.001 36.15798
## 45 jp jp 9.94774 39.8191 0.19155 < 0.001 14.93569
## 46 jp jp 1.30546 57.9457 0.98829 < 0.001 28.32010
## 47 jp jp 3.07718 70.9482 0.87777 < 0.001 33.93552
## 48 jp jp 5.47720 52.7067 0.60193 < 0.001 23.61474
## 49 jp jp 4.95313 59.4451 0.66568 < 0.001 27.24600
## 50 jp jp 0.28617 66.0807 0.99991 < 0.001 32.89728
## 51 jp jp 0.79775 62.8164 0.99747 < 0.001 31.00931
## 52 jp jp 0.87571 66.0031 0.99659 < 0.001 32.56368
## 53 jp jp 3.76184 71.2543 0.80676 < 0.001 33.74625
## 54 jp jp 4.16676 54.7188 0.76038 < 0.001 25.27600
## 55 jp jp 7.80799 83.6733 0.34983 < 0.001 37.93265
## 56 jp jp 3.44858 75.7524 0.84064 < 0.001 36.15191
## 57 jp jp 6.51070 55.2279 0.48154 < 0.001 24.35859
## 58 jp jp 7.67342 71.1261 0.36227 < 0.001 31.72635
## 59 jp jp 6.68157 72.9831 0.46277 < 0.001 33.15077
## 60 jp jp 2.93487 58.9338 0.89096 < 0.001 27.99944
## 61 jp jp 2.24526 69.9482 0.94503 < 0.001 33.85149
## 62 jp jp 1.49622 57.1509 0.98244 < 0.001 27.82735
## 63 jp jp 2.04942 67.7546 0.95705 < 0.001 32.85259
## 64 jp jp 4.95370 72.2193 0.66561 < 0.001 33.63279
## 65 jp jp 14.48444 44.2696 0.04321 < 0.001 14.89260
## 66 jp jp 4.61044 73.4195 0.70738 < 0.001 34.40454
## 67 jp jp 3.83953 66.5136 0.79806 < 0.001 31.33701
## 68 jp jp 1.52805 66.7147 0.98132 < 0.001 32.59333
## 69 jp jp 2.51103 55.5677 0.92626 < 0.001 26.52833
## 70 jp jp 3.73122 73.1467 0.81016 < 0.001 34.70773
## 71 jp jp 5.55123 73.3754 0.59301 < 0.001 33.91207
## 72 jp jp 3.43980 53.8416 0.84156 < 0.001 25.20092
## 73 jp jp 10.61473 72.7303 0.15633 < 0.001 31.05778
## 74 jp jp 4.06275 70.0671 0.77252 < 0.001 33.00216
## 75 jp jp 4.99504 64.6800 0.66057 < 0.001 29.84249
## 76 jp jp 2.68070 60.1206 0.91289 < 0.001 28.71995
## 77 jp jp 2.03977 62.9175 0.95761 < 0.001 30.43886
## 78 jp jp 2.11330 62.8826 0.95330 < 0.001 30.38464
## 79 jp jp 2.88802 60.4300 0.89516 < 0.001 28.77099
## 80 jp jp 3.85108 77.1087 0.79676 < 0.001 36.62879
## 81 jp jp 21.98987 75.1780 0.00255 < 0.001 26.59405
## 82 jp jp 5.34943 77.1066 0.61740 < 0.001 35.87857
## 83 jp jp 15.05826 41.0451 0.03526 < 0.001 12.99344
## 84 jp jp 0.86243 63.6441 0.99675 < 0.001 31.39083
## 85 jp jp 5.87524 71.1833 0.55439 < 0.001 32.65405
## 86 jp jp 3.57291 56.0476 0.82744 < 0.001 26.23733
## 87 jp jp 2.93166 68.3431 0.89125 < 0.001 32.70573
## 88 jp jp 1.04592 64.9119 0.99405 < 0.001 31.93297
## 89 jp jp 3.22494 63.1349 0.86344 < 0.001 29.95500
## 90 jp jp 4.20609 57.2652 0.75576 < 0.001 26.52956
## 91 jp jp 4.08278 73.1550 0.77020 < 0.001 34.53613
## 92 jp jp 4.99927 77.1938 0.66005 < 0.001 36.09725
## 93 jp jp 7.65655 77.0673 0.36385 < 0.001 34.70537
## 94 jp jp 0.76340 64.6861 0.99780 < 0.001 31.96133
## 95 jp jp 1.06505 62.4546 0.99371 < 0.001 30.69478
## 96 jp jp 7.82840 75.4940 0.34797 < 0.001 33.83279
## 97 jp jp 1.44433 65.7126 0.98417 < 0.001 32.13412
## 98 jp jp 0.49844 65.9544 0.99945 < 0.001 32.72799
## 99 jp jp 3.49563 61.0968 0.83569 < 0.001 28.80061
## 100 jp jp 1.09226 68.2825 0.99320 < 0.001 33.59510
## 101 jp jp 3.68265 59.1882 0.81552 < 0.001 27.75276
## 102 jp jp 9.86225 66.1405 0.19651 < 0.001 28.13914
## 103 jp jp 1.79800 58.2808 0.97017 < 0.001 28.24140
## 104 jp jp 2.50086 60.8465 0.92703 < 0.001 29.17282
## 105 jp jp 5.60822 71.3459 0.58616 < 0.001 32.86884
## 106 jp jp 0.30116 65.1179 0.99990 < 0.001 32.40834
## 107 jp jp 3.03488 69.3705 0.88176 < 0.001 33.16779
## 108 jp jp 1.14442 55.6684 0.99215 < 0.001 27.26201
## 109 jp jp 2.93786 58.3995 0.89069 < 0.001 27.73082
## 110 jp jp 0.53678 62.0389 0.99930 < 0.001 30.75107
## 111 jp jp 2.14437 68.7008 0.95142 < 0.001 33.27823
## 112 jp jp 1.56358 69.8366 0.98003 < 0.001 34.13651
## 113 jp jp 1.18777 63.7736 0.99121 < 0.001 31.29292
## 114 jp jp 1.70786 61.6840 0.97423 < 0.001 29.98805
## 115 jp jp 1.59671 61.2762 0.97877 < 0.001 29.83973
## 116 jp jp 3.73687 58.9211 0.80954 < 0.001 27.59211
## 117 jp jp 8.17017 64.0236 0.31783 < 0.001 27.92673
## 118 jp jp 3.32001 60.6842 0.85391 < 0.001 28.68208
## 119 jp jp 1.48513 65.2295 0.98282 < 0.001 31.87219
## 120 jp jp 1.34201 68.6214 0.98728 < 0.001 33.63968
## 121 jp jp 5.70312 63.7230 0.57481 < 0.001 29.00993
## 122 jp jp 2.31923 61.3152 0.94008 < 0.001 29.49797
## 123 jp jp 5.36643 54.1301 0.61534 < 0.001 24.38182
## 124 jp jp 7.62912 68.3061 0.36643 < 0.001 30.33851
## 125 jp jp 4.61349 61.3641 0.70701 < 0.001 28.37531
## 126 jp jp 4.95451 58.8558 0.66552 < 0.001 26.95065
## 127 jp jp 1.98337 69.2272 0.96075 < 0.001 33.62190
## 128 jp jp 2.66756 60.4837 0.91396 < 0.001 28.90809
## 129 jp jp 5.54105 69.5037 0.59424 < 0.001 31.98132
## 130 jp jp 5.81922 65.2644 0.56101 < 0.001 29.72259
## 131 jp jp 4.69911 77.4774 0.69663 < 0.001 36.38913
## 132 jp jp 3.23433 74.4517 0.86251 < 0.001 35.60871
## 133 jp jp 3.87626 65.1482 0.79391 < 0.001 30.63597
## 134 jp jp 10.69348 69.0581 0.15256 < 0.001 29.18231
## 135 jp jp 2.17910 53.7453 0.94927 < 0.001 25.78312
## 136 jp jp 0.63292 64.2285 0.99880 < 0.001 31.79778
## 137 jp jp 3.69142 68.9107 0.81455 < 0.001 32.60962
## 138 jp jp 10.77207 70.5898 0.14887 < 0.001 29.90885
## 139 jp jp 8.67660 85.2215 0.27672 < 0.001 38.27247
## 140 jp jp 0.87845 61.7541 0.99656 < 0.001 30.43783
## 141 jp jp 0.47929 67.2749 0.99952 < 0.001 33.39781
## 142 jp jp 4.56095 71.4637 0.71337 < 0.001 33.45136
## 143 jp jp 2.81538 64.8003 0.90154 < 0.001 30.99246
## 144 jp jp 3.63874 62.3357 0.82032 < 0.001 29.34849
## 145 jp jp 4.02753 46.0637 0.77660 < 0.001 21.01808
## 146 jp jp 3.71625 66.4196 0.81182 < 0.001 31.35166
## 147 jp jp 1.11234 57.8460 0.99281 < 0.001 28.36685
## 148 jp jp 2.01336 61.4356 0.95910 < 0.001 29.71113
## 149 jp jp 3.96347 45.1500 0.78397 < 0.001 20.59325
## 150 jp jp 10.61166 35.7565 0.15648 < 0.001 12.57240
## 151 jp jp 2.98537 69.1636 0.88635 < 0.001 33.08910
## 152 jp jp 4.62496 50.5936 0.70562 < 0.001 22.98434
## 153 jp jp 6.87588 46.2325 0.44192 < 0.001 19.67830
## 154 jp jp 8.48664 43.4655 0.29164 < 0.001 17.48942
## 155 jp jp 3.48509 58.2197 0.83680 < 0.001 27.36730
## 156 jp jp 20.02481 78.5433 0.00552 < 0.001 29.25927
## 157 jp jp 11.61049 30.8805 0.11412 < 0.001 9.63501
## 158 jp jp 2.68798 64.6232 0.91229 < 0.001 30.96759
## 159 jp jp 4.94354 39.4716 0.66685 < 0.001 17.26405
## 160 jp jp 2.60020 52.6619 0.91937 < 0.001 25.03086
## 161 jp jp 32.45336 51.3945 < 0.001 < 0.001 9.47058
## 162 jp jp 3.51376 58.7884 0.83377 < 0.001 27.63733
## 163 jp jp 1.24796 62.8253 0.98978 < 0.001 30.78869
## 164 jp jp 1.74754 68.2735 0.97248 < 0.001 33.26299
## 165 jp jp 60.89939 107.2264 < 0.001 < 0.001 23.16351
## 166 jp jp 2.65434 56.6061 0.91503 < 0.001 26.97586
## 167 jp jp 1.89131 55.2305 0.96561 < 0.001 26.66958
## 168 jp jp 12.93237 62.7287 0.07377 < 0.001 24.89819
## 169 jp jp 0.54708 61.9493 0.99926 < 0.001 30.70111
## 170 jp jp 4.22310 56.9671 0.75375 < 0.001 26.37199
## 171 jp jp 4.42080 60.4598 0.73023 < 0.001 28.01949
## 172 jp jp 9.41347 45.5252 0.22432 < 0.001 18.05588
## 173 jp jp 5.01431 67.1602 0.65822 < 0.001 31.07293
## 174 jp jp 0.17554 63.2129 0.99998 < 0.001 31.51870
## 175 jp jp 9.04003 66.1928 0.24979 < 0.001 28.57637
## 176 jp jp 3.98504 60.9707 0.78150 < 0.001 28.49284
## 177 jp jp 3.71047 49.5109 0.81246 < 0.001 22.90021
## 178 jp jp 7.59977 43.6676 0.36920 < 0.001 18.03391
## 179 jp jp 13.44243 76.0387 0.06203 < 0.001 31.29814
## 180 jp jp 6.58813 42.4432 0.47298 < 0.001 17.92756
## 181 jp jp 11.53015 35.2462 0.11711 < 0.001 11.85804
## 182 jp jp 5.09433 55.1513 0.64845 < 0.001 25.02849
## 183 jp jp 10.81244 59.0270 0.14701 < 0.001 24.10729
## 184 jp jp 5.04838 52.7598 0.65406 < 0.001 23.85571
## 185 jp jp 1.10516 63.7065 0.99295 < 0.001 31.30068
## 186 jp jp 3.03847 49.3662 0.88142 < 0.001 23.16385
## 187 jp jp 3.61587 59.9971 0.82280 < 0.001 28.19060
## 188 jp jp 1.86599 60.7243 0.96688 < 0.001 29.42914
## 189 jp jp 4.96522 41.4284 0.66421 < 0.001 18.23158
## 190 jp jp 5.49942 66.0897 0.59925 < 0.001 30.29517
## 191 jp jp 3.94616 73.2506 0.78596 < 0.001 34.65222
## 192 jp jp 7.77150 40.2626 0.35317 < 0.001 16.24557
## 193 jp jp 7.13958 76.3078 0.41449 < 0.001 34.58411
## 194 jp jp 3.23514 72.5522 0.86243 < 0.001 34.65855
## 195 jp jp 4.07622 48.5878 0.77096 < 0.001 22.25581
## 196 jp jp 38.63377 115.9753 < 0.001 < 0.001 38.67076
## 197 jp jp 5.49473 70.5889 0.59982 < 0.001 32.54706
## 198 jp jp 8.97951 64.3384 0.25413 < 0.001 27.67943
## 199 jp jp 1.83277 56.3319 0.96851 < 0.001 27.24954
## 200 jp jp 16.97447 61.2957 0.01756 < 0.001 22.16061
## 201 jp jp 4.73203 49.1080 0.69263 < 0.001 22.18796
## 202 jp jp 6.72956 69.2261 0.45757 < 0.001 31.24828
## 203 jp jp 3.47706 66.5622 0.83765 < 0.001 31.54257
## 204 jp jp 1.70556 59.1839 0.97433 < 0.001 28.73919
## 205 jp jp 3.73453 56.9739 0.80980 < 0.001 26.61968
## 206 jp jp 2.80399 63.3211 0.90252 < 0.001 30.25858
## 207 jp jp 2.91606 58.9599 0.89266 < 0.001 28.02191
## 208 jp jp 3.16900 63.7419 0.86894 < 0.001 30.28644
## 209 jp jp 1.70485 68.4996 0.97436 < 0.001 33.39736
## 210 jp jp 1.85658 62.5881 0.96735 < 0.001 30.36574
## 211 jp jp 5.78842 60.3950 0.56466 < 0.001 27.30330
## 212 jp jp 9.96377 81.0928 0.19064 < 0.001 35.56453
## 213 jp jp 4.77488 76.9182 0.68741 < 0.001 36.07165
## 214 jp jp 2.67083 54.9042 0.91369 < 0.001 26.11668
## 215 jp jp 3.15454 56.9436 0.87035 < 0.001 26.89452
## 216 jp jp 2.88059 64.6074 0.89582 < 0.001 30.86338
## 217 jp jp 6.31785 59.1536 0.50316 < 0.001 26.41790
## 218 jp jp 1.01671 64.6049 0.99455 < 0.001 31.79411
## 219 jp jp 30.46002 84.7678 < 0.001 < 0.001 27.15387
## 220 jp jp 1.65464 68.1206 0.97647 < 0.001 33.23295
## 221 jp jp 7.59382 72.3306 0.36977 < 0.001 32.36837
## 222 jp jp 1.20913 67.2379 0.99072 < 0.001 33.01436
## 223 jp jp 6.25501 53.3845 0.51031 < 0.001 23.56473
## 224 jp jp 3.50593 71.1331 0.83460 < 0.001 33.81358
## 225 jp jp 2.71770 70.8905 0.90983 < 0.001 34.08639
## 226 jp jp 3.09439 55.2450 0.87614 < 0.001 26.07533
## 227 jp jp 5.57337 64.4402 0.59035 < 0.001 29.43340
## 228 jp jp 3.95164 67.2242 0.78533 < 0.001 31.63627
## 229 jp jp 1.73137 55.4357 0.97320 < 0.001 26.85214
## 230 jp jp 13.76935 77.8320 0.05544 < 0.001 32.03134
## 231 jp jp 10.11806 63.1450 0.18198 < 0.001 26.51348
## 232 jp jp 2.48423 65.5481 0.92828 < 0.001 31.53193
## 233 jp jp 5.93281 66.1735 0.54762 < 0.001 30.12036
## 234 jp jp 4.03932 70.3538 0.77524 < 0.001 33.15724
## 235 jp jp 4.92104 69.1314 0.66960 < 0.001 32.10519
## 236 jp jp 2.13100 65.6594 0.95223 < 0.001 31.76418
## 237 jp jp 4.62699 77.1515 0.70538 < 0.001 36.26226
## 238 jp jp 1.57173 70.7889 0.97972 < 0.001 34.60857
## 239 jp jp 3.02294 61.9404 0.88287 < 0.001 29.45872
## 240 jp jp 5.39830 71.2402 0.61148 < 0.001 32.92095
## 241 jp jp 6.55110 71.4639 0.47706 < 0.001 32.45639
## 242 jp jp 2.08868 70.6929 0.95477 < 0.001 34.30211
## 243 jp jp 2.60962 71.2889 0.91862 < 0.001 34.33963
## 244 jp jp 2.56250 73.6223 0.92232 < 0.001 35.52991
## 245 jp jp 2.14989 70.4701 0.95108 < 0.001 34.16010
## 246 jp jp 9.43014 66.3814 0.22323 < 0.001 28.47563
## 247 jp jp 0.80491 65.5698 0.99739 < 0.001 32.38242
## 248 jp jp 1.96672 66.1245 0.96166 < 0.001 32.07890
## 249 jp jp 2.61520 68.5901 0.91818 < 0.001 32.98744
## 250 jp jp 1.13475 61.4757 0.99235 < 0.001 30.17046
## 251 jp jp 5.73059 71.4729 0.57154 < 0.001 32.87114
## 252 jp jp 4.13864 69.3349 0.76368 < 0.001 32.59812
## 253 jp jp 8.96063 84.3588 0.25550 < 0.001 37.69908
## 254 jp jp 4.43597 66.0655 0.72841 < 0.001 30.81476
## 255 jp jp 0.44148 62.4825 0.99963 < 0.001 31.02052
## 256 jp jp 3.13333 56.6465 0.87240 < 0.001 26.75659
## 257 jp jp 7.92973 80.0291 0.33883 < 0.001 36.04969
## 258 jp jp 12.62851 71.7316 0.08169 < 0.001 29.55153
## 259 jp jp 2.06213 65.8289 0.95632 < 0.001 31.88337
## 260 jp jp 10.37261 69.7682 0.16842 < 0.001 29.69779
## 261 jp jp 4.79761 73.6896 0.68465 < 0.001 34.44600
## 262 jp jp 2.60205 70.2760 0.91922 < 0.001 33.83699
## 263 jp jp 2.29765 58.5680 0.94155 < 0.001 28.13518
## 264 jp jp 1.89064 66.6644 0.96564 < 0.001 32.38691
## 265 jp jp 9.18588 68.3800 0.23958 < 0.001 29.59707
## 266 jp jp 3.64336 71.0642 0.81982 < 0.001 33.71044
## 267 jp jp 11.16624 70.6035 0.13153 < 0.001 29.71864
## 268 jp jp 2.73981 69.3965 0.90798 < 0.001 33.32835
## 269 jp jp 2.05151 66.1950 0.95693 < 0.001 32.07174
## 270 jp jp 3.25949 66.6355 0.86000 < 0.001 31.68802
## 271 jp jp 3.17984 66.5237 0.86788 < 0.001 31.67194
## 272 jp jp 1.61086 69.2324 0.97822 < 0.001 33.81076
## 273 jp jp 0.51164 65.2375 0.99940 < 0.001 32.36294
## 274 jp jp 2.18695 64.7835 0.94877 < 0.001 31.29829
## 275 jp jp 1.71731 67.5478 0.97382 < 0.001 32.91526
## 276 jp jp 1.59555 67.1420 0.97882 < 0.001 32.77320
## 277 jp jp 13.18949 85.8709 0.06762 < 0.001 36.34072
## 278 jp jp 2.57705 70.3754 0.92118 < 0.001 33.89919
## 279 jp jp 0.69482 62.8014 0.99838 < 0.001 31.05327
## 280 jp jp 1.67675 67.2876 0.97555 < 0.001 32.80544
## 281 jp jp 15.57579 91.5792 0.02929 < 0.001 38.00173
## 282 jp jp 3.50080 69.2874 0.83514 < 0.001 32.89329
## 283 jp jp 2.81785 69.1212 0.90133 < 0.001 33.15167
## 284 jp jp 4.39722 58.9659 0.73306 < 0.001 27.28435
## 285 jp jp 8.05457 74.3996 0.32781 < 0.001 33.17249
## 286 jp jp 6.83667 69.0437 0.44608 < 0.001 31.10353
## 287 jp jp 7.24557 69.7193 0.40377 < 0.001 31.23686
## 288 jp jp 7.45572 69.4486 0.38302 < 0.001 30.99645
## 289 jp jp 11.85567 74.2967 0.10541 < 0.001 31.22050
## 290 jp jp 2.91998 62.8345 0.89230 < 0.001 29.95724
## 291 jp jp 7.44140 72.0242 0.38441 < 0.001 32.29142
## 292 jp jp 5.30866 70.0730 0.62235 < 0.001 32.38217
## 293 jp jp 2.06137 63.5894 0.95637 < 0.001 30.76399
## 294 jp jp 1.81459 65.3929 0.96939 < 0.001 31.78915
## 295 jp jp 6.12707 79.4231 0.52499 < 0.001 36.64801
## 296 jp jp 1.07810 68.1293 0.99347 < 0.001 33.52559
## 297 jp jp 10.23900 74.0356 0.17543 < 0.001 31.89829
## 298 jp jp 4.17265 60.2895 0.75969 < 0.001 28.05844
## 299 jp jp 3.72382 67.0447 0.81098 < 0.001 31.66044
## 300 jp jp 4.87415 59.2097 0.67532 < 0.001 27.16778
## 301 jp jp 1.89966 63.4126 0.96518 < 0.001 30.75649
## 302 jp jp 2.96312 53.9298 0.88839 < 0.001 25.48336
## 303 jp jp 18.25103 82.4337 0.01089 < 0.001 32.09134
## 304 jp jp 8.91696 73.2335 0.25867 < 0.001 32.15829
## 305 jp jp 5.81515 59.0728 0.56149 < 0.001 26.62883
## 306 jp jp 6.69683 82.4467 0.46111 < 0.001 37.87494
## 307 jp jp 3.26086 67.1248 0.85987 < 0.001 31.93198
## 308 jp jp 0.76692 62.5648 0.99777 < 0.001 30.89892
## 309 jp jp 1.51818 67.0843 0.98167 < 0.001 32.78305
## 310 jp jp 4.03862 61.6246 0.77532 < 0.001 28.79301
## 311 jp jp 5.47508 65.6695 0.60219 < 0.001 30.09723
## 312 jp jp 0.96313 67.2862 0.99540 < 0.001 33.16153
## 313 jp jp 3.98942 71.6360 0.78100 < 0.001 33.82328
## 314 jp jp 5.80636 62.8449 0.56253 < 0.001 28.51925
## 315 jp jp 1.33962 62.7011 0.98735 < 0.001 30.68076
## 316 jp jp 3.15099 55.7818 0.87069 < 0.001 26.31543
## 317 jp jp 1.53179 64.5428 0.98119 < 0.001 31.50549
## 318 jp jp 11.22267 74.5766 0.12920 < 0.001 31.67695
## 319 jp jp 2.11990 66.6434 0.95291 < 0.001 32.26176
## 320 jp jp 3.85583 56.7602 0.79623 < 0.001 26.45218
## 321 jp jp 4.75546 62.7362 0.68978 < 0.001 28.99036
## 322 jp jp 2.08645 68.2781 0.95490 < 0.001 33.09581
## 323 jp jp 4.73278 63.2235 0.69254 < 0.001 29.24535
## 324 jp jp 11.95679 72.8407 0.10199 < 0.001 30.44196
## 325 jp jp 4.79852 42.9316 0.68454 < 0.001 19.06656
## 326 jp jp 1.91147 70.4461 0.96458 < 0.001 34.26731
## 327 jp jp 1.13422 64.6247 0.99236 < 0.001 31.74524
## 328 jp jp 2.37562 59.9542 0.93617 < 0.001 28.78931
## 329 jp jp 1.66234 64.5903 0.97615 < 0.001 31.46400
## 330 jp jp 3.64585 65.0629 0.81954 < 0.001 30.70850
## 331 jp jp 4.46814 55.2008 0.72455 < 0.001 25.36634
## 332 jp jp 2.21002 72.6633 0.94731 < 0.001 35.22662
## 333 jp jp 4.17719 66.1231 0.75916 < 0.001 30.97297
## 334 jp jp 0.39676 63.7900 0.99974 < 0.001 31.69661
## 335 jp jp 1.21469 71.1174 0.99058 < 0.001 34.95136
## 336 jp jp 3.64065 73.4451 0.82011 < 0.001 34.90223
## 337 jp jp 1.18414 68.0674 0.99129 < 0.001 33.44164
## 338 jp jp 9.75170 73.5198 0.20309 < 0.001 31.88406
## 339 jp jp 3.26353 62.0333 0.85960 < 0.001 29.38490
## 340 jp jp 1.49697 70.2597 0.98241 < 0.001 34.38134
## 341 jp jp 9.21878 75.2696 0.23733 < 0.001 33.02540
## 342 jp jp 5.07980 68.4644 0.65023 < 0.001 31.69230
## 343 jp jp 2.86532 72.5494 0.89718 < 0.001 34.84204
## 344 jp jp 1.75244 68.0669 0.97226 < 0.001 33.15724
## 345 jp jp 1.46317 61.9514 0.98356 < 0.001 30.24413
## 346 jp jp 0.41849 65.4408 0.99969 < 0.001 32.51116
## 347 jp jp 3.87264 70.5829 0.79432 < 0.001 33.35515
## 348 jp jp 4.65056 69.9848 0.70252 < 0.001 32.66710
## 349 jp jp 2.58110 69.8561 0.92087 < 0.001 33.63747
## 350 jp jp 3.48448 67.0185 0.83687 < 0.001 31.76701
## 351 jp jp 14.82116 74.3401 0.03836 < 0.001 29.75945
## 352 jp jp 1.73714 69.1351 0.97295 < 0.001 33.69900
## 353 jp jp 8.41244 67.2165 0.29763 < 0.001 29.40201
## 354 jp jp 0.30407 66.6795 0.99990 < 0.001 33.18774
## 355 jp jp 2.71822 65.0202 0.90979 < 0.001 31.15097
## 356 jp jp 1.87052 70.8086 0.96666 < 0.001 34.46902
## 357 jp jp 1.16513 64.8422 0.99171 < 0.001 31.83854
## 358 jp jp 2.37014 62.0251 0.93655 < 0.001 29.82746
## 359 jp jp 1.06416 63.7381 0.99373 < 0.001 31.33697
## 360 jp jp 2.55697 67.1075 0.92275 < 0.001 32.27525
## 361 jp jp 7.23564 55.8686 0.40476 < 0.001 24.31648
## 362 jp jp 8.12141 72.2567 0.32201 < 0.001 32.06767
## 363 jp jp 2.42387 59.1680 0.93272 < 0.001 28.37207
## 364 jp jp 3.65768 63.3839 0.81825 < 0.001 29.86314
## 365 jp jp 5.51757 77.3995 0.59706 < 0.001 35.94098
## 366 jp jp 6.05112 71.8419 0.53379 < 0.001 32.89541
## 367 jp jp 2.33165 70.9241 0.93923 < 0.001 34.29622
## 368 jp jp 4.46545 68.4272 0.72487 < 0.001 31.98085
## 369 jp jp 4.50094 72.9520 0.72060 < 0.001 34.22555
## 370 jp jp 4.19156 60.6335 0.75747 < 0.001 28.22097
## 371 jp jp 2.33163 73.8226 0.93923 < 0.001 35.74548
## 372 jp jp 2.82018 66.3095 0.90112 < 0.001 31.74468
## 373 jp jp 5.30750 70.4822 0.62249 < 0.001 32.58737
## 374 jp jp 15.07530 72.4523 0.03505 < 0.001 28.68848
## 375 jp jp 2.42401 61.6870 0.93271 < 0.001 29.63150
## 376 jp jp 1.61384 65.7656 0.97810 < 0.001 32.07590
## 377 jp jp 2.66203 70.8877 0.91441 < 0.001 34.11285
## 378 jp jp 4.11137 67.4484 0.76686 < 0.001 31.66849
## 379 jp jp 10.71944 66.6832 0.15133 < 0.001 27.98187
## 380 ns ns 74.57944 10.2497 < 0.001 0.17486 -32.16488
## 381 ns ns 77.64736 18.1746 < 0.001 0.01121 -29.73637
## 382 ns ns 102.97902 15.1873 < 0.001 0.03367 -43.89585
## 383 ns ns 71.92564 5.4456 < 0.001 0.60576 -33.24003
## 384 ns ns 106.66676 8.2155 < 0.001 0.31397 -49.22562
## 385 ns ns 69.58847 6.1129 < 0.001 0.52663 -31.73778
## 386 ns ns 23.95706 13.7432 0.00116 0.05594 -5.10694
## 387 ns ns 55.36457 6.4134 < 0.001 0.49239 -24.47559
## 388 ns ns 65.01989 16.0836 < 0.001 0.02436 -24.46813
## 389 ns ns 83.01501 4.4811 < 0.001 0.72300 -39.26698
## 390 ns ns 21.14465 20.4623 0.00356 0.00465 -0.34117
## 391 ns jp ## 12.76517 24.5880 0.07804 < 0.001 5.91140
## 392 ns ns 83.78408 6.1214 < 0.001 0.52565 -38.83136
## 393 ns ns 100.32246 9.1524 < 0.001 0.24190 -45.58503
## 394 ns ns 38.61953 5.3651 < 0.001 0.61550 -16.62720
## 395 ns ns 132.80297 17.8028 < 0.001 0.01289 -57.50007
## 396 ns ns 105.99366 7.8460 < 0.001 0.34637 -49.07385
## 397 ns ns 130.72570 18.9473 < 0.001 0.00835 -55.88919
## 398 ns ns 222.25878 67.5211 < 0.001 < 0.001 -77.36885
## 399 ns ns 60.22507 1.9892 < 0.001 0.96044 -29.11796
## 400 ns ns 81.17667 49.4976 < 0.001 < 0.001 -15.83952
## 401 ns ns 70.57428 5.5030 < 0.001 0.59882 -32.53563
## 402 ns ns 46.13059 4.5090 < 0.001 0.71963 -20.81078
## 403 ns ns 48.24436 7.6496 < 0.001 0.36451 -20.29739
## 404 ns jp ## 15.84109 23.9875 0.02661 0.00115 4.07321
## 405 ns ns 34.82940 11.3377 < 0.001 0.12455 -11.74587
## 406 ns ns 37.72088 17.1695 < 0.001 0.01634 -10.27567
## 407 ns ns 22.30536 19.0951 0.00225 0.00789 -1.60512
## 408 ns jp ## 14.19413 30.6996 0.04783 < 0.001 8.25271
## 409 ns ns 54.63084 4.0581 < 0.001 0.77306 -25.28637
## 410 ns ns 61.81403 33.1329 < 0.001 < 0.001 -14.34057
## 411 ns ns 95.86215 8.6988 < 0.001 0.27501 -43.58168
## 412 ns ns 68.20597 6.4305 < 0.001 0.49048 -30.88774
## 413 ns ns 93.37849 14.0379 < 0.001 0.05051 -39.67032
## 414 ns ns 94.86852 25.3368 < 0.001 < 0.001 -34.76587
## 415 ns ns 121.12470 17.2749 < 0.001 0.01571 -51.92492
## 416 ns ns 172.34913 46.9084 < 0.001 < 0.001 -62.72038
## 417 ns ns 29.72343 17.7329 < 0.001 0.01324 -5.99529
## 418 ns ns 168.48070 35.5835 < 0.001 < 0.001 -66.44862
## 419 ns ns 111.45508 10.4614 < 0.001 0.16390 -50.49685
##
## $判別結果集計表
## 判別された群
## 実際の群 jp ns
## jp 379 0
## ns 3 37
##
## attr(,"class")
## [1] "sdis" "list"
plot(Index.sdis)
plot(Index.sdis, which="scatterplot", xpos="topright")
library(ggplot2)
str(Index.dat)
## 'data.frame': 419 obs. of 11 variables:
## $ ID : Factor w/ 452 levels "JPN501.txt","JPN502.txt",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Topic: Factor w/ 3 levels "education","money",..: 3 1 1 3 3 2 1 3 3 1 ...
## $ Score: int 4 4 3 4 4 3 4 3 4 3 ...
## $ Token: int 319 351 201 260 417 260 355 195 260 183 ...
## $ Type : int 134 158 121 139 174 123 149 97 103 99 ...
## $ TTR : num 0.42 0.45 0.602 0.535 0.417 ...
## $ GI : num 7.5 8.43 8.53 8.62 8.52 ...
## $ NoS : int 30 29 13 27 25 20 26 20 19 14 ...
## $ ASL : num 10.63 12.1 15.46 9.63 16.68 ...
## $ AWL : num 4.3 4.29 4.75 4.77 4.02 ...
## $ Lang : Factor w/ 2 levels "jp","ns": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "na.action")= 'omit' Named int [1:2] 83 159
## ..- attr(*, "names")= chr [1:2] "83" "159"
g <- ggplot(Index.dat)
g <- g + aes(x=Lang, y=Token)
g <- g + geom_boxplot()
plot(g)
g <- ggplot(Index.dat)
g <- g + aes(x=Type, y=Token)
g <- g + geom_point()
plot(g)
g <- ggplot(Index.dat)
g <- g + aes(x=Type, y=Token, color=Lang)
g <- g + geom_point()
plot(g)
g <- ggplot(Index.dat)
g <- g + aes(x=Token, fill=Lang)
g <- g + geom_histogram()
plot(g)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
g <- ggplot(Index.dat)
g <- g + aes(x=Token, color=Lang)
g <- g + geom_density()
plot(g)
* 母語話者データの山がヒストグラム(絶対的な頻度)に比べ高くなっている(相対的な頻度)点に注意 * 分布の全体像が把握しやすい
g <- ggplot(Index.dat)
g <- g + aes(x=Token, fill=Lang)
g <- g + geom_density(alpha = 0.7)
plot(g)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::select() masks MASS::select()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
head(Index.dat)
## ID Topic Score Token Type TTR GI NoS ASL
## 1 JPN501.txt sports 4 319 134 0.4200627 7.502560 30 10.63333
## 2 JPN502.txt education 4 351 158 0.4501425 8.433416 29 12.10345
## 3 JPN503.txt education 3 201 121 0.6019900 8.534682 13 15.46154
## 4 JPN504.txt sports 4 260 139 0.5346154 8.620414 27 9.62963
## 5 JPN505.txt sports 4 417 174 0.4172662 8.520817 25 16.68000
## 6 JPN506.txt money 3 260 123 0.4730769 7.628136 20 13.00000
## AWL Lang
## 1 4.304075 jp
## 2 4.293447 jp
## 3 4.746269 jp
## 4 4.765385 jp
## 5 4.023981 jp
## 6 4.088462 jp
一つのサンプルについて複数の観点から観測値を取った場合、サンプル名は観測値の数だけ同じものが並ぶ
wide formatのデータをlong formatに変換する命令: pivot_longer
* 個々の見出しが nameにまとめられ、数値は valueという見出しになる
* name にまとめないものは cols=!c(見出し, 見出し) という形で除いておく
* それらは、重複して並べられることになる
Index.dat %>% pivot_longer(cols=!c(ID, Topic, Lang)) %>% head()
## # A tibble: 6 × 5
## ID Topic Lang name value
## <fct> <fct> <fct> <chr> <dbl>
## 1 JPN501.txt sports jp Score 4
## 2 JPN501.txt sports jp Token 319
## 3 JPN501.txt sports jp Type 134
## 4 JPN501.txt sports jp TTR 0.420
## 5 JPN501.txt sports jp GI 7.50
## 6 JPN501.txt sports jp NoS 30
Index.dat.long <- Index.dat %>% pivot_longer(cols=!c(ID, Topic, Lang))
str(Index.dat.long)
## tibble [3,352 × 5] (S3: tbl_df/tbl/data.frame)
## $ ID : Factor w/ 452 levels "JPN501.txt","JPN502.txt",..: 1 1 1 1 1 1 1 1 2 2 ...
## $ Topic: Factor w/ 3 levels "education","money",..: 3 3 3 3 3 3 3 3 1 1 ...
## $ Lang : Factor w/ 2 levels "jp","ns": 1 1 1 1 1 1 1 1 1 1 ...
## $ name : chr [1:3352] "Score" "Token" "Type" "TTR" ...
## $ value: num [1:3352] 4 319 134 0.42 7.5 ...
g <- ggplot(Index.dat)
g <- g + aes(x=Lang, y=Token)
g <- g + geom_boxplot()
plot(g)
g <- ggplot(Index.dat.long)
g <- g + aes(x=Lang, y=value)
g <- g + geom_boxplot()
g <- g + facet_wrap(~name, scales="free")
plot(g)
g <- ggplot(Index.dat.long)
g <- g + aes(x=Lang, y=value, fill=Lang)
g <- g + geom_boxplot()
g <- g + facet_wrap(~name, scales="free")
plot(g)
g <- ggplot(Index.dat.long)
g <- g + aes(x=value, fill=Lang)
g <- g + geom_density(alpha=.7)
g <- g + facet_wrap(~name, scales="free")
plot(g)