taitanic 562118
kagaya fumitaka
2019/12/24-2
始めに文法の基本：
- “文頭に#+スペースで大みだしができる。”
- “以降を箇条書きで書いていく場合は、ここに書いたとおりスペース+”-“+スペースで書ける”
- このローカルデータは、“C:\721540.—Rmd”であり
- webには、http://rpubs.com/fumi/562118
- バックアップとして、上記のデータのダウンロードを C:\721540
r基本マニュアル https://cran.r-project.org/doc/contrib/manuals-jp/Mase-Rstatman.pdf
##http://zeema.hatenablog.com/entry/2017/09/04/003400#%E3%83%87%E3%83%BC%E3%82%BF%E3%81%AE%E8%AA%AD%E3%81%BF%E8%BE%BC%E3%81%BF ##上記はrをベースに記述されているため、console画面で、本画面でエラーになったalldata <- bind_rows(train,test) ##のbind_rows(train,test)について以下のとおり実行する ##> test <- read.csv(test_path, stringsAsFactors = F,na.strings=(c(“NA”, "“))) ## read.table(file = file, header = header, sep = sep, quote = quote, でエラー: ## オブジェクト ‘test_path’ がありません ##> test_path <-”C:/Users/721540/Documents/practice/test.csv" ##> test <- read.csv(test_path, stringsAsFactors = F,na.strings=(c(“NA”, "“))) ##> alldata <- bind_rows(train,test) ## エラー: Argument 1 must be a data fra ##> train_path <-”C:/Users/721540/Documents/practice/train.csv" ##> train <- read.csv(train_path, stringsAsFactors = F,na.strings=(c(“NA”, ""))) ##> alldata <- bind_rows(train,test) ##> glimpse(alldata) ##そうするとｒのコンソール上では、上記のテキストコマンドが実行し成功していることが確認できる ##Observations: 1,309 ##Variables: 12 ##$ PassengerId
##変数dftとはデータフレームのこと ##このデータは1行目が列名になっていることと、１列目が行番号になっていることに注意して、header=Tとrow.names=1を指定##します。 ##プログラム ##df <- read.csv(“sample-data.csv”,header=T,row.names=1)
##ロジスティック回帰分析に使うデータはdfの5,1,2,6列目になるので、以下のようなプログラムで解析に使う部分だけを抽出 ##して変数datに代入します。 ##プログラム ##dat <- df[,c(5,1,2,6)]#５列が目的変数。1,2,6列目が説明変数。 ———————————————————– 参考サイト 全般的な考え方：Pclassについて：http://kefism.hatenablog.com/entry/2017/04/22/203740
library(dummies)## dummies-1.5.6 provided by Decision Patternslibrary(data.table)## Warning: package 'data.table' was built under R version 3.6.2library(tidyr)
library(ranger)## Warning: package 'ranger' was built under R version 3.6.2library(xtable)
library(nnet)## Warning: package 'nnet' was built under R version 3.6.2library(e1071)## Warning: package 'e1071' was built under R version 3.6.2library(epitools)
library(car)## Loading required package: carDatalibrary(caret)## Warning: package 'caret' was built under R version 3.6.2## Loading required package: latticelibrary(ggplot2)
library(ggthemes)## Warning: package 'ggthemes' was built under R version 3.6.2library(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ranger':
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## importance## The following object is masked from 'package:ggplot2':
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## marginlibrary(rgl)
library(rattle)## Warning: package 'rattle' was built under R version 3.6.2## Rattle: A free graphical interface for data science with R.
## バージョン 5.3.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
## 'rattle()' と入力して、データを多角的に分析します。##
## Attaching package: 'rattle'## The following object is masked from 'package:randomForest':
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## importance## The following object is masked from 'package:ranger':
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## importancelibrary(rpart.plot)## Warning: package 'rpart.plot' was built under R version 3.6.2## Loading required package: rpartlibrary(rpart)
library(epitools)
library(caret)
library(ggthemes)
library(readr)## Warning: package 'readr' was built under R version 3.6.2library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:randomForest':
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## combine## The following object is masked from 'package:car':
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## recode## The following objects are masked from 'package:data.table':
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## between, first, last## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, uniongetwd()## [1] "C:/Users/721540/Documents/practice"#table 関数機能確認
set.seed(20120508)
x <- sample(letters[1:3], 300, replace = T)
y <- sample(letters[4:5], 300, replace = T)
z <- sample(letters[6:7], 300, replace = T)
t1 <- table(z, x, y)
ftable(t1)## y d e
## z x
## f a 26 28
## b 28 22
## c 24 19
## g a 23 25
## b 32 28
## c 27 18##書き換え「 train_path <- ‘C:\Users\admin\kaggle\Titanic\train.csv’」→「train_path <- “C:/Users/721540/Documents/practice/train.csv”」
# load data
train_path <- "C:/Users/721540/Documents/practice/train.csv"
test_path <- "C:/Users/721540/Documents/practice/test.csv"
train <- read.csv(train_path, stringsAsFactors = F,na.strings=(c("NA", "")))
test <- read.csv(test_path, stringsAsFactors = F,na.strings=(c("NA", "")))
alldata <- bind_rows(train,test)glimpse(alldata)## Observations: 1,309
## Variables: 12
## $ PassengerId <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, ...
## $ Survived <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0...
## $ Pclass <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3...
## $ Name <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley ...
## $ Sex <chr> "male", "female", "female", "female", "male", "male", "...
## $ Age <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, 39, 1...
## $ SibSp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1...
## $ Parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0...
## $ Ticket <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", ...
## $ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.86...
## $ Cabin <chr> NA, "C85", NA, "C123", NA, NA, "E46", NA, NA, NA, "G6",...
## $ Embarked <chr> "S", "C", "S", "S", "S", "Q", "S", "S", "S", "C", "S", ...summary(alldata)## PassengerId Survived Pclass Name
## Min. : 1 Min. :0.0000 Min. :1.000 Length:1309
## 1st Qu.: 328 1st Qu.:0.0000 1st Qu.:2.000 Class :character
## Median : 655 Median :0.0000 Median :3.000 Mode :character
## Mean : 655 Mean :0.3838 Mean :2.295
## 3rd Qu.: 982 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :1309 Max. :1.0000 Max. :3.000
## NA's :418
## Sex Age SibSp Parch
## Length:1309 Min. : 0.17 Min. :0.0000 Min. :0.000
## Class :character 1st Qu.:21.00 1st Qu.:0.0000 1st Qu.:0.000
## Mode :character Median :28.00 Median :0.0000 Median :0.000
## Mean :29.88 Mean :0.4989 Mean :0.385
## 3rd Qu.:39.00 3rd Qu.:1.0000 3rd Qu.:0.000
## Max. :80.00 Max. :8.0000 Max. :9.000
## NA's :263
## Ticket Fare Cabin Embarked
## Length:1309 Min. : 0.000 Length:1309 Length:1309
## Class :character 1st Qu.: 7.896 Class :character Class :character
## Mode :character Median : 14.454 Mode :character Mode :character
## Mean : 33.295
## 3rd Qu.: 31.275
## Max. :512.329
## NA's :1head(alldata)## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## 6 6 0 3
## Name Sex Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## 6 Moran, Mr. James male NA 0 0
## Ticket Fare Cabin Embarked
## 1 A/5 21171 7.2500 <NA> S
## 2 PC 17599 71.2833 C85 C
## 3 STON/O2. 3101282 7.9250 <NA> S
## 4 113803 53.1000 C123 S
## 5 373450 8.0500 <NA> S
## 6 330877 8.4583 <NA> Q#'Pclass', 'Sex', 'Survived'をファクター型へ変換する
train$Pclass <- as.factor(train$Pclass)
train$Sex <- as.factor(train$Sex)
train$Survived <- factor(train$Survived,levels=c(0,1),labels=c("Died","Survived"))# Survived × Pclass
SP <- table(train$Survived, train$Pclass)
print(SP) # クロス集計表##
## 1 2 3
## Died 80 97 372
## Survived 136 87 119# Survived × Age
ggplot(train, aes(Age, fill = Survived)) +
geom_histogram() +
theme_igray() +
xlab("Age") +
scale_fill_discrete(name = "Survived") +
ggtitle("Age vs Survived")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 177 rows containing non-finite values (stat_bin).
# SibSp
table(train$Survived, train$SibSp)##
## 0 1 2 3 4 5 8
## Died 398 97 15 12 15 5 7
## Survived 210 112 13 4 3 0 0round(prop.table(table(train$Survived,train$SibSp),2),digit=2) ##
## 0 1 2 3 4 5 8
## Died 0.65 0.46 0.54 0.75 0.83 1.00 1.00
## Survived 0.35 0.54 0.46 0.25 0.17 0.00 0.00##乗船している配偶者（夫、妻）と兄弟姉妹の数は1人のときに生存率が最も高くなりその後減少する
# Parch
table(train$Survived, train$Parch)##
## 0 1 2 3 4 5 6
## Died 445 53 40 2 4 4 1
## Survived 233 65 40 3 0 1 0##乗船している両親（父、母）と子供の数が0人のとき生存率が低く，1人から3人では生存率が高くなり，4人以上だとまた低くなっている．
round(prop.table(table(train$Survived, train$Parch),2),digit=2)##
## 0 1 2 3 4 5 6
## Died 0.66 0.45 0.50 0.40 1.00 0.80 1.00
## Survived 0.34 0.55 0.50 0.60 0.00 0.20 0.00Family sizeを表す変数Fsizeの作成、tableの作成→以下のとおり変更
train$Fsize <- train$SibSp + train$Parch + 1
# Survived × Fsize
table(train$Survived, train$Fsize)##
## 1 2 3 4 5 6 7 8 11
## Died 374 72 43 8 12 19 8 6 7
## Survived 163 89 59 21 3 3 4 0 0##round(prop.table(table(train\(Survived, train\)Fsize),2),digit=2)
round(prop.table(table(train$Survived, train$Fsize),2),digit=2)##
## 1 2 3 4 5 6 7 8 11
## Died 0.70 0.45 0.42 0.28 0.80 0.86 0.67 1.00 1.00
## Survived 0.30 0.55 0.58 0.72 0.20 0.14 0.33 0.00 0.00##乗船している家族の人数は1人のとき生存率は0.3と低いが2人から4人までの生存率は高く，5人以上からは生存率が低くなる傾向にある．このことは，家族がいない乗客の生存率は低い傾向にあるが，家族が多すぎる乗客の生存率も低い傾向にあることを示唆している．
# Fare
ggplot(train, aes(x = Fare, fill = Survived)) +
geom_histogram() +
scale_x_continuous() +
labs(x = 'Fare') +
theme_igray()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##運賃が高いほど生存率が上がる？
# Embarked
table(train$Survived, train$Embarked)##
## C Q S
## Died 75 47 427
## Survived 93 30 217round(prop.table(table(train$Survived, train$Embarked),2),digit=2)##
## C Q S
## Died 0.45 0.61 0.66
## Survived 0.55 0.39 0.34説明変数の作成と欠損値の補完
- 年末までにここから
# DFamsize変数の作成(Family sizeをSingle, Small, Largeにカテゴリ化)
alldata$Famsize <- alldata$SibSp + alldata$Parch + 1
alldata$DFamsize[alldata$Famsize==1] <- 'Single'
alldata$DFamsize[alldata$Famsize>1 & alldata$Famsize<5] <- 'Small'
alldata$DFamsize[alldata$Famsize>4] <- 'Large'
#alldata$DFamsize
#alldata[10-11,]これは文法上間違いでPassengerId 10-11まで抽出できない
alldata[10,]#PassengerId10 これでできる。 ## PassengerId Survived Pclass Name Sex Age
## 10 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14
## SibSp Parch Ticket Fare Cabin Embarked Famsize DFamsize
## 10 1 0 237736 30.0708 <NA> C 2 Small- 年末までにここまで支障なくできている
- しかし、ファクター型への変換で躓きうまくいっていない。
- 参考2：https://qiita.com/crash-boy/items/12a6b940fafbc549712a
#x <- factor(c(5, 0, 5, 0, 10), levels=c(10, 5, 0), labels=c("a", "b", "c"))
#labels(x)
#as.integer(x)令和2年1/７ チャレンジ
- データフレーム ファクター型へ変換
- factor_var：順序なしカテゴリ変数に変換する
- ファクター型へ変換（ここで間違いが発生）
- 参考：http://m-hiyama.hatenablog.com/entry/20151215/1450140312
- 参考2：https://qiita.com/crash-boy/items/12a6b940fafbc549712a
- 以下ｒの中身
factor_vars <- c('Pclass','Sex','Embarked','Dfamsize','Survived')
factor_vars## [1] "Pclass" "Sex" "Embarked" "Dfamsize" "Survived"#alldata[factor_vars]
#alldata[factor_vars] <- lapply(alldata[factor_vars], function(x) as.factor(x))ファクター型へ変換
factor_vars <- c(‘Pclass’,‘Sex’,‘Embarked’,‘Dfamsize’,‘Survived’)
でfactor_varsという配列要素を作るところまではできたが、それにファクター・データを書き込む所がうまくいかない
alldata[factor_vars] <- lapply(alldata[factor_vars], function(x) as.factor(x))
が良くわからないので、https://qiita.com/crash-boy/items/12a6b940fafbc549712a
を後半の参考として、sapply(alldata,function(x) sum(is.na(x)))
をまず解析する。これを実行すると 欠損値の合計を一覧で表示する。
例えばAgeは263の欠損があり、 Cabinは1014の欠損
sapply(alldata,function(x) sum(is.na(x)))## PassengerId Survived Pclass Name Sex Age
## 0 418 0 0 0 263
## SibSp Parch Ticket Fare Cabin Embarked
## 0 0 0 1 1014 2
## Famsize DFamsize
## 0 0head(alldata)## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## 6 6 0 3
## Name Sex Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## 6 Moran, Mr. James male NA 0 0
## Ticket Fare Cabin Embarked Famsize DFamsize
## 1 A/5 21171 7.2500 <NA> S 2 Small
## 2 PC 17599 71.2833 C85 C 2 Small
## 3 STON/O2. 3101282 7.9250 <NA> S 1 Single
## 4 113803 53.1000 C123 S 2 Small
## 5 373450 8.0500 <NA> S 1 Single
## 6 330877 8.4583 <NA> Q 1 Single- 次に,lapply(alldata, function(x) as.factor(x))を実行すると,
- 全てのカテゴリごとに全データを打ち出す。
- lapply はリスト型に対して、何かを一括演算するときに利用する。リスト型のデータは、1、2、…
- ように番号付けられているため、行列のように行または列を指定しない。
- そのため行列を指定しない分、引数は入力データと演算処理を行う関数の 2 つだけである。
- https://stats.biopapyrus.jp/r/basic/apply.html
- lapply(x,sum)では、ベクトルｘ毎に合計値を出す。
- lapply(alldata, function(x) as.factor(x))
- ここではfunction(x)は、関数を意味するだけでこれには何も演算機能がない
- as.factor(x)という自分で作成した関数を、実行せよという意味である。
- factor()関数を使って因子型へ返還する。
- 参考：http://taustation.com/r-factor/
alldata[is.na(alldata$Age), “Age”] <- apply(alldata[is.na(alldata$Age), ] , 1, function(x)title.age[title.age[, 1]==x[“Title”], 2])
代替プラン (因子型へ返還）
- alldata[factor_vars] <- lapply(alldata[factor_vars], function(x) as.factor(x))
- のalldata[factor_vars]の文法は良くわからないので、ひとつづつたってみると
- 下記の事をしたいのだろうと推測する
- ◆◆aファクター型へ変換されたａlldataを表示 → alldata\(Pclass <- as.factor(alldata\)Pclass);alldata$Pclass
- ◆参考◆http://by-oneself.com/r_basic_factor/
alldata$Pclass <- as.factor(alldata$Pclass)alldata$Sex <- as.factor(alldata$Sex)alldata$Embarked <- as.factor(alldata$Embarked)alldata$Survived <- as.factor(alldata$Survived)alldata$DFamsize <- as.factor(alldata$DFamsize)head(alldata)## PassengerId Survived Pclass
## 1 1 0 3
## 2 2 1 1
## 3 3 1 3
## 4 4 1 1
## 5 5 0 3
## 6 6 0 3
## Name Sex Age SibSp Parch
## 1 Braund, Mr. Owen Harris male 22 1 0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0
## 3 Heikkinen, Miss. Laina female 26 0 0
## 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0
## 5 Allen, Mr. William Henry male 35 0 0
## 6 Moran, Mr. James male NA 0 0
## Ticket Fare Cabin Embarked Famsize DFamsize
## 1 A/5 21171 7.2500 <NA> S 2 Small
## 2 PC 17599 71.2833 C85 C 2 Small
## 3 STON/O2. 3101282 7.9250 <NA> S 1 Single
## 4 113803 53.1000 C123 S 2 Small
## 5 373450 8.0500 <NA> S 1 Single
## 6 330877 8.4583 <NA> Q 1 Single#次によりsex等は、chr型からfct型変換されているのがわかる。
glimpse(alldata)## Observations: 1,309
## Variables: 14
## $ PassengerId <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, ...
## $ Survived <fct> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0...
## $ Pclass <fct> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3...
## $ Name <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley ...
## $ Sex <fct> male, female, female, female, male, male, male, male, f...
## $ Age <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, 39, 1...
## $ SibSp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1...
## $ Parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0...
## $ Ticket <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", ...
## $ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.86...
## $ Cabin <chr> NA, "C85", NA, "C123", NA, NA, "E46", NA, NA, NA, "G6",...
## $ Embarked <fct> S, C, S, S, S, Q, S, S, S, C, S, S, S, S, S, S, Q, S, S...
## $ Famsize <dbl> 2, 2, 1, 2, 1, 1, 1, 5, 3, 2, 3, 1, 1, 7, 1, 1, 6, 1, 2...
## $ DFamsize <fct> Small, Small, Single, Small, Single, Single, Single, La...欠損値の補完
1.Fare（運賃）の欠損値の補完
- 以下により、PassengerId 1044のみがFareを欠損していることが分かるので、
# Fareの欠損値の補完
sum(is.na(alldata$Fare))## [1] 1which(is.na(alldata$Fare))## [1] 1044alldata[1044,]## PassengerId Survived Pclass Name Sex Age SibSp Parch
## 1044 1044 <NA> 3 Storey, Mr. Thomas male 60.5 0 0
## Ticket Fare Cabin Embarked Famsize DFamsize
## 1044 3701 NA <NA> S 1 Single- 利用できそうな変数：Pclass=3、Embarked=“S”
- Pclassが3の値を有しており，Embarked(乗船場所）がSouthamptonの乗客の運賃の中央値で補完する
tempdata<-alldata[which(alldata[,"Pclass"]==3 & alldata[,"Embarked"]== "S"),]- これにより、8.05で補われた事が確認できる。
alldata[1044,]$Fare<-median(tempdata$Fare,na.rm=T)
alldata[1044,]$Fare## [1] 8.052.Embarked（出発港)の欠損値の補完
- 以下によりmbarked（出発港)の情報が無いのは2人であることが分かる
sum(is.na(alldata$Embarked))## [1] 2- 以下によりmbarked（出発港)の情報が無いのはPassengerIdgが62 and 830
- この2人の乗客の共通点はPclassが1とFareが80ということがわかる。
- Pclassが1の値を有する乗客の乗船地：Qが圧倒的に少ない
- C:シェルブール,Q:アイルランドのクイーンズタウン,S:イギリス・サウサンプトン港
a <- which(is.na(alldata$Embarked)) # 62 and 830
alldata[a,]# この2人の乗客の共通点はPclassが1とFareが80ということ## PassengerId Survived Pclass Name
## 62 62 1 1 Icard, Miss. Amelie
## 830 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn)
## Sex Age SibSp Parch Ticket Fare Cabin Embarked Famsize DFamsize
## 62 female 38 0 0 113572 80 B28 <NA> 1 Single
## 830 female 62 0 0 113572 80 B28 <NA> 1 Singletempdata <- alldata[-a,]
tempdata <- tempdata[which(tempdata[,"Pclass"]==1),]
table(tempdata$Embarked) # Pclassが1の値を有する乗客の乗船地：Qが圧倒的に少ない##
## C Q S
## 141 3 177summary(tempdata$Fare)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 30.70 60.00 87.56 108.90 512.33◆◆ Embarked（出発港別料金の箱ひげ図）
- boxplotよりアウトサンプルにおけるPclassが1の値を有する乗客の乗船場所別の運賃の中央値(box内の横線)が80なのはCだと分かる。なので欠損値にCを補完する
ggplot(tempdata, aes(x = Embarked, y = Fare)) +
geom_boxplot() +
geom_hline(yintercept = 80,colour = "red" ,lwd = .5)
# boxplotよりアウトサンプルにおけるPclassが1の値を有する乗客の乗船場所別の運賃の中央値(box内の横線)が80なのはCだと分かる．
# なので欠損値にCを補完する
alldata$Embarked[a] <- "C"3.Ageの欠損値を補完
- 最も複雑な欠損方法を行っている。
- 氏名からその前についている(Mr, Missなど)部分を抽出して新たな変数Titleを使用する
sum(is.na(alldata$Age)) #263 missing values for 'Age'## [1] 263# Name変数の情報を利用して補完を行う
alldata$Title <- gsub('(.*, )|(\\..*)', '', alldata$Name) #Name変数から呼称(Mr, Missなど)部分を抽出して新たな変数Titleとする．
# 使用している正規表現は次のとおり。
# (.*, )|(\\..*)のうち()はグループ化であり，.は任意の一文字。*は直前の一文字を0回以上続ける。
# \\.は単なる「.」文字のことであり\\..は「.の後に任意の一文字」を意味しており，さらに\\..*は「.●●」と.の後の任意の一文字を0回以上続ける，つまり，.の後の文字列を指定していることを意味する.
table(alldata$Title)##
## Capt Col Don Dona Dr Jonkheer
## 1 4 1 1 8 1
## Lady Major Master Miss Mlle Mme
## 1 2 61 260 2 1
## Mr Mrs Ms Rev Sir the Countess
## 757 197 2 8 1 1officer <- c('Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev')
royalty <- c('Dona', 'Lady', 'the Countess','Sir', 'Jonkheer')Miss, Mrs, Royalty, Officerへ集約
# Miss, Mrs, Royalty, Officerへ集約する
alldata$Title[alldata$Title == 'Mlle'] <- 'Miss'
alldata$Title[alldata$Title == 'Ms'] <- 'Miss'
alldata$Title[alldata$Title == 'Mme'] <- 'Mrs'
alldata$Title[alldata$Title %in% royalty] <- 'Royalty'
alldata$Title[alldata$Title %in% officer] <- 'Officer'
alldata$Title<-as.factor(alldata$Title)Titleごとの中央値でAgeの欠損値を補完する
# Titleごとの中央値でAgeの欠損値を補完する
# alldata$Age#欠損値補正前
tapply(alldata$Age, alldata$Title,median, na.rm=TRUE)## Master Miss Mr Mrs Officer Royalty
## 4 22 29 35 49 39title.age <- aggregate(alldata$Age,by = list(alldata$Title), FUN = function(x) median(x, na.rm = T))
title.age # Titleごとの年齢の中央値## Group.1 x
## 1 Master 4
## 2 Miss 22
## 3 Mr 29
## 4 Mrs 35
## 5 Officer 49
## 6 Royalty 39alldata[is.na(alldata$Age), "Age"] <- apply(alldata[is.na(alldata$Age), ] , 1, function(x) title.age[title.age[, 1]==x["Title"], 2])
#alldata$Age#欠損値を補完後 library(dplyr)
library(tidyverse)## Warning: package 'tidyverse' was built under R version 3.6.2## -- Attaching packages ------------------------------------------------------------------------------ tidyverse 1.3.0 --## √ tibble 2.1.3 √ stringr 1.4.0
## √ purrr 0.3.3 √ forcats 0.4.0## Warning: package 'stringr' was built under R version 3.6.2## Warning: package 'forcats' was built under R version 3.6.2## -- Conflicts --------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::between() masks data.table::between()
## x dplyr::combine() masks randomForest::combine()
## x dplyr::filter() masks stats::filter()
## x dplyr::first() masks data.table::first()
## x dplyr::lag() masks stats::lag()
## x dplyr::last() masks data.table::last()
## x purrr::lift() masks caret::lift()
## x randomForest::margin() masks ggplot2::margin()
## x dplyr::recode() masks car::recode()
## x purrr::some() masks car::some()
## x purrr::transpose() masks data.table::transpose()library(knitr)
library(purrr) #別解のやり方をするときに使用。-相関行列を作成する前に変数Wom_chd,Groupを作成しておく。
# Ticketk変数を番号ごとに集計した結果を新たな変数groupTKTとする．
groupTKT <- alldata %>%
group_by(Ticket) %>%
summarise(N = n()) %>%
filter(N > 2) %>%
arrange(desc(N))
head(groupTKT, 5)## # A tibble: 5 x 2
## Ticket N
## <chr> <int>
## 1 CA. 2343 11
## 2 1601 8
## 3 CA 2144 8
## 4 3101295 7
## 5 347077 7# 女性もしくは18歳以下ならばYes, それ以外はNoの値とるWom_chd変数の作成
alldata$Wom_chd <- "No"
alldata$Wom_chd[which(alldata$Sex == "female" | alldata$Age < 18)] = "Yes"
alldata$Wom_chd <- as.factor(alldata$Wom_chd)
# groupTKTの値が2より大きければYes，それ以外ならNoの値をとるGroup変数の作成
alldata$Group = "No"
alldata$Group[which(alldata$Ticket %in% groupTKT$Ticket)] = "Yes"
alldata$Group <- as.factor(alldata$Group)相関行列
- select文の使い方でエラーがでた。以下参照
- https://kazutan.github.io/kazutanR/hands_on_170730/select.html#%E4%BD%BF%E3%81%84%E6%96%B9
- library(dplyr)
- select(.data = iris, Sepal.Length, Petal.Length, Species)
- select 文を使う場合、テーブル名（iris) の次のフィールド名が来ないとエラーになる
- また、 dum <- select(.data = alldata,Survived, Pclass,Sex, Embarked, DFamsize, Title)とすることにより
- 不要な変数 cordata を使う必要が無くなる。
# ダミー化したい変数をセレクト
dum <- select(.data = alldata,Survived, Pclass,Sex, Embarked, DFamsize, Title, Group, Wom_chd)
# dum <- select(.data = alldata,Survived, Pclass,Sex, Embarked, DFamsize, Title, Wom_chd)
head(dum)## Survived Pclass Sex Embarked DFamsize Title Group Wom_chd
## 1 0 3 male S Small Mr No No
## 2 1 1 female C Small Mrs No Yes
## 3 1 3 female S Single Miss No Yes
## 4 1 1 female S Small Mrs No Yes
## 5 0 3 male S Single Mr No No
## 6 0 3 male Q Single Mr No No# ダミー化しない変数をセレクト
not_dum <- select(.data = alldata,PassengerId, Name, Age, SibSp, Parch, Ticket, Fare, Cabin, Famsize)
head(not_dum)## PassengerId Name Age SibSp
## 1 1 Braund, Mr. Owen Harris 22 1
## 2 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) 38 1
## 3 3 Heikkinen, Miss. Laina 26 0
## 4 4 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35 1
## 5 5 Allen, Mr. William Henry 35 0
## 6 6 Moran, Mr. James 29 0
## Parch Ticket Fare Cabin Famsize
## 1 0 A/5 21171 7.2500 <NA> 2
## 2 0 PC 17599 71.2833 C85 2
## 3 0 STON/O2. 3101282 7.9250 <NA> 1
## 4 0 113803 53.1000 C123 2
## 5 0 373450 8.0500 <NA> 1
## 6 0 330877 8.4583 <NA> 1#作成したダミー変数の名前を修正
#参考1：http://webbeginner.hatenablog.com/entry/2014/05/09/234219
#参考2：https://hikaru1122.hatenadiary.jp/entry/2015/05/06/003000
#dummy_var %>% dplyr::rename(Survived=res, Sex=res.1, Group=res.2, Wom_chd=res.3)
#dummy_var <- dplyr::rename(dummy_var,Survived=res, Sex=res.1, Group=res.2, Wom_chd=res.3)
dum %>% dplyr::rename(res=Survived,res.1=Sex, res.2=Group, res.3=Wom_chd)## res Pclass res.1 Embarked DFamsize Title res.2 res.3
## 1 0 3 male S Small Mr No No
## 2 1 1 female C Small Mrs No Yes
## 3 1 3 female S Single Miss No Yes
## 4 1 1 female S Small Mrs No Yes
## 5 0 3 male S Single Mr No No
## 6 0 3 male Q Single Mr No No
## 7 0 1 male S Single Mr No No
## 8 0 3 male S Large Master Yes Yes
## 9 1 3 female S Small Mrs Yes Yes
## 10 1 2 female C Small Mrs No Yes
## 11 1 3 female S Small Miss Yes Yes
## 12 1 1 female S Single Miss No Yes
## 13 0 3 male S Single Mr No No
## 14 0 3 male S Large Mr Yes No
## 15 0 3 female S Single Miss No Yes
## 16 1 2 female S Single Mrs No Yes
## 17 0 3 male Q Large Master Yes Yes
## 18 1 2 male S Single Mr No No
## 19 0 3 female S Small Mrs No Yes
## 20 1 3 female C Single Mrs No Yes
## 21 0 2 male S Single Mr No No
## 22 1 2 male S Single Mr No No
## 23 1 3 female Q Single Miss No Yes
## 24 1 1 male S Single Mr No No
## 25 0 3 female S Large Miss Yes Yes
## 26 1 3 female S Large Mrs Yes Yes
## 27 0 3 male C Single Mr No No
## 28 0 1 male S Large Mr Yes No
## 29 1 3 female Q Single Miss No Yes
## 30 0 3 male S Single Mr No No
## 31 0 1 male C Single Officer No No
## 32 1 1 female C Small Mrs Yes Yes
## 33 1 3 female Q Single Miss No Yes
## 34 0 2 male S Single Mr No No
## 35 0 1 male C Small Mr No No
## 36 0 1 male S Small Mr No No
## 37 1 3 male C Single Mr No No
## 38 0 3 male S Single Mr No No
## 39 0 3 female S Small Miss No Yes
## 40 1 3 female C Small Miss No Yes
## 41 0 3 female S Small Mrs No Yes
## 42 0 2 female S Small Mrs No Yes
## 43 0 3 male C Single Mr No No
## 44 1 2 female C Small Miss Yes Yes
## 45 1 3 female Q Single Miss No Yes
## 46 0 3 male S Single Mr No No
## 47 0 3 male Q Small Mr No No
## 48 1 3 female Q Single Miss No Yes
## 49 0 3 male C Small Mr Yes No
## 50 0 3 female S Small Mrs No Yes
## 51 0 3 male S Large Master Yes Yes
## 52 0 3 male S Single Mr No No
## 53 1 1 female C Small Mrs Yes Yes
## 54 1 2 female S Small Mrs No Yes
## 55 0 1 male C Small Mr No No
## 56 1 1 male S Single Mr No No
## 57 1 2 female S Single Miss No Yes
## 58 0 3 male C Single Mr No No
## 59 1 2 female S Small Miss Yes Yes
## 60 0 3 male S Large Master Yes Yes
## 61 0 3 male C Single Mr No No
## 62 1 1 female C Single Miss No Yes
## 63 0 1 male S Small Mr No No
## 64 0 3 male S Large Master Yes Yes
## 65 0 1 male C Single Mr No No
## 66 1 3 male C Small Master Yes Yes
## 67 1 2 female S Single Mrs No Yes
## 68 0 3 male S Single Mr No No
## 69 1 3 female S Large Miss No Yes
## 70 0 3 male S Small Mr No No
## 71 0 2 male S Single Mr No No
## 72 0 3 female S Large Miss Yes Yes
## 73 0 2 male S Single Mr Yes No
## 74 0 3 male C Small Mr No No
## 75 1 3 male S Single Mr Yes No
## 76 0 3 male S Single Mr No No
## 77 0 3 male S Single Mr No No
## 78 0 3 male S Single Mr No No
## 79 1 2 male S Small Master Yes Yes
## 80 1 3 female S Single Miss No Yes
## 81 0 3 male S Single Mr No No
## 82 1 3 male S Single Mr No No
## 83 1 3 female Q Single Miss No Yes
## 84 0 1 male S Single Mr No No
## 85 1 2 female S Single Miss No Yes
## 86 1 3 female S Small Mrs No Yes
## 87 0 3 male S Large Mr Yes Yes
## 88 0 3 male S Single Mr No No
## 89 1 1 female S Large Miss Yes Yes
## 90 0 3 male S Single Mr No No
## 91 0 3 male S Single Mr No No
## 92 0 3 male S Single Mr No No
## 93 0 1 male S Small Mr No No
## 94 0 3 male S Small Mr Yes No
## 95 0 3 male S Single Mr No No
## 96 0 3 male S Single Mr No No
## 97 0 1 male C Single Mr No No
## 98 1 1 male C Small Mr No No
## 99 1 2 female S Small Mrs No Yes
## 100 0 2 male S Small Mr No No
## 101 0 3 female S Single Miss No Yes
## 102 0 3 male S Single Mr No No
## 103 0 1 male S Small Mr No No
## 104 0 3 male S Single Mr No No
## 105 0 3 male S Small Mr No No
## 106 0 3 male S Single Mr No No
## 107 1 3 female S Single Miss No Yes
## 108 1 3 male S Single Mr No No
## 109 0 3 male S Single Mr No No
## 110 1 3 female Q Small Miss Yes Yes
## 111 0 1 male S Single Mr No No
## 112 0 3 female C Small Miss No Yes
## 113 0 3 male S Single Mr No No
## 114 0 3 female S Small Miss No Yes
## 115 0 3 female C Single Miss No Yes
## 116 0 3 male S Single Mr No No
## 117 0 3 male Q Single Mr No No
## 118 0 2 male S Small Mr No No
## 119 0 1 male C Small Mr Yes No
## 120 0 3 female S Large Miss Yes Yes
## 121 0 2 male S Small Mr Yes No
## 122 0 3 male S Single Mr No No
## 123 0 2 male C Small Mr No No
## 124 1 2 female S Single Miss No Yes
## 125 0 1 male S Small Mr No No
## 126 1 3 male C Small Master No Yes
## 127 0 3 male Q Single Mr No No
## 128 1 3 male S Single Mr No No
## 129 1 3 female C Small Miss Yes Yes
## 130 0 3 male S Single Mr No No
## 131 0 3 male C Single Mr No No
## 132 0 3 male S Single Mr No No
## 133 0 3 female S Small Mrs No Yes
## 134 1 2 female S Small Mrs No Yes
## 135 0 2 male S Single Mr No No
## 136 0 2 male C Single Mr No No
## 137 1 1 female S Small Miss No Yes
## 138 0 1 male S Small Mr No No
## 139 0 3 male S Single Mr No Yes
## 140 0 1 male C Single Mr No No
## 141 0 3 female C Small Mrs Yes Yes
## 142 1 3 female S Single Miss No Yes
## 143 1 3 female S Small Mrs No Yes
## 144 0 3 male Q Single Mr No No
## 145 0 2 male S Single Mr No No
## 146 0 2 male S Small Mr Yes No
## 147 1 3 male S Single Mr No No
## 148 0 3 female S Large Miss Yes Yes
## 149 0 2 male S Small Mr Yes No
## 150 0 2 male S Single Officer No No
## 151 0 2 male S Single Officer No No
## 152 1 1 female S Small Mrs No Yes
## 153 0 3 male S Single Mr No No
## 154 0 3 male S Small Mr Yes No
## 155 0 3 male S Single Mr No No
## 156 0 1 male C Small Mr No No
## 157 1 3 female Q Single Miss No Yes
## 158 0 3 male S Single Mr No No
## 159 0 3 male S Single Mr No No
## 160 0 3 male S Large Master Yes Yes
## 161 0 3 male S Small Mr No No
## 162 1 2 female S Single Mrs No Yes
## 163 0 3 male S Single Mr No No
## 164 0 3 male S Single Mr No Yes
## 165 0 3 male S Large Master Yes Yes
## 166 1 3 male S Small Master Yes Yes
## 167 1 1 female S Small Mrs No Yes
## 168 0 3 female S Large Mrs Yes Yes
## 169 0 1 male S Single Mr No No
## 170 0 3 male S Single Mr Yes No
## 171 0 1 male S Single Mr No No
## 172 0 3 male Q Large Master Yes Yes
## 173 1 3 female S Small Miss Yes Yes
## 174 0 3 male S Single Mr No No
## 175 0 1 male C Single Mr No No
## 176 0 3 male S Small Mr No No
## 177 0 3 male S Large Master Yes Yes
## 178 0 1 female C Single Miss No Yes
## 179 0 2 male S Single Mr No No
## 180 0 3 male S Single Mr Yes No
## 181 0 3 female S Large Miss Yes Yes
## 182 0 2 male C Single Mr No No
## 183 0 3 male S Large Master Yes Yes
## 184 1 2 male S Small Master Yes Yes
## 185 1 3 female S Small Miss Yes Yes
## 186 0 1 male S Single Mr No No
## 187 1 3 female Q Small Mrs No Yes
## 188 1 1 male S Single Mr No No
## 189 0 3 male Q Small Mr No No
## 190 0 3 male S Single Mr No No
## 191 1 2 female S Single Mrs No Yes
## 192 0 2 male S Single Mr No No
## 193 1 3 female S Small Miss No Yes
## 194 1 2 male S Small Master Yes Yes
## 195 1 1 female C Single Mrs No Yes
## 196 1 1 female C Single Miss Yes Yes
## 197 0 3 male Q Single Mr No No
## 198 0 3 male S Small Mr No No
## 199 1 3 female Q Single Miss No Yes
## 200 0 2 female S Single Miss No Yes
## 201 0 3 male S Single Mr No No
## 202 0 3 male S Large Mr Yes No
## 203 0 3 male S Single Mr No No
## 204 0 3 male C Single Mr No No
## 205 1 3 male S Single Mr No No
## 206 0 3 female S Small Miss No Yes
## 207 0 3 male S Small Mr No No
## 208 1 3 male C Single Mr No No
## 209 1 3 female Q Single Miss No Yes
## 210 1 1 male C Single Mr No No
## 211 0 3 male S Single Mr No No
## 212 1 2 female S Single Miss No Yes
## 213 0 3 male S Single Mr No No
## 214 0 2 male S Single Mr No No
## 215 0 3 male Q Small Mr No No
## 216 1 1 female C Small Miss Yes Yes
## 217 1 3 female S Single Miss No Yes
## 218 0 2 male S Small Mr No No
## 219 1 1 female C Single Miss No Yes
## 220 0 2 male S Single Mr No No
## 221 1 3 male S Single Mr No Yes
## 222 0 2 male S Single Mr No No
## 223 0 3 male S Single Mr No No
## 224 0 3 male S Single Mr No No
## 225 1 1 male S Small Mr No No
## 226 0 3 male S Single Mr No No
## 227 1 2 male S Single Mr No No
## 228 0 3 male S Single Mr No No
## 229 0 2 male S Single Mr No No
## 230 0 3 female S Large Miss Yes Yes
## 231 1 1 female S Small Mrs No Yes
## 232 0 3 male S Single Mr No No
## 233 0 2 male S Single Mr No No
## 234 1 3 female S Large Miss Yes Yes
## 235 0 2 male S Single Mr No No
## 236 0 3 female S Single Miss No Yes
## 237 0 2 male S Small Mr No No
## 238 1 2 female S Small Miss Yes Yes
## 239 0 2 male S Single Mr No No
## 240 0 2 male S Single Mr No No
## 241 0 3 female C Small Miss No Yes
## 242 1 3 female Q Small Miss No Yes
## 243 0 2 male S Single Mr No No
## 244 0 3 male S Single Mr No No
## 245 0 3 male C Single Mr No No
## 246 0 1 male Q Small Officer Yes No
## 247 0 3 female S Single Miss No Yes
## 248 1 2 female S Small Mrs No Yes
## 249 1 1 male S Small Mr No No
## 250 0 2 male S Small Officer No No
## 251 0 3 male S Single Mr No No
## 252 0 3 female S Small Mrs No Yes
## 253 0 1 male S Single Mr No No
## 254 0 3 male S Small Mr No No
## 255 0 3 female S Small Mrs Yes Yes
## 256 1 3 female C Small Mrs Yes Yes
## 257 1 1 female C Single Mrs No Yes
## 258 1 1 female S Single Miss Yes Yes
## 259 1 1 female C Single Miss Yes Yes
## 260 1 2 female S Small Mrs No Yes
## 261 0 3 male Q Single Mr No No
## 262 1 3 male S Large Master Yes Yes
## 263 0 1 male S Small Mr Yes No
## 264 0 1 male S Single Mr No No
## 265 0 3 female Q Single Miss No Yes
## 266 0 2 male S Single Mr No No
## 267 0 3 male S Large Mr Yes Yes
## 268 1 3 male S Small Mr No No
## 269 1 1 female S Small Mrs Yes Yes
## 270 1 1 female S Single Miss Yes Yes
## 271 0 1 male S Single Mr No No
## 272 1 3 male S Single Mr Yes No
## 273 1 2 female S Small Mrs No Yes
## 274 0 1 male C Small Mr No No
## 275 1 3 female Q Single Miss No Yes
## 276 1 1 female S Small Miss Yes Yes
## 277 0 3 female S Single Miss No Yes
## 278 0 2 male S Single Mr Yes No
## 279 0 3 male Q Large Master Yes Yes
## 280 1 3 female S Small Mrs Yes Yes
## 281 0 3 male Q Single Mr No No
## 282 0 3 male S Single Mr No No
## 283 0 3 male S Single Mr No Yes
## 284 1 3 male S Single Mr No No
## 285 0 1 male S Single Mr No No
## 286 0 3 male C Single Mr No No
## 287 1 3 male S Single Mr No No
## 288 0 3 male S Single Mr No No
## 289 1 2 male S Single Mr No No
## 290 1 3 female Q Single Miss No Yes
## 291 1 1 female S Single Miss Yes Yes
## 292 1 1 female C Small Mrs No Yes
## 293 0 2 male C Single Mr No No
## 294 0 3 female S Single Miss No Yes
## 295 0 3 male S Single Mr No No
## 296 0 1 male C Single Mr No No
## 297 0 3 male C Single Mr No No
## 298 0 1 female S Small Miss Yes Yes
## 299 1 1 male S Single Mr No No
## 300 1 1 female C Small Mrs Yes Yes
## 301 1 3 female Q Single Miss No Yes
## 302 1 3 male Q Small Mr Yes No
## 303 0 3 male S Single Mr Yes No
## 304 1 2 female Q Single Miss No Yes
## 305 0 3 male S Single Mr No No
## 306 1 1 male S Small Master Yes Yes
## 307 1 1 female C Single Miss Yes Yes
## 308 1 1 female C Small Mrs Yes Yes
## 309 0 2 male C Small Mr No No
## 310 1 1 female C Single Miss No Yes
## 311 1 1 female C Single Miss Yes Yes
## 312 1 1 female C Large Miss Yes Yes
## 313 0 2 female S Small Mrs No Yes
## 314 0 3 male S Single Mr No No
## 315 0 2 male S Small Mr Yes No
## 316 1 3 female S Single Miss No Yes
## 317 1 2 female S Small Mrs No Yes
## 318 0 2 male S Single Officer No No
## 319 1 1 female S Small Miss Yes Yes
## 320 1 1 female C Small Mrs Yes Yes
## 321 0 3 male S Single Mr No No
## 322 0 3 male S Single Mr No No
## 323 1 2 female Q Single Miss No Yes
## 324 1 2 female S Small Mrs Yes Yes
## 325 0 3 male S Large Mr Yes No
## 326 1 1 female C Single Miss Yes Yes
## 327 0 3 male S Single Mr No No
## 328 1 2 female S Single Mrs No Yes
## 329 1 3 female S Small Mrs Yes Yes
## 330 1 1 female C Small Miss No Yes
## 331 1 3 female Q Small Miss Yes Yes
## 332 0 1 male S Single Mr No No
## 333 0 1 male S Small Mr Yes No
## 334 0 3 male S Small Mr No Yes
## 335 1 1 female S Small Mrs No Yes
## 336 0 3 male S Single Mr No No
## 337 0 1 male S Small Mr No No
## 338 1 1 female C Single Miss Yes Yes
## 339 1 3 male S Single Mr No No
## 340 0 1 male S Single Mr No No
## 341 1 2 male S Small Master Yes Yes
## 342 1 1 female S Large Miss Yes Yes
## 343 0 2 male S Single Mr No No
## 344 0 2 male S Single Mr No No
## 345 0 2 male S Single Mr No No
## 346 1 2 female S Single Miss No Yes
## 347 1 2 female S Single Miss No Yes
## 348 1 3 female S Small Mrs No Yes
## 349 1 3 male S Small Master Yes Yes
## 350 0 3 male S Single Mr No No
## 351 0 3 male S Single Mr No No
## 352 0 1 male S Single Mr No No
## 353 0 3 male C Small Mr No Yes
## 354 0 3 male S Small Mr No No
## 355 0 3 male C Single Mr No No
## 356 0 3 male S Single Mr No No
## 357 1 1 female S Small Miss No Yes
## 358 0 2 female S Single Miss No Yes
## 359 1 3 female Q Single Miss No Yes
## 360 1 3 female Q Single Miss No Yes
## 361 0 3 male S Large Mr Yes No
## 362 0 2 male C Small Mr No No
## 363 0 3 female C Small Mrs No Yes
## 364 0 3 male S Single Mr No No
## 365 0 3 male Q Small Mr No No
## 366 0 3 male S Single Mr No No
## 367 1 1 female C Small Mrs No Yes
## 368 1 3 female C Single Mrs No Yes
## 369 1 3 female Q Single Miss No Yes
## 370 1 1 female C Single Mrs No Yes
## 371 1 1 male C Small Mr No No
## 372 0 3 male S Small Mr No No
## 373 0 3 male S Single Mr No No
## 374 0 1 male C Single Mr Yes No
## 375 0 3 female S Large Miss Yes Yes
## 376 1 1 female C Small Mrs No Yes
## 377 1 3 female S Single Miss No Yes
## 378 0 1 male C Small Mr Yes No
## 379 0 3 male C Single Mr No No
## 380 0 3 male S Single Mr No No
## 381 1 1 female C Single Miss Yes Yes
## 382 1 3 female C Small Miss Yes Yes
## 383 0 3 male S Single Mr No No
## 384 1 1 female S Small Mrs No Yes
## 385 0 3 male S Single Mr No No
## 386 0 2 male S Single Mr Yes No
## 387 0 3 male S Large Master Yes Yes
## 388 1 2 female S Single Miss No Yes
## 389 0 3 male Q Single Mr No No
## 390 1 2 female C Single Miss No Yes
## 391 1 1 male S Small Mr Yes No
## 392 1 3 male S Single Mr No No
## 393 0 3 male S Small Mr No No
## 394 1 1 female C Small Miss Yes Yes
## 395 1 3 female S Small Mrs Yes Yes
## 396 0 3 male S Single Mr No No
## 397 0 3 female S Single Miss No Yes
## 398 0 2 male S Single Mr No No
## 399 0 2 male S Single Officer No No
## 400 1 2 female S Single Mrs No Yes
## 401 1 3 male S Single Mr No No
## 402 0 3 male S Single Mr No No
## 403 0 3 female S Small Miss No Yes
## 404 0 3 male S Small Mr No No
## 405 0 3 female S Single Miss No Yes
## 406 0 2 male S Small Mr No No
## 407 0 3 male S Single Mr No No
## 408 1 2 male S Small Master Yes Yes
## 409 0 3 male S Single Mr No No
## 410 0 3 female S Large Miss Yes Yes
## 411 0 3 male S Single Mr No No
## 412 0 3 male Q Single Mr No No
## 413 1 1 female Q Small Miss Yes Yes
## 414 0 2 male S Single Mr Yes No
## 415 1 3 male S Single Mr No No
## 416 0 3 female S Single Mrs No Yes
## 417 1 2 female S Small Mrs Yes Yes
## 418 1 2 female S Small Miss No Yes
## 419 0 2 male S Single Mr No No
## 420 0 3 female S Small Miss Yes Yes
## 421 0 3 male C Single Mr No No
## 422 0 3 male Q Single Mr No No
## 423 0 3 male S Single Mr No No
## 424 0 3 female S Small Mrs Yes Yes
## 425 0 3 male S Small Mr Yes No
## 426 0 3 male S Single Mr No No
## 427 1 2 female S Small Mrs No Yes
## 428 1 2 female S Single Miss No Yes
## 429 0 3 male Q Single Mr No No
## 430 1 3 male S Single Mr No No
## 431 1 1 male S Single Mr No No
## 432 1 3 female S Small Mrs No Yes
## 433 1 2 female S Small Mrs No Yes
## 434 0 3 male S Single Mr No Yes
## 435 0 1 male S Small Mr No No
## 436 1 1 female S Small Miss Yes Yes
## 437 0 3 female S Large Miss Yes Yes
## 438 1 2 female S Large Mrs Yes Yes
## 439 0 1 male S Large Mr Yes No
## 440 0 2 male S Single Mr No No
## 441 1 2 female S Small Mrs Yes Yes
## 442 0 3 male S Single Mr No No
## 443 0 3 male S Small Mr No No
## 444 1 2 female S Single Miss No Yes
## 445 1 3 male S Single Mr No No
## 446 1 1 male S Small Master Yes Yes
## 447 1 2 female S Small Miss No Yes
## 448 1 1 male S Single Mr No No
## 449 1 3 female C Small Miss Yes Yes
## 450 1 1 male S Single Officer No No
## 451 0 2 male S Small Mr Yes No
## 452 0 3 male S Small Mr No No
## 453 0 1 male C Single Mr No No
## 454 1 1 male C Small Mr No No
## 455 0 3 male S Single Mr No No
## 456 1 3 male C Single Mr No No
## 457 0 1 male S Single Mr No No
## 458 1 1 female S Small Mrs No Yes
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## 461 1 1 male S Single Mr No No
## 462 0 3 male S Single Mr No No
## 463 0 1 male S Single Mr No No
## 464 0 2 male S Single Mr No No
## 465 0 3 male S Single Mr No No
## 466 0 3 male S Single Mr No No
## 467 0 2 male S Single Mr Yes No
## 468 0 1 male S Single Mr No No
## 469 0 3 male Q Single Mr No No
## 470 1 3 female C Small Miss Yes Yes
## 471 0 3 male S Single Mr No No
## 472 0 3 male S Single Mr No No
## 473 1 2 female S Small Mrs Yes Yes
## 474 1 2 female C Single Mrs No Yes
## 475 0 3 female S Single Miss No Yes
## 476 0 1 male S Single Mr No No
## 477 0 2 male S Small Mr No No
## 478 0 3 male S Small Mr No No
## 479 0 3 male S Single Mr No No
## 480 1 3 female S Small Miss No Yes
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## 482 0 2 male S Single Mr No No
## 483 0 3 male S Single Mr No No
## 484 1 3 female S Single Mrs No Yes
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## 488 0 1 male C Single Mr No No
## 489 0 3 male S Single Mr No No
## 490 1 3 male S Small Master Yes Yes
## 491 0 3 male S Small Mr No No
## 492 0 3 male S Single Mr No No
## 493 0 1 male S Single Mr No No
## 494 0 1 male C Single Mr No No
## 495 0 3 male S Single Mr No No
## 496 0 3 male C Single Mr No No
## 497 1 1 female C Small Miss No Yes
## 498 0 3 male S Single Mr No No
## 499 0 1 female S Small Mrs Yes Yes
## 500 0 3 male S Single Mr No No
## 501 0 3 male S Single Mr No Yes
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## 503 0 3 female Q Single Miss No Yes
## 504 0 3 female S Single Miss No Yes
## 505 1 1 female S Single Miss Yes Yes
## 506 0 1 male C Small Mr Yes No
## 507 1 2 female S Small Mrs Yes Yes
## 508 1 1 male S Single Mr No No
## 509 0 3 male S Single Mr Yes No
## 510 1 3 male S Single Mr Yes No
## 511 1 3 male Q Single Mr No No
## 512 0 3 male S Single Mr No No
## 513 1 1 male S Single Mr No No
## 514 1 1 female C Small Mrs No Yes
## 515 0 3 male S Single Mr No No
## 516 0 1 male S Single Mr No No
## 517 1 2 female S Single Mrs No Yes
## 518 0 3 male Q Single Mr Yes No
## 519 1 2 female S Small Mrs No Yes
## 520 0 3 male S Single Mr No No
## 521 1 1 female S Single Miss Yes Yes
## 522 0 3 male S Single Mr No No
## 523 0 3 male C Single Mr No No
## 524 1 1 female C Small Mrs No Yes
## 525 0 3 male C Single Mr No No
## 526 0 3 male Q Single Mr No No
## 527 1 2 female S Single Miss No Yes
## 528 0 1 male S Single Mr Yes No
## 529 0 3 male S Single Mr No No
## 530 0 2 male S Small Mr No No
## 531 1 2 female S Small Miss Yes Yes
## 532 0 3 male C Single Mr No No
## 533 0 3 male C Small Mr No Yes
## 534 1 3 female C Small Mrs Yes Yes
## 535 0 3 female S Single Miss No Yes
## 536 1 2 female S Small Miss Yes Yes
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## 538 1 1 female C Single Miss Yes Yes
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## 541 1 1 female S Small Miss No Yes
## 542 0 3 female S Large Miss Yes Yes
## 543 0 3 female S Large Miss Yes Yes
## 544 1 2 male S Small Mr No No
## 545 0 1 male C Small Mr Yes No
## 546 0 1 male S Single Mr No No
## 547 1 2 female S Small Mrs No Yes
## 548 1 2 male C Single Mr No No
## 549 0 3 male S Small Mr Yes No
## 550 1 2 male S Small Master Yes Yes
## 551 1 1 male C Small Mr Yes Yes
## 552 0 2 male S Single Mr No No
## 553 0 3 male Q Single Mr No No
## 554 1 3 male C Single Mr No No
## 555 1 3 female S Single Miss No Yes
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## 557 1 1 female C Small Royalty No Yes
## 558 0 1 male C Single Mr Yes No
## 559 1 1 female S Small Mrs Yes Yes
## 560 1 3 female S Small Mrs No Yes
## 561 0 3 male Q Single Mr No No
## 562 0 3 male S Single Mr No No
## 563 0 2 male S Single Mr No No
## 564 0 3 male S Single Mr No No
## 565 0 3 female S Single Miss No Yes
## 566 0 3 male S Small Mr Yes No
## 567 0 3 male S Single Mr No No
## 568 0 3 female S Large Mrs Yes Yes
## 569 0 3 male C Single Mr No No
## 570 1 3 male S Single Mr No No
## 571 1 2 male S Single Mr No No
## 572 1 1 female S Small Mrs No Yes
## 573 1 1 male S Single Mr No No
## 574 1 3 female Q Single Miss No Yes
## 575 0 3 male S Single Mr No Yes
## 576 0 3 male S Single Mr No No
## 577 1 2 female S Single Miss No Yes
## 578 1 1 female S Small Mrs No Yes
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## 580 1 3 male S Single Mr No No
## 581 1 2 female S Small Miss No Yes
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## 583 0 2 male S Single Mr No No
## 584 0 1 male C Single Mr No No
## 585 0 3 male C Single Mr No No
## 586 1 1 female S Small Miss Yes Yes
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## 588 1 1 male C Small Mr No No
## 589 0 3 male S Single Mr No No
## 590 0 3 male S Single Mr No No
## 591 0 3 male S Single Mr No No
## 592 1 1 female C Small Mrs No Yes
## 593 0 3 male S Single Mr No No
## 594 0 3 female Q Small Miss No Yes
## 595 0 2 male S Small Mr No No
## 596 0 3 male S Small Mr Yes No
## 597 1 2 female S Single Miss Yes Yes
## 598 0 3 male S Single Mr Yes No
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## 600 1 1 male C Small Royalty No No
## 601 1 2 female S Small Mrs No Yes
## 602 0 3 male S Single Mr No No
## 603 0 1 male S Single Mr No No
## 604 0 3 male S Single Mr No No
## 605 1 1 male C Single Mr No No
## 606 0 3 male S Small Mr No No
## 607 0 3 male S Single Mr No No
## 608 1 1 male S Single Mr No No
## 609 1 2 female C Small Mrs Yes Yes
## 610 1 1 female S Single Miss Yes Yes
## 611 0 3 female S Large Mrs Yes Yes
## 612 0 3 male S Single Mr No No
## 613 1 3 female Q Small Miss No Yes
## 614 0 3 male Q Single Mr No No
## 615 0 3 male S Single Mr No No
## 616 1 2 female S Small Miss Yes Yes
## 617 0 3 male S Small Mr Yes No
## 618 0 3 female S Small Mrs No Yes
## 619 1 2 female S Small Miss Yes Yes
## 620 0 2 male S Single Mr No No
## 621 0 3 male C Small Mr No No
## 622 1 1 male S Small Mr No No
## 623 1 3 male C Small Mr Yes No
## 624 0 3 male S Single Mr No No
## 625 0 3 male S Single Mr No No
## 626 0 1 male S Single Mr No No
## 627 0 2 male Q Single Officer No No
## 628 1 1 female S Single Miss Yes Yes
## 629 0 3 male S Single Mr No No
## 630 0 3 male Q Single Mr No No
## 631 1 1 male S Single Mr No No
## 632 0 3 male S Single Mr No No
## 633 1 1 male C Single Officer No No
## 634 0 1 male S Single Mr No No
## 635 0 3 female S Large Miss Yes Yes
## 636 1 2 female S Single Miss No Yes
## 637 0 3 male S Single Mr No No
## 638 0 2 male S Small Mr Yes No
## 639 0 3 female S Large Mrs Yes Yes
## 640 0 3 male S Small Mr No No
## 641 0 3 male S Single Mr No No
## 642 1 1 female C Single Miss No Yes
## 643 0 3 female S Large Miss Yes Yes
## 644 1 3 male S Single Mr Yes No
## 645 1 3 female C Small Miss Yes Yes
## 646 1 1 male C Small Mr Yes No
## 647 0 3 male S Single Mr No No
## 648 1 1 male C Single Officer No No
## 649 0 3 male S Single Mr No No
## 650 1 3 female S Single Miss No Yes
## 651 0 3 male S Single Mr No No
## 652 1 2 female S Small Miss No Yes
## 653 0 3 male S Single Mr No No
## 654 1 3 female Q Single Miss No Yes
## 655 0 3 female Q Single Miss No Yes
## 656 0 2 male S Small Mr Yes No
## 657 0 3 male S Single Mr No No
## 658 0 3 female Q Small Mrs No Yes
## 659 0 2 male S Single Mr No No
## 660 0 1 male C Small Mr Yes No
## 661 1 1 male S Small Officer No No
## 662 0 3 male C Single Mr No No
## 663 0 1 male S Single Mr No No
## 664 0 3 male S Single Mr No No
## 665 1 3 male S Small Mr No No
## 666 0 2 male S Small Mr Yes No
## 667 0 2 male S Single Mr No No
## 668 0 3 male S Single Mr No No
## 669 0 3 male S Single Mr No No
## 670 1 1 female S Small Mrs No Yes
## 671 1 2 female S Small Mrs Yes Yes
## 672 0 1 male S Small Mr No No
## 673 0 2 male S Single Mr No No
## 674 1 2 male S Single Mr No No
## 675 0 2 male S Single Mr No No
## 676 0 3 male S Single Mr No No
## 677 0 3 male S Single Mr No No
## 678 1 3 female S Single Miss No Yes
## 679 0 3 female S Large Mrs Yes Yes
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## 681 0 3 female Q Single Miss No Yes
## 682 1 1 male C Single Mr Yes No
## 683 0 3 male S Single Mr No No
## 684 0 3 male S Large Mr Yes Yes
## 685 0 2 male S Small Mr Yes No
## 686 0 2 male C Small Mr Yes No
## 687 0 3 male S Large Mr Yes Yes
## 688 0 3 male S Single Mr No No
## 689 0 3 male S Single Mr No No
## 690 1 1 female S Small Miss Yes Yes
## 691 1 1 male S Small Mr No No
## 692 1 3 female C Small Miss No Yes
## 693 1 3 male S Single Mr Yes No
## 694 0 3 male C Single Mr No No
## 695 0 1 male S Single Officer No No
## 696 0 2 male S Single Mr No No
## 697 0 3 male S Single Mr No No
## 698 1 3 female Q Single Miss No Yes
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## 700 0 3 male S Single Mr No No
## 701 1 1 female C Small Mrs Yes Yes
## 702 1 1 male S Single Mr No No
## 703 0 3 female C Small Miss No Yes
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## 705 0 3 male S Small Mr No No
## 706 0 2 male S Single Mr No No
## 707 1 2 female S Single Mrs No Yes
## 708 1 1 male S Single Mr No No
## 709 1 1 female S Single Miss Yes Yes
## 710 1 3 male C Small Master Yes Yes
## 711 1 1 female C Single Miss No Yes
## 712 0 1 male S Single Mr No No
## 713 1 1 male S Small Mr No No
## 714 0 3 male S Single Mr No No
## 715 0 2 male S Single Mr No No
## 716 0 3 male S Single Mr No No
## 717 1 1 female C Single Miss Yes Yes
## 718 1 2 female S Single Miss No Yes
## 719 0 3 male Q Single Mr No No
## 720 0 3 male S Single Mr No No
## 721 1 2 female S Small Miss Yes Yes
## 722 0 3 male S Small Mr No Yes
## 723 0 2 male S Single Mr No No
## 724 0 2 male S Single Mr No No
## 725 1 1 male S Small Mr No No
## 726 0 3 male S Single Mr No No
## 727 1 2 female S Small Mrs No Yes
## 728 1 3 female Q Single Miss No Yes
## 729 0 2 male S Small Mr No No
## 730 0 3 female S Small Miss No Yes
## 731 1 1 female S Single Miss Yes Yes
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## 733 0 2 male S Single Mr No No
## 734 0 2 male S Single Mr No No
## 735 0 2 male S Single Mr No No
## 736 0 3 male S Single Mr No No
## 737 0 3 female S Large Mrs Yes Yes
## 738 1 1 male C Single Mr Yes No
## 739 0 3 male S Single Mr No No
## 740 0 3 male S Single Mr No No
## 741 1 1 male S Single Mr No No
## 742 0 1 male S Small Mr Yes No
## 743 1 1 female C Large Miss Yes Yes
## 744 0 3 male S Small Mr No No
## 745 1 3 male S Single Mr No No
## 746 0 1 male S Small Officer No No
## 747 0 3 male S Small Mr Yes Yes
## 748 1 2 female S Single Miss No Yes
## 749 0 1 male S Small Mr No No
## 750 0 3 male Q Single Mr No No
## 751 1 2 female S Small Miss Yes Yes
## 752 1 3 male S Small Master No Yes
## 753 0 3 male S Single Mr No No
## 754 0 3 male S Single Mr No No
## 755 1 2 female S Small Mrs Yes Yes
## 756 1 2 male S Small Master No Yes
## 757 0 3 male S Single Mr No No
## 758 0 2 male S Single Mr No No
## 759 0 3 male S Single Mr No No
## 760 1 1 female S Single Royalty Yes Yes
## 761 0 3 male S Single Mr No No
## 762 0 3 male S Single Mr No No
## 763 1 3 male C Single Mr No No
## 764 1 1 female S Small Mrs Yes Yes
## 765 0 3 male S Single Mr No Yes
## 766 1 1 female S Small Mrs Yes Yes
## 767 0 1 male C Single Officer No No
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## 769 0 3 male Q Small Mr Yes No
## 770 0 3 male S Single Mr No No
## 771 0 3 male S Single Mr No No
## 772 0 3 male S Single Mr No No
## 773 0 2 female S Single Mrs No Yes
## 774 0 3 male C Single Mr No No
## 775 1 2 female S Large Mrs No Yes
## 776 0 3 male S Single Mr No No
## 777 0 3 male Q Single Mr No No
## 778 1 3 female S Single Miss No Yes
## 779 0 3 male Q Single Mr No No
## 780 1 1 female S Small Mrs Yes Yes
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## 782 1 1 female S Small Mrs No Yes
## 783 0 1 male S Single Mr No No
## 784 0 3 male S Small Mr Yes No
## 785 0 3 male S Single Mr No No
## 786 0 3 male S Single Mr No No
## 787 1 3 female S Single Miss No Yes
## 788 0 3 male Q Large Master Yes Yes
## 789 1 3 male S Small Master Yes Yes
## 790 0 1 male C Single Mr No No
## 791 0 3 male Q Single Mr No No
## 792 0 2 male S Single Mr No Yes
## 793 0 3 female S Large Miss Yes Yes
## 794 0 1 male C Single Mr No No
## 795 0 3 male S Single Mr No No
## 796 0 2 male S Single Mr No No
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## 800 0 3 female S Small Mrs Yes Yes
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## 802 1 2 female S Small Mrs Yes Yes
## 803 1 1 male S Small Master Yes Yes
## 804 1 3 male C Small Master No Yes
## 805 1 3 male S Single Mr No No
## 806 0 3 male S Single Mr No No
## 807 0 1 male S Single Mr No No
## 808 0 3 female S Single Miss No Yes
## 809 0 2 male S Single Mr No No
## 810 1 1 female S Small Mrs No Yes
## 811 0 3 male S Single Mr No No
## 812 0 3 male S Single Mr Yes No
## 813 0 2 male S Single Mr No No
## 814 0 3 female S Large Miss Yes Yes
## 815 0 3 male S Single Mr No No
## 816 0 1 male S Single Mr No No
## 817 0 3 female S Single Miss No Yes
## 818 0 2 male C Small Mr Yes No
## 819 0 3 male S Single Mr No No
## 820 0 3 male S Large Master Yes Yes
## 821 1 1 female S Small Mrs Yes Yes
## 822 1 3 male S Single Mr No No
## 823 0 1 male S Single Royalty No No
## 824 1 3 female S Small Mrs No Yes
## 825 0 3 male S Large Master Yes Yes
## 826 0 3 male Q Single Mr No No
## 827 0 3 male S Single Mr Yes No
## 828 1 2 male C Small Master Yes Yes
## 829 1 3 male Q Single Mr No No
## 830 1 1 female C Single Mrs No Yes
## 831 1 3 female C Small Mrs No Yes
## 832 1 2 male S Small Master Yes Yes
## 833 0 3 male C Single Mr No No
## 834 0 3 male S Single Mr No No
## 835 0 3 male S Single Mr No No
## 836 1 1 female C Small Miss Yes Yes
## 837 0 3 male S Single Mr No No
## 838 0 3 male S Single Mr No No
## 839 1 3 male S Single Mr Yes No
## 840 1 1 male C Single Mr No No
## 841 0 3 male S Single Mr No No
## 842 0 2 male S Single Mr No Yes
## 843 1 1 female C Single Miss No Yes
## 844 0 3 male C Single Mr No No
## 845 0 3 male S Single Mr No Yes
## 846 0 3 male S Single Mr No No
## 847 0 3 male S Large Mr Yes No
## 848 0 3 male C Single Mr No No
## 849 0 2 male S Small Officer Yes No
## 850 1 1 female C Small Mrs No Yes
## 851 0 3 male S Large Master Yes Yes
## 852 0 3 male S Single Mr No No
## 853 0 3 female C Small Miss Yes Yes
## 854 1 1 female S Small Miss No Yes
## 855 0 2 female S Small Mrs No Yes
## 856 1 3 female S Small Mrs No Yes
## 857 1 1 female S Small Mrs Yes Yes
## 858 1 1 male S Single Mr No No
## 859 1 3 female C Small Mrs Yes Yes
## 860 0 3 male C Single Mr No No
## 861 0 3 male S Small Mr No No
## 862 0 2 male S Small Mr No No
## 863 1 1 female S Single Mrs No Yes
## 864 0 3 female S Large Miss Yes Yes
## 865 0 2 male S Single Mr No No
## 866 1 2 female S Single Mrs No Yes
## 867 1 2 female C Small Miss No Yes
## 868 0 1 male S Single Mr No No
## 869 0 3 male S Single Mr No No
## 870 1 3 male S Small Master Yes Yes
## 871 0 3 male S Single Mr No No
## 872 1 1 female S Small Mrs No Yes
## 873 0 1 male S Single Mr No No
## 874 0 3 male S Single Mr No No
## 875 1 2 female C Small Mrs No Yes
## 876 1 3 female C Single Miss No Yes
## 877 0 3 male S Single Mr No No
## 878 0 3 male S Single Mr No No
## 879 0 3 male S Single Mr No No
## 880 1 1 female C Small Mrs Yes Yes
## 881 1 2 female S Small Mrs No Yes
## 882 0 3 male S Single Mr No No
## 883 0 3 female S Single Miss No Yes
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## 885 0 3 male S Single Mr No No
## 886 0 3 female Q Large Mrs Yes Yes
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## 889 0 3 female S Small Miss Yes Yes
## 890 1 1 male C Single Mr No No
## 891 0 3 male Q Single Mr No No
## 892 <NA> 3 male Q Single Mr No No
## 893 <NA> 3 female S Small Mrs No Yes
## 894 <NA> 2 male Q Single Mr No No
## 895 <NA> 3 male S Single Mr No No
## 896 <NA> 3 female S Small Mrs No Yes
## 897 <NA> 3 male S Single Mr No Yes
## 898 <NA> 3 female Q Single Miss No Yes
## 899 <NA> 2 male S Small Mr Yes No
## 900 <NA> 3 female C Single Mrs No Yes
## 901 <NA> 3 male S Small Mr Yes No
## 902 <NA> 3 male S Single Mr No No
## 903 <NA> 1 male S Single Mr No No
## 904 <NA> 1 female S Small Mrs No Yes
## 905 <NA> 2 male S Small Mr No No
## 906 <NA> 1 female S Small Mrs No Yes
## 907 <NA> 2 female C Small Mrs No Yes
## 908 <NA> 2 male Q Single Mr No No
## 909 <NA> 3 male C Single Mr No No
## 910 <NA> 3 female S Small Miss No Yes
## 911 <NA> 3 female C Single Mrs No Yes
## 912 <NA> 1 male C Small Mr No No
## 913 <NA> 3 male S Small Master No Yes
## 914 <NA> 1 female S Single Mrs No Yes
## 915 <NA> 1 male C Small Mr No No
## 916 <NA> 1 female C Large Mrs Yes Yes
## 917 <NA> 3 male S Small Mr No No
## 918 <NA> 1 female C Small Miss No Yes
## 919 <NA> 3 male C Single Mr No No
## 920 <NA> 1 male S Single Mr No No
## 921 <NA> 3 male C Small Mr Yes No
## 922 <NA> 2 male S Small Mr No No
## 923 <NA> 2 male S Small Mr Yes No
## 924 <NA> 3 female S Small Mrs Yes Yes
## 925 <NA> 3 female S Small Mrs Yes Yes
## 926 <NA> 1 male C Small Mr No No
## 927 <NA> 3 male C Single Mr No No
## 928 <NA> 3 female S Single Miss No Yes
## 929 <NA> 3 female S Single Miss No Yes
## 930 <NA> 3 male S Single Mr No No
## 931 <NA> 3 male S Single Mr Yes No
## 932 <NA> 3 male C Small Mr No No
## 933 <NA> 1 male S Single Mr No No
## 934 <NA> 3 male S Single Mr No No
## 935 <NA> 2 female S Single Mrs No Yes
## 936 <NA> 1 female S Small Mrs No Yes
## 937 <NA> 3 male S Single Mr No No
## 938 <NA> 1 male C Single Mr No No
## 939 <NA> 3 male Q Single Mr No No
## 940 <NA> 1 female C Single Mrs No Yes
## 941 <NA> 3 female S Small Mrs Yes Yes
## 942 <NA> 1 male S Small Mr No No
## 943 <NA> 2 male C Single Mr No No
## 944 <NA> 2 female S Small Miss No Yes
## 945 <NA> 1 female S Large Miss Yes Yes
## 946 <NA> 2 male C Single Mr No No
## 947 <NA> 3 male Q Large Master Yes Yes
## 948 <NA> 3 male S Single Mr No No
## 949 <NA> 3 male S Single Mr No No
## 950 <NA> 3 male S Small Mr No No
## 951 <NA> 1 female C Single Miss Yes Yes
## 952 <NA> 3 male S Single Mr No Yes
## 953 <NA> 2 male S Single Mr No No
## 954 <NA> 3 male S Single Mr No No
## 955 <NA> 3 female Q Single Miss No Yes
## 956 <NA> 1 male C Large Master Yes Yes
## 957 <NA> 2 female S Single Mrs No Yes
## 958 <NA> 3 female Q Single Miss No Yes
## 959 <NA> 1 male S Single Mr No No
## 960 <NA> 1 male C Single Mr No No
## 961 <NA> 1 female S Large Mrs Yes Yes
## 962 <NA> 3 female Q Single Miss No Yes
## 963 <NA> 3 male S Single Mr No No
## 964 <NA> 3 female S Single Miss No Yes
## 965 <NA> 1 male C Single Mr No No
## 966 <NA> 1 female C Single Miss Yes Yes
## 967 <NA> 1 male C Single Mr Yes No
## 968 <NA> 3 male S Single Mr No No
## 969 <NA> 1 female S Small Mrs No Yes
## 970 <NA> 2 male S Single Mr No No
## 971 <NA> 3 female Q Single Miss No Yes
## 972 <NA> 3 male C Small Master Yes Yes
## 973 <NA> 1 male S Small Mr Yes No
## 974 <NA> 1 male S Single Mr No No
## 975 <NA> 3 male S Single Mr No No
## 976 <NA> 2 male Q Single Mr No No
## 977 <NA> 3 male C Small Mr No No
## 978 <NA> 3 female Q Single Miss No Yes
## 979 <NA> 3 female S Single Miss No Yes
## 980 <NA> 3 female Q Single Miss No Yes
## 981 <NA> 2 male S Small Master Yes Yes
## 982 <NA> 3 female S Small Mrs No Yes
## 983 <NA> 3 male S Single Mr No No
## 984 <NA> 1 female S Small Mrs No Yes
## 985 <NA> 3 male S Single Mr No No
## 986 <NA> 1 male C Single Mr No No
## 987 <NA> 3 male S Single Mr No No
## 988 <NA> 1 female S Small Mrs Yes Yes
## 989 <NA> 3 male S Single Mr No No
## 990 <NA> 3 female S Single Miss No Yes
## 991 <NA> 3 male S Single Mr No No
## 992 <NA> 1 female C Small Mrs No Yes
## 993 <NA> 2 male S Small Mr No No
## 994 <NA> 3 male Q Single Mr No No
## 995 <NA> 3 male S Single Mr No No
## 996 <NA> 3 female C Small Mrs No Yes
## 997 <NA> 3 male S Single Mr Yes No
## 998 <NA> 3 male Q Single Mr No No
## 999 <NA> 3 male Q Single Mr No No
## 1000 <NA> 3 male S Single Mr No No
## 1001 <NA> 2 male S Single Mr No No
## 1002 <NA> 2 male C Single Mr No No
## 1003 <NA> 3 female Q Single Miss No Yes
## 1004 <NA> 1 female C Single Miss No Yes
## 1005 <NA> 3 female Q Single Miss No Yes
## 1006 <NA> 1 female S Small Mrs Yes Yes
## 1007 <NA> 3 male C Small Mr No No
## 1008 <NA> 3 male C Single Mr No No
## 1009 <NA> 3 female S Small Miss Yes Yes
## 1010 <NA> 1 male C Single Mr No No
## 1011 <NA> 2 female S Small Mrs No Yes
## 1012 <NA> 2 female S Single Miss No Yes
## 1013 <NA> 3 male Q Small Mr No No
## 1014 <NA> 1 female C Small Mrs No Yes
## 1015 <NA> 3 male S Single Mr No No
## 1016 <NA> 3 male Q Single Mr No No
## 1017 <NA> 3 female S Small Miss No Yes
## 1018 <NA> 3 male S Single Mr No No
## 1019 <NA> 3 female Q Small Miss Yes Yes
## 1020 <NA> 2 male S Single Mr No No
## 1021 <NA> 3 male S Single Mr No No
## 1022 <NA> 3 male S Single Mr No No
## 1023 <NA> 1 male C Single Officer No No
## 1024 <NA> 3 female S Large Mrs Yes Yes
## 1025 <NA> 3 male C Small Mr No No
## 1026 <NA> 3 male S Single Mr No No
## 1027 <NA> 3 male S Single Mr No No
## 1028 <NA> 3 male C Single Mr No No
## 1029 <NA> 2 male S Single Mr No No
## 1030 <NA> 3 female S Single Miss No Yes
## 1031 <NA> 3 male S Large Mr Yes No
## 1032 <NA> 3 female S Large Miss Yes Yes
## 1033 <NA> 1 female S Single Miss Yes Yes
## 1034 <NA> 1 male C Large Mr Yes No
## 1035 <NA> 2 male S Single Mr No No
## 1036 <NA> 1 male S Single Mr No No
## 1037 <NA> 3 male S Small Mr No No
## 1038 <NA> 1 male S Single Mr No No
## 1039 <NA> 3 male S Single Mr No No
## 1040 <NA> 1 male S Single Mr No No
## 1041 <NA> 2 male S Small Officer No No
## 1042 <NA> 1 female C Small Mrs Yes Yes
## 1043 <NA> 3 male C Single Mr No No
## 1044 <NA> 3 male S Single Mr No No
## 1045 <NA> 3 female S Small Mrs No Yes
## 1046 <NA> 3 male S Large Master Yes Yes
## 1047 <NA> 3 male S Single Mr No No
## 1048 <NA> 1 female S Single Miss Yes Yes
## 1049 <NA> 3 female S Single Miss No Yes
## 1050 <NA> 1 male S Single Mr No No
## 1051 <NA> 3 female S Small Mrs Yes Yes
## 1052 <NA> 3 female Q Single Miss No Yes
## 1053 <NA> 3 male C Small Master Yes Yes
## 1054 <NA> 2 female S Single Miss No Yes
## 1055 <NA> 3 male S Single Mr No No
## 1056 <NA> 2 male S Single Officer No No
## 1057 <NA> 3 female S Small Mrs Yes Yes
## 1058 <NA> 1 male C Single Mr No No
## 1059 <NA> 3 male S Large Mr Yes No
## 1060 <NA> 1 female C Single Mrs No Yes
## 1061 <NA> 3 female S Single Miss No Yes
## 1062 <NA> 3 male S Single Mr No No
## 1063 <NA> 3 male C Single Mr No No
## 1064 <NA> 3 male S Small Mr No No
## 1065 <NA> 3 male C Single Mr No No
## 1066 <NA> 3 male S Large Mr Yes No
## 1067 <NA> 2 female S Small Miss Yes Yes
## 1068 <NA> 2 female S Single Miss Yes Yes
## 1069 <NA> 1 male C Small Mr No No
## 1070 <NA> 2 female S Small Mrs Yes Yes
## 1071 <NA> 1 female C Small Mrs Yes Yes
## 1072 <NA> 2 male S Single Mr No No
## 1073 <NA> 1 male C Small Mr Yes No
## 1074 <NA> 1 female S Small Mrs No Yes
## 1075 <NA> 3 male Q Single Mr No No
## 1076 <NA> 1 female C Small Mrs Yes Yes
## 1077 <NA> 2 male S Single Mr No No
## 1078 <NA> 2 female S Small Miss No Yes
## 1079 <NA> 3 male S Small Mr No Yes
## 1080 <NA> 3 female S Large Miss Yes Yes
## 1081 <NA> 2 male S Single Mr No No
## 1082 <NA> 2 male S Small Mr No No
## 1083 <NA> 1 male S Single Mr No No
## 1084 <NA> 3 male S Small Master Yes Yes
## 1085 <NA> 2 male Q Single Mr No No
## 1086 <NA> 2 male S Small Master Yes Yes
## 1087 <NA> 3 male S Single Mr No No
## 1088 <NA> 1 male C Small Master Yes Yes
## 1089 <NA> 3 female S Single Miss No Yes
## 1090 <NA> 2 male S Single Mr No No
## 1091 <NA> 3 female S Single Mrs No Yes
## 1092 <NA> 3 female Q Single Miss No Yes
## 1093 <NA> 3 male S Small Master Yes Yes
## 1094 <NA> 1 male C Small Officer Yes No
## 1095 <NA> 2 female S Small Miss Yes Yes
## 1096 <NA> 2 male S Single Mr No No
## 1097 <NA> 1 male C Single Mr No No
## 1098 <NA> 3 female Q Single Miss No Yes
## 1099 <NA> 2 male S Single Mr No No
## 1100 <NA> 1 female C Single Miss No Yes
## 1101 <NA> 3 male S Single Mr No No
## 1102 <NA> 3 male S Single Mr Yes No
## 1103 <NA> 3 male S Single Mr No No
## 1104 <NA> 2 male S Single Mr Yes Yes
## 1105 <NA> 2 female S Small Mrs No Yes
## 1106 <NA> 3 female S Large Miss No Yes
## 1107 <NA> 1 male S Single Mr No No
## 1108 <NA> 3 female Q Single Miss No Yes
## 1109 <NA> 1 male S Small Mr Yes No
## 1110 <NA> 1 female C Small Mrs Yes Yes
## 1111 <NA> 3 male S Single Mr No No
## 1112 <NA> 2 female C Small Miss No Yes
## 1113 <NA> 3 male S Single Mr No No
## 1114 <NA> 2 female S Single Mrs No Yes
## 1115 <NA> 3 male S Single Mr No No
## 1116 <NA> 1 female C Single Mrs No Yes
## 1117 <NA> 3 female C Small Mrs Yes Yes
## 1118 <NA> 3 male S Single Mr No No
## 1119 <NA> 3 female Q Single Miss No Yes
## 1120 <NA> 3 male S Single Mr No No
## 1121 <NA> 2 male S Single Mr No No
## 1122 <NA> 2 male S Single Mr Yes Yes
## 1123 <NA> 1 female S Single Miss No Yes
## 1124 <NA> 3 male S Small Mr No No
## 1125 <NA> 3 male Q Single Mr No No
## 1126 <NA> 1 male C Small Mr No No
## 1127 <NA> 3 male S Single Mr No No
## 1128 <NA> 1 male C Small Mr No No
## 1129 <NA> 3 male C Single Mr No No
## 1130 <NA> 2 female S Small Miss No Yes
## 1131 <NA> 1 female C Small Mrs Yes Yes
## 1132 <NA> 1 female C Single Mrs No Yes
## 1133 <NA> 2 female S Small Mrs No Yes
## 1134 <NA> 1 male C Small Mr Yes No
## 1135 <NA> 3 male S Single Mr No No
## 1136 <NA> 3 male S Small Master Yes Yes
## 1137 <NA> 1 male S Small Mr No No
## 1138 <NA> 2 female S Single Mrs No Yes
## 1139 <NA> 2 male S Small Mr Yes No
## 1140 <NA> 2 female S Small Mrs No Yes
## 1141 <NA> 3 female C Small Mrs No Yes
## 1142 <NA> 2 female S Small Miss Yes Yes
## 1143 <NA> 3 male S Single Mr No No
## 1144 <NA> 1 male C Small Mr No No
## 1145 <NA> 3 male S Single Mr No No
## 1146 <NA> 3 male S Single Mr No No
## 1147 <NA> 3 male S Single Mr No No
## 1148 <NA> 3 male Q Single Mr No No
## 1149 <NA> 3 male S Single Mr No No
## 1150 <NA> 2 female S Single Miss No Yes
## 1151 <NA> 3 male S Single Mr No No
## 1152 <NA> 3 male S Small Mr No No
## 1153 <NA> 3 male S Single Mr No No
## 1154 <NA> 2 female S Small Mrs Yes Yes
## 1155 <NA> 3 female S Small Miss No Yes
## 1156 <NA> 2 male C Single Mr No No
## 1157 <NA> 3 male S Single Mr No No
## 1158 <NA> 1 male S Single Mr No No
## 1159 <NA> 3 male S Single Mr No No
## 1160 <NA> 3 female S Single Miss No Yes
## 1161 <NA> 3 male S Single Mr No Yes
## 1162 <NA> 1 male C Single Mr No No
## 1163 <NA> 3 male Q Single Mr No No
## 1164 <NA> 1 female C Small Mrs No Yes
## 1165 <NA> 3 female Q Small Miss No Yes
## 1166 <NA> 3 male C Single Mr No No
## 1167 <NA> 2 female S Small Miss No Yes
## 1168 <NA> 2 male S Single Mr No No
## 1169 <NA> 2 male S Small Mr No No
## 1170 <NA> 2 male S Small Mr No No
## 1171 <NA> 2 male S Single Mr No No
## 1172 <NA> 3 female S Single Miss No Yes
## 1173 <NA> 3 male S Small Master Yes Yes
## 1174 <NA> 3 female Q Single Miss No Yes
## 1175 <NA> 3 female C Small Miss Yes Yes
## 1176 <NA> 3 female S Small Miss Yes Yes
## 1177 <NA> 3 male S Single Mr No No
## 1178 <NA> 3 male S Single Mr No No
## 1179 <NA> 1 male S Small Mr No No
## 1180 <NA> 3 male C Single Mr No No
## 1181 <NA> 3 male S Single Mr No No
## 1182 <NA> 1 male S Single Mr No No
## 1183 <NA> 3 female Q Single Miss No Yes
## 1184 <NA> 3 male C Single Mr No No
## 1185 <NA> 1 male S Small Officer Yes No
## 1186 <NA> 3 male S Single Mr No No
## 1187 <NA> 3 male S Single Mr No No
## 1188 <NA> 2 female C Small Miss Yes Yes
## 1189 <NA> 3 male C Small Mr Yes No
## 1190 <NA> 1 male S Single Mr No No
## 1191 <NA> 3 male S Single Mr No No
## 1192 <NA> 3 male S Single Mr No No
## 1193 <NA> 2 male C Single Mr No No
## 1194 <NA> 2 male S Small Mr No No
## 1195 <NA> 3 male S Single Mr No No
## 1196 <NA> 3 female Q Single Miss No Yes
## 1197 <NA> 1 female S Small Mrs No Yes
## 1198 <NA> 1 male S Small Mr Yes No
## 1199 <NA> 3 male S Small Master No Yes
## 1200 <NA> 1 male S Small Mr Yes No
## 1201 <NA> 3 female S Small Mrs No Yes
## 1202 <NA> 3 male S Single Mr No No
## 1203 <NA> 3 male C Single Mr No No
## 1204 <NA> 3 male S Single Mr No No
## 1205 <NA> 3 female Q Single Miss No Yes
## 1206 <NA> 1 female C Single Mrs Yes Yes
## 1207 <NA> 3 female Q Single Miss No Yes
## 1208 <NA> 1 male C Small Mr Yes No
## 1209 <NA> 2 male S Single Mr No No
## 1210 <NA> 3 male S Single Mr No No
## 1211 <NA> 2 male S Small Mr Yes No
## 1212 <NA> 3 male S Single Mr No No
## 1213 <NA> 3 male C Single Mr No No
## 1214 <NA> 2 male S Single Mr No No
## 1215 <NA> 1 male S Single Mr No No
## 1216 <NA> 1 female S Single Miss Yes Yes
## 1217 <NA> 3 male S Single Mr No No
## 1218 <NA> 2 female S Small Miss Yes Yes
## 1219 <NA> 1 male C Single Mr No No
## 1220 <NA> 2 male S Small Mr No No
## 1221 <NA> 2 male S Single Mr No No
## 1222 <NA> 2 female S Small Mrs Yes Yes
## 1223 <NA> 1 male C Single Mr No No
## 1224 <NA> 3 male C Single Mr No No
## 1225 <NA> 3 female C Small Mrs Yes Yes
## 1226 <NA> 3 male S Single Mr No No
## 1227 <NA> 1 male S Single Mr No No
## 1228 <NA> 2 male S Single Mr No No
## 1229 <NA> 3 male C Small Mr No No
## 1230 <NA> 2 male S Single Mr Yes No
## 1231 <NA> 3 male C Single Master No Yes
## 1232 <NA> 2 male S Single Mr No No
## 1233 <NA> 3 male S Single Mr No No
## 1234 <NA> 3 male S Large Mr Yes No
## 1235 <NA> 1 female C Small Mrs Yes Yes
## 1236 <NA> 3 male S Small Master Yes Yes
## 1237 <NA> 3 female S Single Miss No Yes
## 1238 <NA> 2 male S Single Mr No No
## 1239 <NA> 3 female C Single Mrs No Yes
## 1240 <NA> 2 male S Single Mr No No
## 1241 <NA> 2 female S Single Miss No Yes
## 1242 <NA> 1 female C Small Mrs No Yes
## 1243 <NA> 2 male S Single Mr No No
## 1244 <NA> 2 male S Single Mr Yes No
## 1245 <NA> 2 male S Small Mr Yes No
## 1246 <NA> 3 female S Small Miss Yes Yes
## 1247 <NA> 1 male S Single Mr No No
## 1248 <NA> 1 female S Small Mrs No Yes
## 1249 <NA> 3 male S Single Mr No No
## 1250 <NA> 3 male Q Single Mr No No
## 1251 <NA> 3 female S Small Mrs No Yes
## 1252 <NA> 3 male S Large Master Yes Yes
## 1253 <NA> 2 female C Small Mrs Yes Yes
## 1254 <NA> 2 female S Single Mrs No Yes
## 1255 <NA> 3 male S Single Mr No No
## 1256 <NA> 1 female C Small Mrs No Yes
## 1257 <NA> 3 female S Large Mrs Yes Yes
## 1258 <NA> 3 male C Small Mr No No
## 1259 <NA> 3 female S Single Miss Yes Yes
## 1260 <NA> 1 female C Small Mrs No Yes
## 1261 <NA> 2 male C Single Mr No No
## 1262 <NA> 2 male S Small Mr No No
## 1263 <NA> 1 female C Single Miss Yes Yes
## 1264 <NA> 1 male S Single Mr No No
## 1265 <NA> 2 male S Single Mr No No
## 1266 <NA> 1 female S Small Mrs Yes Yes
## 1267 <NA> 1 female C Single Miss Yes Yes
## 1268 <NA> 3 female S Small Miss No Yes
## 1269 <NA> 2 male S Single Mr No No
## 1270 <NA> 1 male S Single Mr No No
## 1271 <NA> 3 male S Large Master Yes Yes
## 1272 <NA> 3 male Q Single Mr No No
## 1273 <NA> 3 male Q Single Mr No No
## 1274 <NA> 3 female S Single Mrs No Yes
## 1275 <NA> 3 female S Small Mrs No Yes
## 1276 <NA> 2 male S Single Mr No No
## 1277 <NA> 2 female S Small Miss Yes Yes
## 1278 <NA> 3 male S Single Mr No No
## 1279 <NA> 2 male S Single Mr No No
## 1280 <NA> 3 male Q Single Mr No No
## 1281 <NA> 3 male S Large Master Yes Yes
## 1282 <NA> 1 male S Single Mr Yes No
## 1283 <NA> 1 female S Small Mrs No Yes
## 1284 <NA> 3 male S Small Master Yes Yes
## 1285 <NA> 2 male S Single Mr No No
## 1286 <NA> 3 male S Large Mr Yes No
## 1287 <NA> 1 female S Small Mrs No Yes
## 1288 <NA> 3 male Q Single Mr No No
## 1289 <NA> 1 female C Small Mrs No Yes
## 1290 <NA> 3 male S Single Mr No No
## 1291 <NA> 3 male Q Single Mr No No
## 1292 <NA> 1 female S Single Miss Yes Yes
## 1293 <NA> 2 male S Small Mr No No
## 1294 <NA> 1 female C Small Miss No Yes
## 1295 <NA> 1 male S Single Mr No Yes
## 1296 <NA> 1 male C Small Mr No No
## 1297 <NA> 2 male C Single Mr No No
## 1298 <NA> 2 male S Small Mr No No
## 1299 <NA> 1 male C Small Mr Yes No
## 1300 <NA> 3 female Q Single Miss No Yes
## 1301 <NA> 3 female S Small Miss Yes Yes
## 1302 <NA> 3 female Q Single Miss No Yes
## 1303 <NA> 1 female Q Small Mrs Yes Yes
## 1304 <NA> 3 female S Single Miss No Yes
## 1305 <NA> 3 male S Single Mr No No
## 1306 <NA> 1 female C Single Royalty Yes Yes
## 1307 <NA> 3 male S Single Mr No No
## 1308 <NA> 3 male S Single Mr No No
## 1309 <NA> 3 male C Small Master Yes Yeslibrary("makedummies")## Warning: package 'makedummies' was built under R version 3.6.2# makedummies()を使用してダミー変数を作成
dummy_var <- makedummies(dum, basal_level = FALSE)
# 結合する
cordata <- cbind(dummy_var, not_dum)
head(cordata)## Survived Pclass_2 Pclass_3 Sex Embarked_Q Embarked_S DFamsize_Single
## 1 0 0 1 1 0 1 0
## 2 1 0 0 0 0 0 0
## 3 1 0 1 0 0 1 1
## 4 1 0 0 0 0 1 0
## 5 0 0 1 1 0 1 1
## 6 0 0 1 1 1 0 1
## DFamsize_Small Title_Miss Title_Mr Title_Mrs Title_Officer Title_Royalty
## 1 1 0 1 0 0 0
## 2 1 0 0 1 0 0
## 3 0 1 0 0 0 0
## 4 1 0 0 1 0 0
## 5 0 0 1 0 0 0
## 6 0 0 1 0 0 0
## Group Wom_chd PassengerId Name
## 1 0 0 1 Braund, Mr. Owen Harris
## 2 0 1 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer)
## 3 0 1 3 Heikkinen, Miss. Laina
## 4 0 1 4 Futrelle, Mrs. Jacques Heath (Lily May Peel)
## 5 0 0 5 Allen, Mr. William Henry
## 6 0 0 6 Moran, Mr. James
## Age SibSp Parch Ticket Fare Cabin Famsize
## 1 22 1 0 A/5 21171 7.2500 <NA> 2
## 2 38 1 0 PC 17599 71.2833 C85 2
## 3 26 0 0 STON/O2. 3101282 7.9250 <NA> 1
## 4 35 1 0 113803 53.1000 C123 2
## 5 35 0 0 373450 8.0500 <NA> 1
## 6 29 0 0 330877 8.4583 <NA> 1相関行列の表示
cordata_ver1 <- cordata %>% dplyr::select(Pclass_2,Pclass_3, Sex, Age, Fare, Embarked_Q,Embarked_S,DFamsize_Single, DFamsize_Small, Title_Miss, Title_Mr,Title_Mrs, Title_Officer, Title_Royalty)
factor_vars <- c('Pclass_2','Pclass_3', 'Sex', 'Age', 'Fare', 'Embarked_Q', 'Embarked_S','DFamsize_Single', 'DFamsize_Small', 'Title_Miss', 'Title_Mr', 'Title_Mrs','Title_Officer', 'Title_Royalty')
cordata_ver1[factor_vars] <- lapply(cordata_ver1[factor_vars], function(x) as.numeric(x))
cormat1 <- cor(cordata_ver1)
library("corrplot")## Warning: package 'corrplot' was built under R version 3.6.2## corrplot 0.84 loadedcorrplot(cormat1,method="circle",,numbers=T)## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt =
## tl.srt, : "numbers" はグラフィックスパラメータではありません## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col =
## tl.col, : "numbers" はグラフィックスパラメータではありません## Warning in title(title, ...): "numbers" はグラフィックスパラメータではありません
データセットの分割(◆■原文ではcordatagがalldataになっており間違い）
- cordataをもとのtrainデータとtestデータに分割(◆■原文ではalldataになっており間違い）
# cordataをもとのtrainデータとtestデータに分割
train <- cordata[1:891,]
test <- cordata[892:1309,]説明変数formulaの作成（1回目）
# 説明変数formulaの作成
n <- names(train)
n <- n[-14][-15][-14:-15][-15:-17][-16:-18] # 推定に使用しないカラム名を取り除く
formula_train <- as.formula(paste("Survived~",paste(n[!n%in%c("Survived")],collapse="+")))
Survived ~ Pclass_2 + Pclass_3 + Sex + Embarked_Q + Embarked_S +
Dfamsize_Single + Dfamsize_Small + Title_Miss + Title_Mr +
Title_Mrs + Title_Officer + Title_Royalty + Age + Fare## Survived ~ Pclass_2 + Pclass_3 + Sex + Embarked_Q + Embarked_S +
## Dfamsize_Single + Dfamsize_Small + Title_Miss + Title_Mr +
## Title_Mrs + Title_Officer + Title_Royalty + Age + Farelibrary(car)#carライブラリ読み込みVIFの確認
#VIFの確認:VIF / Variance Inflation Factor:
#独立変数間の多重共線性を検出するための指標の1つ。独立変数間の相関係数行列の逆行列の対角要素であり、値が大きい場
#合はその変数を分析から除いた方がよいと考えられる。10を基準とすることが多い。
#関数 glm() を用いることで一般化線形モデルを扱うことが出来る．
#http://cse.naro.affrc.go.jp/takezawa/r-tips/r/72.html
#関数 vif()で
#carライブラリにvif()という関数がある。
#https://toukeier.hatenablog.com/entry/how-to-calculate-vif-by-r/
#car::vif(data_vif_ver1)は、パッケージcarの場合に、car::vif(data_vif_ver1)は名前空間carエクスポートされた変数vif(data_vif_ver1)の値を返す。
#https://stat.ethz.ch/R-manual/R-devel/library/base/html/ns-dblcolon.html
data_vif_ver1 <- glm(formula_train,family=binomial(link='logit'),data=train)
car::vif(data_vif_ver1) ## Pclass_2 Pclass_3 Sex Embarked_Q Embarked_S
## 2.018606e+00 2.887521e+00 6.305522e+06 1.597044e+00 1.528755e+00
## DFamsize_Single DFamsize_Small Title_Miss Title_Mr Title_Mrs
## 7.014843e+00 5.768453e+00 5.062867e+06 9.332808e+00 3.037016e+06
## Title_Officer Title_Royalty Age Fare
## 2.072341e+00 1.187356e+00 1.879358e+00 1.723444e+00説明変数formulaの作成(2回目)）Sex変数を除い
- 1回目の確認でVIFが10を超えている変数はSex他いくつかあった。
- 1回目の確認dでTitle_Mrが限りなく10に近く怪しいがすべての変数が10以下なので，この説明変数を使用してモデルを推定する(VIFが10以下はあくまで目安?)
# 説明変数formulaの作成（Sex変数を除くバージョン）
n <- names(train)
n <- n[-14][-15][-14:-15][-15:-17][-16:-18][-4] # 不要なカラム名を取り除く
formula_train <- as.formula(paste("Survived~",paste(n[!n%in%c("Survived")],collapse="+")))
Survived ~ Pclass_2 + Pclass_3 + Sex + Embarked_Q + Embarked_S +
Dfamsize_Single + Dfamsize_Small + Title_Miss + Title_Mr +
Title_Mrs + Title_Officer + Title_Royalty + Age + Fare## Survived ~ Pclass_2 + Pclass_3 + Sex + Embarked_Q + Embarked_S +
## Dfamsize_Single + Dfamsize_Small + Title_Miss + Title_Mr +
## Title_Mrs + Title_Officer + Title_Royalty + Age + Fareformula_train## Survived ~ Pclass_2 + Pclass_3 + Embarked_Q + Embarked_S + DFamsize_Single +
## DFamsize_Small + Title_Miss + Title_Mr + Title_Mrs + Title_Officer +
## Title_Royalty + Age + Fare- 1回目からSEXを除いたところ以下のとおりすべての変数が10以下なので，この説明変数を使用してモデルを推定する。 # VIFの確認2
# VIFの確認（Sex変数を除くバージョン）
data_vif_ver1 <- glm(formula_train,family=binomial(link='logit'),data=train)
car::vif(data_vif_ver1) ## Pclass_2 Pclass_3 Embarked_Q Embarked_S DFamsize_Single
## 2.014926 2.896888 1.595989 1.526755 7.044565
## DFamsize_Small Title_Miss Title_Mr Title_Mrs Title_Officer
## 5.803506 6.283852 9.397575 4.804631 2.261898
## Title_Royalty Age Fare
## 1.272498 1.895658 1.716537#例題では説明変数がformula_trainとなっていたが、これではうまくいかないので、変数を書き連ねる。 #train\(Survived <- as.factor(train\)Survived) #Survived変数がnumeric型になっていたのでfactor型に変換する #acc_data_ver1 <- train(data = train,formula_train,method = “glmStepAIC”, #AICに基づきモデル構築 #family = binomial()) #summary(acc_data_ver1)
ロジスティック回帰モデルの推定
- http://yuranhiko.hatenablog.com/entry/DataAnalysis_R_caret_LinearRegression
- モデル構築を行うには “train関数” を使用します。
- cars.lm <- train(
- +data = cars, # 元となるデータを選択
- dist ~ ., # 結果変数と説明変数を選択
- method = “lm”) # モデル選択。線形回帰は “lm”
- method = “glm()” + family = binomial() #ロジスティック回帰モデルの文法
– glm関数(ロジスティック曲線）の文法————————————② - glm(解析用のデータ$目的変数の列名~.,data=解析用のデータ,family=binomial) - https://to-kei.net/r-beginner/r-5-logistic/
# ロジスティック回帰モデルの推定
#rdata <- alldata[1:891,]
#str(rdata)
#rdata$Survived <- as.factor(rdata$Survived) #Survived変数がnumeric型になっていたのでfactor型に変換する
#head(rdata)
#acc_data_ver1 <- glm(rdata$Survived~.,data=rdata,family=binomial)
#algorithm did not converge:アルゴリズムが収束しませんでしたというエラーがでる。？？？
#cc_data_ver1 <- train(data = train,formula_train,method = "glm",family = binomial())
#教科書ﾃﾞｰﾀではうまくいかないので、glm関数の文法------②を使用して解いた
#summary(acc_data_ver1)ロジスティック回帰モデルの推定の見直し
trainデータを使ってもうまくいかないので、他のモデルでやってみることを試みる。
Titanic1 <- expand.table(Titanic)
head (Titanic1)## Class Sex Age Survived
## 1 1st Male Child Yes
## 2 1st Male Child Yes
## 3 1st Male Child Yes
## 4 1st Male Child Yes
## 5 1st Male Child Yes
## 6 1st Male Adult Nonrow (Titanic1)## [1] 2201#head (Titanic1)Titanic.glm <- glm(Survived ~ +Sex + Age+ Class, data = Titanic1, family = "binomial")
summary (Titanic.glm)##
## Call:
## glm(formula = Survived ~ +Sex + Age + Class, family = "binomial",
## data = Titanic1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0812 -0.7149 -0.6656 0.6858 2.1278
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6853 0.2730 2.510 0.0121 *
## SexFemale 2.4201 0.1404 17.236 < 2e-16 ***
## AgeAdult -1.0615 0.2440 -4.350 1.36e-05 ***
## Class2nd -1.0181 0.1960 -5.194 2.05e-07 ***
## Class3rd -1.7778 0.1716 -10.362 < 2e-16 ***
## ClassCrew -0.8577 0.1573 -5.451 5.00e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2769.5 on 2200 degrees of freedom
## Residual deviance: 2210.1 on 2195 degrees of freedom
## AIC: 2222.1
##
## Number of Fisher Scoring iterations: 4#注目するのは，やはりCoefficientsのところです．
#まず(Intercept) ですが，これは切片です．より具体的にいうと，性別が男性，年齢が子供，等級が1等客室の乗客の場合の推定値です．
#実際にこの条件で予測をおこなってみると以下のようになります．
newData <- data.frame (Sex = "Male", Age = "Child", Class = "1st" )
predict(Titanic.glm, newdata = newData)## 1
## 0.6853195predict(Titanic.glm, newdata = newData, type = "response")## 1
## 0.6649249#最初にnewDataという名前で，性別が男性，年齢が子供，等級が1等客室の乗客というデータを作っています．predict関数に，先のロジスティック回帰分析の結果と，いまの新規データを指定して実行してみると，予測値が出ます．
#predict()関数を二度実行していますが，最初の出力は，先ほどの切片(Intercept)と同じ0.6853ですが，これは対数オッズです．二度目の実行では，「type = "response"」を追加しています．これは対数オッズを確率に変える指定です． 結果の0.66，約 66% が生き残る確率になります．Coefficients:の欄の切片の下は，まず女性の場合対数オッズで2.4201が追加されます．これは対数を外すと(exp(2.4201)を計算すると)，約11で，女性の場合，生存率が11倍に跳ね上がることを意味します．また年齢が成人(Adult)の係数は-1.0615で負の値です．これは成人の場合，生存率が三分の１に減少することを意味します(exp(-1.06)を計算)．以下同様に，Class2ndは等級が2等の場合，class3rdは3等の場合，そしてClassCrewは乗務員を表しますが，いずれもマイナスなので，生存率は下がっていることになります#mまずhttp://smrmkt.hatenablog.jp/entry/2012/12/20/232113のロジステック回帰とおりにやってみる
#train3 <- read.table('train.csv', header=T, sep=',')
#test3 <- expand.table(train3)
#train3.lr <- glm(survived~pclass+sex+age+sibsp+parch+fare, data=test3, family = "binomial")
#pred <-predict(train3.lr, test3, interval='prediction')
#pred_b <- ifelse(pred[,1] > 0.5, 1, 0)
#table(train3$survived, pred_b)
#dat = data.frame(Titanic)# dat <- expand.table(Titanic)でも結果は同じ
#train.lr <- glm(survived~pclass+sex+age+sibsp+parch+fare,data=dat)1/17 失敗 pred の値が2値でない。
次の例題に基づき実施：http://smrmkt.hatenablog.jp/entry/2012/12/20/232113
print(Titanic1)## Class Sex Age Survived
## 1 1st Male Child Yes
## 2 1st Male Child Yes
## 3 1st Male Child Yes
## 4 1st Male Child Yes
## 5 1st Male Child Yes
## 6 1st Male Adult No
## 7 1st Male Adult No
## 8 1st Male Adult No
## 9 1st Male Adult No
## 10 1st Male Adult No
## 11 1st Male Adult No
## 12 1st Male Adult No
## 13 1st Male Adult No
## 14 1st Male Adult No
## 15 1st Male Adult No
## 16 1st Male Adult No
## 17 1st Male Adult No
## 18 1st Male Adult No
## 19 1st Male Adult No
## 20 1st Male Adult No
## 21 1st Male Adult No
## 22 1st Male Adult No
## 23 1st Male Adult No
## 24 1st Male Adult No
## 25 1st Male Adult No
## 26 1st Male Adult No
## 27 1st Male Adult No
## 28 1st Male Adult No
## 29 1st Male Adult No
## 30 1st Male Adult No
## 31 1st Male Adult No
## 32 1st Male Adult No
## 33 1st Male Adult No
## 34 1st Male Adult No
## 35 1st Male Adult No
## 36 1st Male Adult No
## 37 1st Male Adult No
## 38 1st Male Adult No
## 39 1st Male Adult No
## 40 1st Male Adult No
## 41 1st Male Adult No
## 42 1st Male Adult No
## 43 1st Male Adult No
## 44 1st Male Adult No
## 45 1st Male Adult No
## 46 1st Male Adult No
## 47 1st Male Adult No
## 48 1st Male Adult No
## 49 1st Male Adult No
## 50 1st Male Adult No
## 51 1st Male Adult No
## 52 1st Male Adult No
## 53 1st Male Adult No
## 54 1st Male Adult No
## 55 1st Male Adult No
## 56 1st Male Adult No
## 57 1st Male Adult No
## 58 1st Male Adult No
## 59 1st Male Adult No
## 60 1st Male Adult No
## 61 1st Male Adult No
## 62 1st Male Adult No
## 63 1st Male Adult No
## 64 1st Male Adult No
## 65 1st Male Adult No
## 66 1st Male Adult No
## 67 1st Male Adult No
## 68 1st Male Adult No
## 69 1st Male Adult No
## 70 1st Male Adult No
## 71 1st Male Adult No
## 72 1st Male Adult No
## 73 1st Male Adult No
## 74 1st Male Adult No
## 75 1st Male Adult No
## 76 1st Male Adult No
## 77 1st Male Adult No
## 78 1st Male Adult No
## 79 1st Male Adult No
## 80 1st Male Adult No
## 81 1st Male Adult No
## 82 1st Male Adult No
## 83 1st Male Adult No
## 84 1st Male Adult No
## 85 1st Male Adult No
## 86 1st Male Adult No
## 87 1st Male Adult No
## 88 1st Male Adult No
## 89 1st Male Adult No
## 90 1st Male Adult No
## 91 1st Male Adult No
## 92 1st Male Adult No
## 93 1st Male Adult No
## 94 1st Male Adult No
## 95 1st Male Adult No
## 96 1st Male Adult No
## 97 1st Male Adult No
## 98 1st Male Adult No
## 99 1st Male Adult No
## 100 1st Male Adult No
## 101 1st Male Adult No
## 102 1st Male Adult No
## 103 1st Male Adult No
## 104 1st Male Adult No
## 105 1st Male Adult No
## 106 1st Male Adult No
## 107 1st Male Adult No
## 108 1st Male Adult No
## 109 1st Male Adult No
## 110 1st Male Adult No
## 111 1st Male Adult No
## 112 1st Male Adult No
## 113 1st Male Adult No
## 114 1st Male Adult No
## 115 1st Male Adult No
## 116 1st Male Adult No
## 117 1st Male Adult No
## 118 1st Male Adult No
## 119 1st Male Adult No
## 120 1st Male Adult No
## 121 1st Male Adult No
## 122 1st Male Adult No
## 123 1st Male Adult No
## 124 1st Male Adult Yes
## 125 1st Male Adult Yes
## 126 1st Male Adult Yes
## 127 1st Male Adult Yes
## 128 1st Male Adult Yes
## 129 1st Male Adult Yes
## 130 1st Male Adult Yes
## 131 1st Male Adult Yes
## 132 1st Male Adult Yes
## 133 1st Male Adult Yes
## 134 1st Male Adult Yes
## 135 1st Male Adult Yes
## 136 1st Male Adult Yes
## 137 1st Male Adult Yes
## 138 1st Male Adult Yes
## 139 1st Male Adult Yes
## 140 1st Male Adult Yes
## 141 1st Male Adult Yes
## 142 1st Male Adult Yes
## 143 1st Male Adult Yes
## 144 1st Male Adult Yes
## 145 1st Male Adult Yes
## 146 1st Male Adult Yes
## 147 1st Male Adult Yes
## 148 1st Male Adult Yes
## 149 1st Male Adult Yes
## 150 1st Male Adult Yes
## 151 1st Male Adult Yes
## 152 1st Male Adult Yes
## 153 1st Male Adult Yes
## 154 1st Male Adult Yes
## 155 1st Male Adult Yes
## 156 1st Male Adult Yes
## 157 1st Male Adult Yes
## 158 1st Male Adult Yes
## 159 1st Male Adult Yes
## 160 1st Male Adult Yes
## 161 1st Male Adult Yes
## 162 1st Male Adult Yes
## 163 1st Male Adult Yes
## 164 1st Male Adult Yes
## 165 1st Male Adult Yes
## 166 1st Male Adult Yes
## 167 1st Male Adult Yes
## 168 1st Male Adult Yes
## 169 1st Male Adult Yes
## 170 1st Male Adult Yes
## 171 1st Male Adult Yes
## 172 1st Male Adult Yes
## 173 1st Male Adult Yes
## 174 1st Male Adult Yes
## 175 1st Male Adult Yes
## 176 1st Male Adult Yes
## 177 1st Male Adult Yes
## 178 1st Male Adult Yes
## 179 1st Male Adult Yes
## 180 1st Male Adult Yes
## 181 1st Female Child Yes
## 182 1st Female Adult No
## 183 1st Female Adult No
## 184 1st Female Adult No
## 185 1st Female Adult No
## 186 1st Female Adult Yes
## 187 1st Female Adult Yes
## 188 1st Female Adult Yes
## 189 1st Female Adult Yes
## 190 1st Female Adult Yes
## 191 1st Female Adult Yes
## 192 1st Female Adult Yes
## 193 1st Female Adult Yes
## 194 1st Female Adult Yes
## 195 1st Female Adult Yes
## 196 1st Female Adult Yes
## 197 1st Female Adult Yes
## 198 1st Female Adult Yes
## 199 1st Female Adult Yes
## 200 1st Female Adult Yes
## 201 1st Female Adult Yes
## 202 1st Female Adult Yes
## 203 1st Female Adult Yes
## 204 1st Female Adult Yes
## 205 1st Female Adult Yes
## 206 1st Female Adult Yes
## 207 1st Female Adult Yes
## 208 1st Female Adult Yes
## 209 1st Female Adult Yes
## 210 1st Female Adult Yes
## 211 1st Female Adult Yes
## 212 1st Female Adult Yes
## 213 1st Female Adult Yes
## 214 1st Female Adult Yes
## 215 1st Female Adult Yes
## 216 1st Female Adult Yes
## 217 1st Female Adult Yes
## 218 1st Female Adult Yes
## 219 1st Female Adult Yes
## 220 1st Female Adult Yes
## 221 1st Female Adult Yes
## 222 1st Female Adult Yes
## 223 1st Female Adult Yes
## 224 1st Female Adult Yes
## 225 1st Female Adult Yes
## 226 1st Female Adult Yes
## 227 1st Female Adult Yes
## 228 1st Female Adult Yes
## 229 1st Female Adult Yes
## 230 1st Female Adult Yes
## 231 1st Female Adult Yes
## 232 1st Female Adult Yes
## 233 1st Female Adult Yes
## 234 1st Female Adult Yes
## 235 1st Female Adult Yes
## 236 1st Female Adult Yes
## 237 1st Female Adult Yes
## 238 1st Female Adult Yes
## 239 1st Female Adult Yes
## 240 1st Female Adult Yes
## 241 1st Female Adult Yes
## 242 1st Female Adult Yes
## 243 1st Female Adult Yes
## 244 1st Female Adult Yes
## 245 1st Female Adult Yes
## 246 1st Female Adult Yes
## 247 1st Female Adult Yes
## 248 1st Female Adult Yes
## 249 1st Female Adult Yes
## 250 1st Female Adult Yes
## 251 1st Female Adult Yes
## 252 1st Female Adult Yes
## 253 1st Female Adult Yes
## 254 1st Female Adult Yes
## 255 1st Female Adult Yes
## 256 1st Female Adult Yes
## 257 1st Female Adult Yes
## 258 1st Female Adult Yes
## 259 1st Female Adult Yes
## 260 1st Female Adult Yes
## 261 1st Female Adult Yes
## 262 1st Female Adult Yes
## 263 1st Female Adult Yes
## 264 1st Female Adult Yes
## 265 1st Female Adult Yes
## 266 1st Female Adult Yes
## 267 1st Female Adult Yes
## 268 1st Female Adult Yes
## 269 1st Female Adult Yes
## 270 1st Female Adult Yes
## 271 1st Female Adult Yes
## 272 1st Female Adult Yes
## 273 1st Female Adult Yes
## 274 1st Female Adult Yes
## 275 1st Female Adult Yes
## 276 1st Female Adult Yes
## 277 1st Female Adult Yes
## 278 1st Female Adult Yes
## 279 1st Female Adult Yes
## 280 1st Female Adult Yes
## 281 1st Female Adult Yes
## 282 1st Female Adult Yes
## 283 1st Female Adult Yes
## 284 1st Female Adult Yes
## 285 1st Female Adult Yes
## 286 1st Female Adult Yes
## 287 1st Female Adult Yes
## 288 1st Female Adult Yes
## 289 1st Female Adult Yes
## 290 1st Female Adult Yes
## 291 1st Female Adult Yes
## 292 1st Female Adult Yes
## 293 1st Female Adult Yes
## 294 1st Female Adult Yes
## 295 1st Female Adult Yes
## 296 1st Female Adult Yes
## 297 1st Female Adult Yes
## 298 1st Female Adult Yes
## 299 1st Female Adult Yes
## 300 1st Female Adult Yes
## 301 1st Female Adult Yes
## 302 1st Female Adult Yes
## 303 1st Female Adult Yes
## 304 1st Female Adult Yes
## 305 1st Female Adult Yes
## 306 1st Female Adult Yes
## 307 1st Female Adult Yes
## 308 1st Female Adult Yes
## 309 1st Female Adult Yes
## 310 1st Female Adult Yes
## 311 1st Female Adult Yes
## 312 1st Female Adult Yes
## 313 1st Female Adult Yes
## 314 1st Female Adult Yes
## 315 1st Female Adult Yes
## 316 1st Female Adult Yes
## 317 1st Female Adult Yes
## 318 1st Female Adult Yes
## 319 1st Female Adult Yes
## 320 1st Female Adult Yes
## 321 1st Female Adult Yes
## 322 1st Female Adult Yes
## 323 1st Female Adult Yes
## 324 1st Female Adult Yes
## 325 1st Female Adult Yes
## 326 2nd Male Child Yes
## 327 2nd Male Child Yes
## 328 2nd Male Child Yes
## 329 2nd Male Child Yes
## 330 2nd Male Child Yes
## 331 2nd Male Child Yes
## 332 2nd Male Child Yes
## 333 2nd Male Child Yes
## 334 2nd Male Child Yes
## 335 2nd Male Child Yes
## 336 2nd Male Child Yes
## 337 2nd Male Adult No
## 338 2nd Male Adult No
## 339 2nd Male Adult No
## 340 2nd Male Adult No
## 341 2nd Male Adult No
## 342 2nd Male Adult No
## 343 2nd Male Adult No
## 344 2nd Male Adult No
## 345 2nd Male Adult No
## 346 2nd Male Adult No
## 347 2nd Male Adult No
## 348 2nd Male Adult No
## 349 2nd Male Adult No
## 350 2nd Male Adult No
## 351 2nd Male Adult No
## 352 2nd Male Adult No
## 353 2nd Male Adult No
## 354 2nd Male Adult No
## 355 2nd Male Adult No
## 356 2nd Male Adult No
## 357 2nd Male Adult No
## 358 2nd Male Adult No
## 359 2nd Male Adult No
## 360 2nd Male Adult No
## 361 2nd Male Adult No
## 362 2nd Male Adult No
## 363 2nd Male Adult No
## 364 2nd Male Adult No
## 365 2nd Male Adult No
## 366 2nd Male Adult No
## 367 2nd Male Adult No
## 368 2nd Male Adult No
## 369 2nd Male Adult No
## 370 2nd Male Adult No
## 371 2nd Male Adult No
## 372 2nd Male Adult No
## 373 2nd Male Adult No
## 374 2nd Male Adult No
## 375 2nd Male Adult No
## 376 2nd Male Adult No
## 377 2nd Male Adult No
## 378 2nd Male Adult No
## 379 2nd Male Adult No
## 380 2nd Male Adult No
## 381 2nd Male Adult No
## 382 2nd Male Adult No
## 383 2nd Male Adult No
## 384 2nd Male Adult No
## 385 2nd Male Adult No
## 386 2nd Male Adult No
## 387 2nd Male Adult No
## 388 2nd Male Adult No
## 389 2nd Male Adult No
## 390 2nd Male Adult No
## 391 2nd Male Adult No
## 392 2nd Male Adult No
## 393 2nd Male Adult No
## 394 2nd Male Adult No
## 395 2nd Male Adult No
## 396 2nd Male Adult No
## 397 2nd Male Adult No
## 398 2nd Male Adult No
## 399 2nd Male Adult No
## 400 2nd Male Adult No
## 401 2nd Male Adult No
## 402 2nd Male Adult No
## 403 2nd Male Adult No
## 404 2nd Male Adult No
## 405 2nd Male Adult No
## 406 2nd Male Adult No
## 407 2nd Male Adult No
## 408 2nd Male Adult No
## 409 2nd Male Adult No
## 410 2nd Male Adult No
## 411 2nd Male Adult No
## 412 2nd Male Adult No
## 413 2nd Male Adult No
## 414 2nd Male Adult No
## 415 2nd Male Adult No
## 416 2nd Male Adult No
## 417 2nd Male Adult No
## 418 2nd Male Adult No
## 419 2nd Male Adult No
## 420 2nd Male Adult No
## 421 2nd Male Adult No
## 422 2nd Male Adult No
## 423 2nd Male Adult No
## 424 2nd Male Adult No
## 425 2nd Male Adult No
## 426 2nd Male Adult No
## 427 2nd Male Adult No
## 428 2nd Male Adult No
## 429 2nd Male Adult No
## 430 2nd Male Adult No
## 431 2nd Male Adult No
## 432 2nd Male Adult No
## 433 2nd Male Adult No
## 434 2nd Male Adult No
## 435 2nd Male Adult No
## 436 2nd Male Adult No
## 437 2nd Male Adult No
## 438 2nd Male Adult No
## 439 2nd Male Adult No
## 440 2nd Male Adult No
## 441 2nd Male Adult No
## 442 2nd Male Adult No
## 443 2nd Male Adult No
## 444 2nd Male Adult No
## 445 2nd Male Adult No
## 446 2nd Male Adult No
## 447 2nd Male Adult No
## 448 2nd Male Adult No
## 449 2nd Male Adult No
## 450 2nd Male Adult No
## 451 2nd Male Adult No
## 452 2nd Male Adult No
## 453 2nd Male Adult No
## 454 2nd Male Adult No
## 455 2nd Male Adult No
## 456 2nd Male Adult No
## 457 2nd Male Adult No
## 458 2nd Male Adult No
## 459 2nd Male Adult No
## 460 2nd Male Adult No
## 461 2nd Male Adult No
## 462 2nd Male Adult No
## 463 2nd Male Adult No
## 464 2nd Male Adult No
## 465 2nd Male Adult No
## 466 2nd Male Adult No
## 467 2nd Male Adult No
## 468 2nd Male Adult No
## 469 2nd Male Adult No
## 470 2nd Male Adult No
## 471 2nd Male Adult No
## 472 2nd Male Adult No
## 473 2nd Male Adult No
## 474 2nd Male Adult No
## 475 2nd Male Adult No
## 476 2nd Male Adult No
## 477 2nd Male Adult No
## 478 2nd Male Adult No
## 479 2nd Male Adult No
## 480 2nd Male Adult No
## 481 2nd Male Adult No
## 482 2nd Male Adult No
## 483 2nd Male Adult No
## 484 2nd Male Adult No
## 485 2nd Male Adult No
## 486 2nd Male Adult No
## 487 2nd Male Adult No
## 488 2nd Male Adult No
## 489 2nd Male Adult No
## 490 2nd Male Adult No
## 491 2nd Male Adult Yes
## 492 2nd Male Adult Yes
## 493 2nd Male Adult Yes
## 494 2nd Male Adult Yes
## 495 2nd Male Adult Yes
## 496 2nd Male Adult Yes
## 497 2nd Male Adult Yes
## 498 2nd Male Adult Yes
## 499 2nd Male Adult Yes
## 500 2nd Male Adult Yes
## 501 2nd Male Adult Yes
## 502 2nd Male Adult Yes
## 503 2nd Male Adult Yes
## 504 2nd Male Adult Yes
## 505 2nd Female Child Yes
## 506 2nd Female Child Yes
## 507 2nd Female Child Yes
## 508 2nd Female Child Yes
## 509 2nd Female Child Yes
## 510 2nd Female Child Yes
## 511 2nd Female Child Yes
## 512 2nd Female Child Yes
## 513 2nd Female Child Yes
## 514 2nd Female Child Yes
## 515 2nd Female Child Yes
## 516 2nd Female Child Yes
## 517 2nd Female Child Yes
## 518 2nd Female Adult No
## 519 2nd Female Adult No
## 520 2nd Female Adult No
## 521 2nd Female Adult No
## 522 2nd Female Adult No
## 523 2nd Female Adult No
## 524 2nd Female Adult No
## 525 2nd Female Adult No
## 526 2nd Female Adult No
## 527 2nd Female Adult No
## 528 2nd Female Adult No
## 529 2nd Female Adult No
## 530 2nd Female Adult No
## 531 2nd Female Adult Yes
## 532 2nd Female Adult Yes
## 533 2nd Female Adult Yes
## 534 2nd Female Adult Yes
## 535 2nd Female Adult Yes
## 536 2nd Female Adult Yes
## 537 2nd Female Adult Yes
## 538 2nd Female Adult Yes
## 539 2nd Female Adult Yes
## 540 2nd Female Adult Yes
## 541 2nd Female Adult Yes
## 542 2nd Female Adult Yes
## 543 2nd Female Adult Yes
## 544 2nd Female Adult Yes
## 545 2nd Female Adult Yes
## 546 2nd Female Adult Yes
## 547 2nd Female Adult Yes
## 548 2nd Female Adult Yes
## 549 2nd Female Adult Yes
## 550 2nd Female Adult Yes
## 551 2nd Female Adult Yes
## 552 2nd Female Adult Yes
## 553 2nd Female Adult Yes
## 554 2nd Female Adult Yes
## 555 2nd Female Adult Yes
## 556 2nd Female Adult Yes
## 557 2nd Female Adult Yes
## 558 2nd Female Adult Yes
## 559 2nd Female Adult Yes
## 560 2nd Female Adult Yes
## 561 2nd Female Adult Yes
## 562 2nd Female Adult Yes
## 563 2nd Female Adult Yes
## 564 2nd Female Adult Yes
## 565 2nd Female Adult Yes
## 566 2nd Female Adult Yes
## 567 2nd Female Adult Yes
## 568 2nd Female Adult Yes
## 569 2nd Female Adult Yes
## 570 2nd Female Adult Yes
## 571 2nd Female Adult Yes
## 572 2nd Female Adult Yes
## 573 2nd Female Adult Yes
## 574 2nd Female Adult Yes
## 575 2nd Female Adult Yes
## 576 2nd Female Adult Yes
## 577 2nd Female Adult Yes
## 578 2nd Female Adult Yes
## 579 2nd Female Adult Yes
## 580 2nd Female Adult Yes
## 581 2nd Female Adult Yes
## 582 2nd Female Adult Yes
## 583 2nd Female Adult Yes
## 584 2nd Female Adult Yes
## 585 2nd Female Adult Yes
## 586 2nd Female Adult Yes
## 587 2nd Female Adult Yes
## 588 2nd Female Adult Yes
## 589 2nd Female Adult Yes
## 590 2nd Female Adult Yes
## 591 2nd Female Adult Yes
## 592 2nd Female Adult Yes
## 593 2nd Female Adult Yes
## 594 2nd Female Adult Yes
## 595 2nd Female Adult Yes
## 596 2nd Female Adult Yes
## 597 2nd Female Adult Yes
## 598 2nd Female Adult Yes
## 599 2nd Female Adult Yes
## 600 2nd Female Adult Yes
## 601 2nd Female Adult Yes
## 602 2nd Female Adult Yes
## 603 2nd Female Adult Yes
## 604 2nd Female Adult Yes
## 605 2nd Female Adult Yes
## 606 2nd Female Adult Yes
## 607 2nd Female Adult Yes
## 608 2nd Female Adult Yes
## 609 2nd Female Adult Yes
## 610 2nd Female Adult Yes
## 611 3rd Male Child No
## 612 3rd Male Child No
## 613 3rd Male Child No
## 614 3rd Male Child No
## 615 3rd Male Child No
## 616 3rd Male Child No
## 617 3rd Male Child No
## 618 3rd Male Child No
## 619 3rd Male Child No
## 620 3rd Male Child No
## 621 3rd Male Child No
## 622 3rd Male Child No
## 623 3rd Male Child No
## 624 3rd Male Child No
## 625 3rd Male Child No
## 626 3rd Male Child No
## 627 3rd Male Child No
## 628 3rd Male Child No
## 629 3rd Male Child No
## 630 3rd Male Child No
## 631 3rd Male Child No
## 632 3rd Male Child No
## 633 3rd Male Child No
## 634 3rd Male Child No
## 635 3rd Male Child No
## 636 3rd Male Child No
## 637 3rd Male Child No
## 638 3rd Male Child No
## 639 3rd Male Child No
## 640 3rd Male Child No
## 641 3rd Male Child No
## 642 3rd Male Child No
## 643 3rd Male Child No
## 644 3rd Male Child No
## 645 3rd Male Child No
## 646 3rd Male Child Yes
## 647 3rd Male Child Yes
## 648 3rd Male Child Yes
## 649 3rd Male Child Yes
## 650 3rd Male Child Yes
## 651 3rd Male Child Yes
## 652 3rd Male Child Yes
## 653 3rd Male Child Yes
## 654 3rd Male Child Yes
## 655 3rd Male Child Yes
## 656 3rd Male Child Yes
## 657 3rd Male Child Yes
## 658 3rd Male Child Yes
## 659 3rd Male Adult No
## 660 3rd Male Adult No
## 661 3rd Male Adult No
## 662 3rd Male Adult No
## 663 3rd Male Adult No
## 664 3rd Male Adult No
## 665 3rd Male Adult No
## 666 3rd Male Adult No
## 667 3rd Male Adult No
## 668 3rd Male Adult No
## 669 3rd Male Adult No
## 670 3rd Male Adult No
## 671 3rd Male Adult No
## 672 3rd Male Adult No
## 673 3rd Male Adult No
## 674 3rd Male Adult No
## 675 3rd Male Adult No
## 676 3rd Male Adult No
## 677 3rd Male Adult No
## 678 3rd Male Adult No
## 679 3rd Male Adult No
## 680 3rd Male Adult No
## 681 3rd Male Adult No
## 682 3rd Male Adult No
## 683 3rd Male Adult No
## 684 3rd Male Adult No
## 685 3rd Male Adult No
## 686 3rd Male Adult No
## 687 3rd Male Adult No
## 688 3rd Male Adult No
## 689 3rd Male Adult No
## 690 3rd Male Adult No
## 691 3rd Male Adult No
## 692 3rd Male Adult No
## 693 3rd Male Adult No
## 694 3rd Male Adult No
## 695 3rd Male Adult No
## 696 3rd Male Adult No
## 697 3rd Male Adult No
## 698 3rd Male Adult No
## 699 3rd Male Adult No
## 700 3rd Male Adult No
## 701 3rd Male Adult No
## 702 3rd Male Adult No
## 703 3rd Male Adult No
## 704 3rd Male Adult No
## 705 3rd Male Adult No
## 706 3rd Male Adult No
## 707 3rd Male Adult No
## 708 3rd Male Adult No
## 709 3rd Male Adult No
## 710 3rd Male Adult No
## 711 3rd Male Adult No
## 712 3rd Male Adult No
## 713 3rd Male Adult No
## 714 3rd Male Adult No
## 715 3rd Male Adult No
## 716 3rd Male Adult No
## 717 3rd Male Adult No
## 718 3rd Male Adult No
## 719 3rd Male Adult No
## 720 3rd Male Adult No
## 721 3rd Male Adult No
## 722 3rd Male Adult No
## 723 3rd Male Adult No
## 724 3rd Male Adult No
## 725 3rd Male Adult No
## 726 3rd Male Adult No
## 727 3rd Male Adult No
## 728 3rd Male Adult No
## 729 3rd Male Adult No
## 730 3rd Male Adult No
## 731 3rd Male Adult No
## 732 3rd Male Adult No
## 733 3rd Male Adult No
## 734 3rd Male Adult No
## 735 3rd Male Adult No
## 736 3rd Male Adult No
## 737 3rd Male Adult No
## 738 3rd Male Adult No
## 739 3rd Male Adult No
## 740 3rd Male Adult No
## 741 3rd Male Adult No
## 742 3rd Male Adult No
## 743 3rd Male Adult No
## 744 3rd Male Adult No
## 745 3rd Male Adult No
## 746 3rd Male Adult No
## 747 3rd Male Adult No
## 748 3rd Male Adult No
## 749 3rd Male Adult No
## 750 3rd Male Adult No
## 751 3rd Male Adult No
## 752 3rd Male Adult No
## 753 3rd Male Adult No
## 754 3rd Male Adult No
## 755 3rd Male Adult No
## 756 3rd Male Adult No
## 757 3rd Male Adult No
## 758 3rd Male Adult No
## 759 3rd Male Adult No
## 760 3rd Male Adult No
## 761 3rd Male Adult No
## 762 3rd Male Adult No
## 763 3rd Male Adult No
## 764 3rd Male Adult No
## 765 3rd Male Adult No
## 766 3rd Male Adult No
## 767 3rd Male Adult No
## 768 3rd Male Adult No
## 769 3rd Male Adult No
## 770 3rd Male Adult No
## 771 3rd Male Adult No
## 772 3rd Male Adult No
## 773 3rd Male Adult No
## 774 3rd Male Adult No
## 775 3rd Male Adult No
## 776 3rd Male Adult No
## 777 3rd Male Adult No
## 778 3rd Male Adult No
## 779 3rd Male Adult No
## 780 3rd Male Adult No
## 781 3rd Male Adult No
## 782 3rd Male Adult No
## 783 3rd Male Adult No
## 784 3rd Male Adult No
## 785 3rd Male Adult No
## 786 3rd Male Adult No
## 787 3rd Male Adult No
## 788 3rd Male Adult No
## 789 3rd Male Adult No
## 790 3rd Male Adult No
## 791 3rd Male Adult No
## 792 3rd Male Adult No
## 793 3rd Male Adult No
## 794 3rd Male Adult No
## 795 3rd Male Adult No
## 796 3rd Male Adult No
## 797 3rd Male Adult No
## 798 3rd Male Adult No
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## 1649 Crew Male Adult No
## 1650 Crew Male Adult No
## 1651 Crew Male Adult No
## 1652 Crew Male Adult No
## 1653 Crew Male Adult No
## 1654 Crew Male Adult No
## 1655 Crew Male Adult No
## 1656 Crew Male Adult No
## 1657 Crew Male Adult No
## 1658 Crew Male Adult No
## 1659 Crew Male Adult No
## 1660 Crew Male Adult No
## 1661 Crew Male Adult No
## 1662 Crew Male Adult No
## 1663 Crew Male Adult No
## 1664 Crew Male Adult No
## 1665 Crew Male Adult No
## 1666 Crew Male Adult No
## 1667 Crew Male Adult No
## 1668 Crew Male Adult No
## 1669 Crew Male Adult No
## 1670 Crew Male Adult No
## 1671 Crew Male Adult No
## 1672 Crew Male Adult No
## 1673 Crew Male Adult No
## 1674 Crew Male Adult No
## 1675 Crew Male Adult No
## 1676 Crew Male Adult No
## 1677 Crew Male Adult No
## 1678 Crew Male Adult No
## 1679 Crew Male Adult No
## 1680 Crew Male Adult No
## 1681 Crew Male Adult No
## 1682 Crew Male Adult No
## 1683 Crew Male Adult No
## 1684 Crew Male Adult No
## 1685 Crew Male Adult No
## 1686 Crew Male Adult No
## 1687 Crew Male Adult No
## 1688 Crew Male Adult No
## 1689 Crew Male Adult No
## 1690 Crew Male Adult No
## 1691 Crew Male Adult No
## 1692 Crew Male Adult No
## 1693 Crew Male Adult No
## 1694 Crew Male Adult No
## 1695 Crew Male Adult No
## 1696 Crew Male Adult No
## 1697 Crew Male Adult No
## 1698 Crew Male Adult No
## 1699 Crew Male Adult No
## 1700 Crew Male Adult No
## 1701 Crew Male Adult No
## 1702 Crew Male Adult No
## 1703 Crew Male Adult No
## 1704 Crew Male Adult No
## 1705 Crew Male Adult No
## 1706 Crew Male Adult No
## 1707 Crew Male Adult No
## 1708 Crew Male Adult No
## 1709 Crew Male Adult No
## 1710 Crew Male Adult No
## 1711 Crew Male Adult No
## 1712 Crew Male Adult No
## 1713 Crew Male Adult No
## 1714 Crew Male Adult No
## 1715 Crew Male Adult No
## 1716 Crew Male Adult No
## 1717 Crew Male Adult No
## 1718 Crew Male Adult No
## 1719 Crew Male Adult No
## 1720 Crew Male Adult No
## 1721 Crew Male Adult No
## 1722 Crew Male Adult No
## 1723 Crew Male Adult No
## 1724 Crew Male Adult No
## 1725 Crew Male Adult No
## 1726 Crew Male Adult No
## 1727 Crew Male Adult No
## 1728 Crew Male Adult No
## 1729 Crew Male Adult No
## 1730 Crew Male Adult No
## 1731 Crew Male Adult No
## 1732 Crew Male Adult No
## 1733 Crew Male Adult No
## 1734 Crew Male Adult No
## 1735 Crew Male Adult No
## 1736 Crew Male Adult No
## 1737 Crew Male Adult No
## 1738 Crew Male Adult No
## 1739 Crew Male Adult No
## 1740 Crew Male Adult No
## 1741 Crew Male Adult No
## 1742 Crew Male Adult No
## 1743 Crew Male Adult No
## 1744 Crew Male Adult No
## 1745 Crew Male Adult No
## 1746 Crew Male Adult No
## 1747 Crew Male Adult No
## 1748 Crew Male Adult No
## 1749 Crew Male Adult No
## 1750 Crew Male Adult No
## 1751 Crew Male Adult No
## 1752 Crew Male Adult No
## 1753 Crew Male Adult No
## 1754 Crew Male Adult No
## 1755 Crew Male Adult No
## 1756 Crew Male Adult No
## 1757 Crew Male Adult No
## 1758 Crew Male Adult No
## 1759 Crew Male Adult No
## 1760 Crew Male Adult No
## 1761 Crew Male Adult No
## 1762 Crew Male Adult No
## 1763 Crew Male Adult No
## 1764 Crew Male Adult No
## 1765 Crew Male Adult No
## 1766 Crew Male Adult No
## 1767 Crew Male Adult No
## 1768 Crew Male Adult No
## 1769 Crew Male Adult No
## 1770 Crew Male Adult No
## 1771 Crew Male Adult No
## 1772 Crew Male Adult No
## 1773 Crew Male Adult No
## 1774 Crew Male Adult No
## 1775 Crew Male Adult No
## 1776 Crew Male Adult No
## 1777 Crew Male Adult No
## 1778 Crew Male Adult No
## 1779 Crew Male Adult No
## 1780 Crew Male Adult No
## 1781 Crew Male Adult No
## 1782 Crew Male Adult No
## 1783 Crew Male Adult No
## 1784 Crew Male Adult No
## 1785 Crew Male Adult No
## 1786 Crew Male Adult No
## 1787 Crew Male Adult No
## 1788 Crew Male Adult No
## 1789 Crew Male Adult No
## 1790 Crew Male Adult No
## 1791 Crew Male Adult No
## 1792 Crew Male Adult No
## 1793 Crew Male Adult No
## 1794 Crew Male Adult No
## 1795 Crew Male Adult No
## 1796 Crew Male Adult No
## 1797 Crew Male Adult No
## 1798 Crew Male Adult No
## 1799 Crew Male Adult No
## 1800 Crew Male Adult No
## 1801 Crew Male Adult No
## 1802 Crew Male Adult No
## 1803 Crew Male Adult No
## 1804 Crew Male Adult No
## 1805 Crew Male Adult No
## 1806 Crew Male Adult No
## 1807 Crew Male Adult No
## 1808 Crew Male Adult No
## 1809 Crew Male Adult No
## 1810 Crew Male Adult No
## 1811 Crew Male Adult No
## 1812 Crew Male Adult No
## 1813 Crew Male Adult No
## 1814 Crew Male Adult No
## 1815 Crew Male Adult No
## 1816 Crew Male Adult No
## 1817 Crew Male Adult No
## 1818 Crew Male Adult No
## 1819 Crew Male Adult No
## 1820 Crew Male Adult No
## 1821 Crew Male Adult No
## 1822 Crew Male Adult No
## 1823 Crew Male Adult No
## 1824 Crew Male Adult No
## 1825 Crew Male Adult No
## 1826 Crew Male Adult No
## 1827 Crew Male Adult No
## 1828 Crew Male Adult No
## 1829 Crew Male Adult No
## 1830 Crew Male Adult No
## 1831 Crew Male Adult No
## 1832 Crew Male Adult No
## 1833 Crew Male Adult No
## 1834 Crew Male Adult No
## 1835 Crew Male Adult No
## 1836 Crew Male Adult No
## 1837 Crew Male Adult No
## 1838 Crew Male Adult No
## 1839 Crew Male Adult No
## 1840 Crew Male Adult No
## 1841 Crew Male Adult No
## 1842 Crew Male Adult No
## 1843 Crew Male Adult No
## 1844 Crew Male Adult No
## 1845 Crew Male Adult No
## 1846 Crew Male Adult No
## 1847 Crew Male Adult No
## 1848 Crew Male Adult No
## 1849 Crew Male Adult No
## 1850 Crew Male Adult No
## 1851 Crew Male Adult No
## 1852 Crew Male Adult No
## 1853 Crew Male Adult No
## 1854 Crew Male Adult No
## 1855 Crew Male Adult No
## 1856 Crew Male Adult No
## 1857 Crew Male Adult No
## 1858 Crew Male Adult No
## 1859 Crew Male Adult No
## 1860 Crew Male Adult No
## 1861 Crew Male Adult No
## 1862 Crew Male Adult No
## 1863 Crew Male Adult No
## 1864 Crew Male Adult No
## 1865 Crew Male Adult No
## 1866 Crew Male Adult No
## 1867 Crew Male Adult No
## 1868 Crew Male Adult No
## 1869 Crew Male Adult No
## 1870 Crew Male Adult No
## 1871 Crew Male Adult No
## 1872 Crew Male Adult No
## 1873 Crew Male Adult No
## 1874 Crew Male Adult No
## 1875 Crew Male Adult No
## 1876 Crew Male Adult No
## 1877 Crew Male Adult No
## 1878 Crew Male Adult No
## 1879 Crew Male Adult No
## 1880 Crew Male Adult No
## 1881 Crew Male Adult No
## 1882 Crew Male Adult No
## 1883 Crew Male Adult No
## 1884 Crew Male Adult No
## 1885 Crew Male Adult No
## 1886 Crew Male Adult No
## 1887 Crew Male Adult No
## 1888 Crew Male Adult No
## 1889 Crew Male Adult No
## 1890 Crew Male Adult No
## 1891 Crew Male Adult No
## 1892 Crew Male Adult No
## 1893 Crew Male Adult No
## 1894 Crew Male Adult No
## 1895 Crew Male Adult No
## 1896 Crew Male Adult No
## 1897 Crew Male Adult No
## 1898 Crew Male Adult No
## 1899 Crew Male Adult No
## 1900 Crew Male Adult No
## 1901 Crew Male Adult No
## 1902 Crew Male Adult No
## 1903 Crew Male Adult No
## 1904 Crew Male Adult No
## 1905 Crew Male Adult No
## 1906 Crew Male Adult No
## 1907 Crew Male Adult No
## 1908 Crew Male Adult No
## 1909 Crew Male Adult No
## 1910 Crew Male Adult No
## 1911 Crew Male Adult No
## 1912 Crew Male Adult No
## 1913 Crew Male Adult No
## 1914 Crew Male Adult No
## 1915 Crew Male Adult No
## 1916 Crew Male Adult No
## 1917 Crew Male Adult No
## 1918 Crew Male Adult No
## 1919 Crew Male Adult No
## 1920 Crew Male Adult No
## 1921 Crew Male Adult No
## 1922 Crew Male Adult No
## 1923 Crew Male Adult No
## 1924 Crew Male Adult No
## 1925 Crew Male Adult No
## 1926 Crew Male Adult No
## 1927 Crew Male Adult No
## 1928 Crew Male Adult No
## 1929 Crew Male Adult No
## 1930 Crew Male Adult No
## 1931 Crew Male Adult No
## 1932 Crew Male Adult No
## 1933 Crew Male Adult No
## 1934 Crew Male Adult No
## 1935 Crew Male Adult No
## 1936 Crew Male Adult No
## 1937 Crew Male Adult No
## 1938 Crew Male Adult No
## 1939 Crew Male Adult No
## 1940 Crew Male Adult No
## 1941 Crew Male Adult No
## 1942 Crew Male Adult No
## 1943 Crew Male Adult No
## 1944 Crew Male Adult No
## 1945 Crew Male Adult No
## 1946 Crew Male Adult No
## 1947 Crew Male Adult No
## 1948 Crew Male Adult No
## 1949 Crew Male Adult No
## 1950 Crew Male Adult No
## 1951 Crew Male Adult No
## 1952 Crew Male Adult No
## 1953 Crew Male Adult No
## 1954 Crew Male Adult No
## 1955 Crew Male Adult No
## 1956 Crew Male Adult No
## 1957 Crew Male Adult No
## 1958 Crew Male Adult No
## 1959 Crew Male Adult No
## 1960 Crew Male Adult No
## 1961 Crew Male Adult No
## 1962 Crew Male Adult No
## 1963 Crew Male Adult No
## 1964 Crew Male Adult No
## 1965 Crew Male Adult No
## 1966 Crew Male Adult No
## 1967 Crew Male Adult No
## 1968 Crew Male Adult No
## 1969 Crew Male Adult No
## 1970 Crew Male Adult No
## 1971 Crew Male Adult No
## 1972 Crew Male Adult No
## 1973 Crew Male Adult No
## 1974 Crew Male Adult No
## 1975 Crew Male Adult No
## 1976 Crew Male Adult No
## 1977 Crew Male Adult No
## 1978 Crew Male Adult No
## 1979 Crew Male Adult No
## 1980 Crew Male Adult No
## 1981 Crew Male Adult No
## 1982 Crew Male Adult No
## 1983 Crew Male Adult No
## 1984 Crew Male Adult No
## 1985 Crew Male Adult No
## 1986 Crew Male Adult No
## 1987 Crew Male Adult Yes
## 1988 Crew Male Adult Yes
## 1989 Crew Male Adult Yes
## 1990 Crew Male Adult Yes
## 1991 Crew Male Adult Yes
## 1992 Crew Male Adult Yes
## 1993 Crew Male Adult Yes
## 1994 Crew Male Adult Yes
## 1995 Crew Male Adult Yes
## 1996 Crew Male Adult Yes
## 1997 Crew Male Adult Yes
## 1998 Crew Male Adult Yes
## 1999 Crew Male Adult Yes
## 2000 Crew Male Adult Yes
## 2001 Crew Male Adult Yes
## 2002 Crew Male Adult Yes
## 2003 Crew Male Adult Yes
## 2004 Crew Male Adult Yes
## 2005 Crew Male Adult Yes
## 2006 Crew Male Adult Yes
## 2007 Crew Male Adult Yes
## 2008 Crew Male Adult Yes
## 2009 Crew Male Adult Yes
## 2010 Crew Male Adult Yes
## 2011 Crew Male Adult Yes
## 2012 Crew Male Adult Yes
## 2013 Crew Male Adult Yes
## 2014 Crew Male Adult Yes
## 2015 Crew Male Adult Yes
## 2016 Crew Male Adult Yes
## 2017 Crew Male Adult Yes
## 2018 Crew Male Adult Yes
## 2019 Crew Male Adult Yes
## 2020 Crew Male Adult Yes
## 2021 Crew Male Adult Yes
## 2022 Crew Male Adult Yes
## 2023 Crew Male Adult Yes
## 2024 Crew Male Adult Yes
## 2025 Crew Male Adult Yes
## 2026 Crew Male Adult Yes
## 2027 Crew Male Adult Yes
## 2028 Crew Male Adult Yes
## 2029 Crew Male Adult Yes
## 2030 Crew Male Adult Yes
## 2031 Crew Male Adult Yes
## 2032 Crew Male Adult Yes
## 2033 Crew Male Adult Yes
## 2034 Crew Male Adult Yes
## 2035 Crew Male Adult Yes
## 2036 Crew Male Adult Yes
## 2037 Crew Male Adult Yes
## 2038 Crew Male Adult Yes
## 2039 Crew Male Adult Yes
## 2040 Crew Male Adult Yes
## 2041 Crew Male Adult Yes
## 2042 Crew Male Adult Yes
## 2043 Crew Male Adult Yes
## 2044 Crew Male Adult Yes
## 2045 Crew Male Adult Yes
## 2046 Crew Male Adult Yes
## 2047 Crew Male Adult Yes
## 2048 Crew Male Adult Yes
## 2049 Crew Male Adult Yes
## 2050 Crew Male Adult Yes
## 2051 Crew Male Adult Yes
## 2052 Crew Male Adult Yes
## 2053 Crew Male Adult Yes
## 2054 Crew Male Adult Yes
## 2055 Crew Male Adult Yes
## 2056 Crew Male Adult Yes
## 2057 Crew Male Adult Yes
## 2058 Crew Male Adult Yes
## 2059 Crew Male Adult Yes
## 2060 Crew Male Adult Yes
## 2061 Crew Male Adult Yes
## 2062 Crew Male Adult Yes
## 2063 Crew Male Adult Yes
## 2064 Crew Male Adult Yes
## 2065 Crew Male Adult Yes
## 2066 Crew Male Adult Yes
## 2067 Crew Male Adult Yes
## 2068 Crew Male Adult Yes
## 2069 Crew Male Adult Yes
## 2070 Crew Male Adult Yes
## 2071 Crew Male Adult Yes
## 2072 Crew Male Adult Yes
## 2073 Crew Male Adult Yes
## 2074 Crew Male Adult Yes
## 2075 Crew Male Adult Yes
## 2076 Crew Male Adult Yes
## 2077 Crew Male Adult Yes
## 2078 Crew Male Adult Yes
## 2079 Crew Male Adult Yes
## 2080 Crew Male Adult Yes
## 2081 Crew Male Adult Yes
## 2082 Crew Male Adult Yes
## 2083 Crew Male Adult Yes
## 2084 Crew Male Adult Yes
## 2085 Crew Male Adult Yes
## 2086 Crew Male Adult Yes
## 2087 Crew Male Adult Yes
## 2088 Crew Male Adult Yes
## 2089 Crew Male Adult Yes
## 2090 Crew Male Adult Yes
## 2091 Crew Male Adult Yes
## 2092 Crew Male Adult Yes
## 2093 Crew Male Adult Yes
## 2094 Crew Male Adult Yes
## 2095 Crew Male Adult Yes
## 2096 Crew Male Adult Yes
## 2097 Crew Male Adult Yes
## 2098 Crew Male Adult Yes
## 2099 Crew Male Adult Yes
## 2100 Crew Male Adult Yes
## 2101 Crew Male Adult Yes
## 2102 Crew Male Adult Yes
## 2103 Crew Male Adult Yes
## 2104 Crew Male Adult Yes
## 2105 Crew Male Adult Yes
## 2106 Crew Male Adult Yes
## 2107 Crew Male Adult Yes
## 2108 Crew Male Adult Yes
## 2109 Crew Male Adult Yes
## 2110 Crew Male Adult Yes
## 2111 Crew Male Adult Yes
## 2112 Crew Male Adult Yes
## 2113 Crew Male Adult Yes
## 2114 Crew Male Adult Yes
## 2115 Crew Male Adult Yes
## 2116 Crew Male Adult Yes
## 2117 Crew Male Adult Yes
## 2118 Crew Male Adult Yes
## 2119 Crew Male Adult Yes
## 2120 Crew Male Adult Yes
## 2121 Crew Male Adult Yes
## 2122 Crew Male Adult Yes
## 2123 Crew Male Adult Yes
## 2124 Crew Male Adult Yes
## 2125 Crew Male Adult Yes
## 2126 Crew Male Adult Yes
## 2127 Crew Male Adult Yes
## 2128 Crew Male Adult Yes
## 2129 Crew Male Adult Yes
## 2130 Crew Male Adult Yes
## 2131 Crew Male Adult Yes
## 2132 Crew Male Adult Yes
## 2133 Crew Male Adult Yes
## 2134 Crew Male Adult Yes
## 2135 Crew Male Adult Yes
## 2136 Crew Male Adult Yes
## 2137 Crew Male Adult Yes
## 2138 Crew Male Adult Yes
## 2139 Crew Male Adult Yes
## 2140 Crew Male Adult Yes
## 2141 Crew Male Adult Yes
## 2142 Crew Male Adult Yes
## 2143 Crew Male Adult Yes
## 2144 Crew Male Adult Yes
## 2145 Crew Male Adult Yes
## 2146 Crew Male Adult Yes
## 2147 Crew Male Adult Yes
## 2148 Crew Male Adult Yes
## 2149 Crew Male Adult Yes
## 2150 Crew Male Adult Yes
## 2151 Crew Male Adult Yes
## 2152 Crew Male Adult Yes
## 2153 Crew Male Adult Yes
## 2154 Crew Male Adult Yes
## 2155 Crew Male Adult Yes
## 2156 Crew Male Adult Yes
## 2157 Crew Male Adult Yes
## 2158 Crew Male Adult Yes
## 2159 Crew Male Adult Yes
## 2160 Crew Male Adult Yes
## 2161 Crew Male Adult Yes
## 2162 Crew Male Adult Yes
## 2163 Crew Male Adult Yes
## 2164 Crew Male Adult Yes
## 2165 Crew Male Adult Yes
## 2166 Crew Male Adult Yes
## 2167 Crew Male Adult Yes
## 2168 Crew Male Adult Yes
## 2169 Crew Male Adult Yes
## 2170 Crew Male Adult Yes
## 2171 Crew Male Adult Yes
## 2172 Crew Male Adult Yes
## 2173 Crew Male Adult Yes
## 2174 Crew Male Adult Yes
## 2175 Crew Male Adult Yes
## 2176 Crew Male Adult Yes
## 2177 Crew Male Adult Yes
## 2178 Crew Male Adult Yes
## 2179 Crew Female Adult No
## 2180 Crew Female Adult No
## 2181 Crew Female Adult No
## 2182 Crew Female Adult Yes
## 2183 Crew Female Adult Yes
## 2184 Crew Female Adult Yes
## 2185 Crew Female Adult Yes
## 2186 Crew Female Adult Yes
## 2187 Crew Female Adult Yes
## 2188 Crew Female Adult Yes
## 2189 Crew Female Adult Yes
## 2190 Crew Female Adult Yes
## 2191 Crew Female Adult Yes
## 2192 Crew Female Adult Yes
## 2193 Crew Female Adult Yes
## 2194 Crew Female Adult Yes
## 2195 Crew Female Adult Yes
## 2196 Crew Female Adult Yes
## 2197 Crew Female Adult Yes
## 2198 Crew Female Adult Yes
## 2199 Crew Female Adult Yes
## 2200 Crew Female Adult Yes
## 2201 Crew Female Adult YesTitanic1$Survived <- as.integer(Titanic1$Survived)
pred = predict(Titanic.glm,Titanic1)
print(pred)## 1 2 3 4 5 6 7
## 0.6853195 0.6853195 0.6853195 0.6853195 0.6853195 -0.3762229 -0.3762229
## 8 9 10 11 12 13 14
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 15 16 17 18 19 20 21
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 22 23 24 25 26 27 28
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 29 30 31 32 33 34 35
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 36 37 38 39 40 41 42
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 43 44 45 46 47 48 49
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 50 51 52 53 54 55 56
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 57 58 59 60 61 62 63
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 64 65 66 67 68 69 70
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 71 72 73 74 75 76 77
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 78 79 80 81 82 83 84
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 85 86 87 88 89 90 91
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 92 93 94 95 96 97 98
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 99 100 101 102 103 104 105
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 106 107 108 109 110 111 112
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 113 114 115 116 117 118 119
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 120 121 122 123 124 125 126
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 127 128 129 130 131 132 133
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 134 135 136 137 138 139 140
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 141 142 143 144 145 146 147
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 148 149 150 151 152 153 154
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 155 156 157 158 159 160 161
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 162 163 164 165 166 167 168
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 169 170 171 172 173 174 175
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229
## 176 177 178 179 180 181 182
## -0.3762229 -0.3762229 -0.3762229 -0.3762229 -0.3762229 3.1053798 2.0438374
## 183 184 185 186 187 188 189
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 190 191 192 193 194 195 196
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 197 198 199 200 201 202 203
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 204 205 206 207 208 209 210
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 211 212 213 214 215 216 217
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 218 219 220 221 222 223 224
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 225 226 227 228 229 230 231
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 232 233 234 235 236 237 238
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 239 240 241 242 243 244 245
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 246 247 248 249 250 251 252
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 253 254 255 256 257 258 259
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 260 261 262 263 264 265 266
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 267 268 269 270 271 272 273
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 274 275 276 277 278 279 280
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 281 282 283 284 285 286 287
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 288 289 290 291 292 293 294
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 295 296 297 298 299 300 301
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 302 303 304 305 306 307 308
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 309 310 311 312 313 314 315
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 316 317 318 319 320 321 322
## 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374 2.0438374
## 323 324 325 326 327 328 329
## 2.0438374 2.0438374 2.0438374 -0.3327755 -0.3327755 -0.3327755 -0.3327755
## 330 331 332 333 334 335 336
## -0.3327755 -0.3327755 -0.3327755 -0.3327755 -0.3327755 -0.3327755 -0.3327755
## 337 338 339 340 341 342 343
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 344 345 346 347 348 349 350
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 351 352 353 354 355 356 357
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 358 359 360 361 362 363 364
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 365 366 367 368 369 370 371
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 372 373 374 375 376 377 378
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 379 380 381 382 383 384 385
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 386 387 388 389 390 391 392
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 393 394 395 396 397 398 399
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 400 401 402 403 404 405 406
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 407 408 409 410 411 412 413
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 414 415 416 417 418 419 420
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 421 422 423 424 425 426 427
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 428 429 430 431 432 433 434
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 435 436 437 438 439 440 441
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 442 443 444 445 446 447 448
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 449 450 451 452 453 454 455
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 456 457 458 459 460 461 462
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 463 464 465 466 467 468 469
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 470 471 472 473 474 475 476
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 477 478 479 480 481 482 483
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 484 485 486 487 488 489 490
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 491 492 493 494 495 496 497
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 498 499 500 501 502 503 504
## -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179 -1.3943179
## 505 506 507 508 509 510 511
## 2.0872848 2.0872848 2.0872848 2.0872848 2.0872848 2.0872848 2.0872848
## 512 513 514 515 516 517 518
## 2.0872848 2.0872848 2.0872848 2.0872848 2.0872848 2.0872848 1.0257425
## 519 520 521 522 523 524 525
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 526 527 528 529 530 531 532
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 533 534 535 536 537 538 539
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 540 541 542 543 544 545 546
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 547 548 549 550 551 552 553
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 554 555 556 557 558 559 560
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 561 562 563 564 565 566 567
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 568 569 570 571 572 573 574
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 575 576 577 578 579 580 581
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 582 583 584 585 586 587 588
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 589 590 591 592 593 594 595
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 596 597 598 599 600 601 602
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 603 604 605 606 607 608 609
## 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425 1.0257425
## 610 611 612 613 614 615 616
## 1.0257425 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428
## 617 618 619 620 621 622 623
## -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428
## 624 625 626 627 628 629 630
## -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428
## 631 632 633 634 635 636 637
## -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428
## 638 639 640 641 642 643 644
## -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428
## 645 646 647 648 649 650 651
## -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428
## 652 653 654 655 656 657 658
## -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428 -1.0924428
## 659 660 661 662 663 664 665
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 666 667 668 669 670 671 672
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 673 674 675 676 677 678 679
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 680 681 682 683 684 685 686
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 687 688 689 690 691 692 693
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 694 695 696 697 698 699 700
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 701 702 703 704 705 706 707
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 708 709 710 711 712 713 714
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 715 716 717 718 719 720 721
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 722 723 724 725 726 727 728
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 729 730 731 732 733 734 735
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 736 737 738 739 740 741 742
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 743 744 745 746 747 748 749
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 750 751 752 753 754 755 756
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 757 758 759 760 761 762 763
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 764 765 766 767 768 769 770
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 771 772 773 774 775 776 777
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 778 779 780 781 782 783 784
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 785 786 787 788 789 790 791
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 792 793 794 795 796 797 798
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 799 800 801 802 803 804 805
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 806 807 808 809 810 811 812
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 813 814 815 816 817 818 819
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 820 821 822 823 824 825 826
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 827 828 829 830 831 832 833
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 834 835 836 837 838 839 840
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 841 842 843 844 845 846 847
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 848 849 850 851 852 853 854
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 855 856 857 858 859 860 861
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 862 863 864 865 866 867 868
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 869 870 871 872 873 874 875
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 876 877 878 879 880 881 882
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 883 884 885 886 887 888 889
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 890 891 892 893 894 895 896
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 897 898 899 900 901 902 903
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 904 905 906 907 908 909 910
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 911 912 913 914 915 916 917
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 918 919 920 921 922 923 924
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 925 926 927 928 929 930 931
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 932 933 934 935 936 937 938
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 939 940 941 942 943 944 945
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 946 947 948 949 950 951 952
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 953 954 955 956 957 958 959
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 960 961 962 963 964 965 966
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 967 968 969 970 971 972 973
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 974 975 976 977 978 979 980
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 981 982 983 984 985 986 987
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 988 989 990 991 992 993 994
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 995 996 997 998 999 1000 1001
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1002 1003 1004 1005 1006 1007 1008
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1009 1010 1011 1012 1013 1014 1015
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1016 1017 1018 1019 1020 1021 1022
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1023 1024 1025 1026 1027 1028 1029
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1030 1031 1032 1033 1034 1035 1036
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1037 1038 1039 1040 1041 1042 1043
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1044 1045 1046 1047 1048 1049 1050
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1051 1052 1053 1054 1055 1056 1057
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1058 1059 1060 1061 1062 1063 1064
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1065 1066 1067 1068 1069 1070 1071
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1072 1073 1074 1075 1076 1077 1078
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1079 1080 1081 1082 1083 1084 1085
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1086 1087 1088 1089 1090 1091 1092
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1093 1094 1095 1096 1097 1098 1099
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1100 1101 1102 1103 1104 1105 1106
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1107 1108 1109 1110 1111 1112 1113
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1114 1115 1116 1117 1118 1119 1120
## -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851 -2.1539851
## 1121 1122 1123 1124 1125 1126 1127
## 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176
## 1128 1129 1130 1131 1132 1133 1134
## 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176
## 1135 1136 1137 1138 1139 1140 1141
## 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176
## 1142 1143 1144 1145 1146 1147 1148
## 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176 1.3276176
## 1149 1150 1151 1152 1153 1154 1155
## 1.3276176 1.3276176 1.3276176 0.2660752 0.2660752 0.2660752 0.2660752
## 1156 1157 1158 1159 1160 1161 1162
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1163 1164 1165 1166 1167 1168 1169
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1170 1171 1172 1173 1174 1175 1176
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1177 1178 1179 1180 1181 1182 1183
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1184 1185 1186 1187 1188 1189 1190
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1191 1192 1193 1194 1195 1196 1197
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1198 1199 1200 1201 1202 1203 1204
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1205 1206 1207 1208 1209 1210 1211
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1212 1213 1214 1215 1216 1217 1218
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1219 1220 1221 1222 1223 1224 1225
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1226 1227 1228 1229 1230 1231 1232
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1233 1234 1235 1236 1237 1238 1239
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1240 1241 1242 1243 1244 1245 1246
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1247 1248 1249 1250 1251 1252 1253
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1254 1255 1256 1257 1258 1259 1260
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1261 1262 1263 1264 1265 1266 1267
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1268 1269 1270 1271 1272 1273 1274
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1275 1276 1277 1278 1279 1280 1281
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1282 1283 1284 1285 1286 1287 1288
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1289 1290 1291 1292 1293 1294 1295
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1296 1297 1298 1299 1300 1301 1302
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1303 1304 1305 1306 1307 1308 1309
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1310 1311 1312 1313 1314 1315 1316
## 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752 0.2660752
## 1317 1318 1319 1320 1321 1322 1323
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1324 1325 1326 1327 1328 1329 1330
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1331 1332 1333 1334 1335 1336 1337
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1338 1339 1340 1341 1342 1343 1344
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1345 1346 1347 1348 1349 1350 1351
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1352 1353 1354 1355 1356 1357 1358
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1359 1360 1361 1362 1363 1364 1365
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1366 1367 1368 1369 1370 1371 1372
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1373 1374 1375 1376 1377 1378 1379
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1380 1381 1382 1383 1384 1385 1386
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1387 1388 1389 1390 1391 1392 1393
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1394 1395 1396 1397 1398 1399 1400
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1401 1402 1403 1404 1405 1406 1407
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1408 1409 1410 1411 1412 1413 1414
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1415 1416 1417 1418 1419 1420 1421
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1422 1423 1424 1425 1426 1427 1428
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1429 1430 1431 1432 1433 1434 1435
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1436 1437 1438 1439 1440 1441 1442
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1443 1444 1445 1446 1447 1448 1449
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1450 1451 1452 1453 1454 1455 1456
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1457 1458 1459 1460 1461 1462 1463
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1464 1465 1466 1467 1468 1469 1470
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1471 1472 1473 1474 1475 1476 1477
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1478 1479 1480 1481 1482 1483 1484
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1485 1486 1487 1488 1489 1490 1491
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1492 1493 1494 1495 1496 1497 1498
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1499 1500 1501 1502 1503 1504 1505
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1506 1507 1508 1509 1510 1511 1512
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1513 1514 1515 1516 1517 1518 1519
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1520 1521 1522 1523 1524 1525 1526
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1527 1528 1529 1530 1531 1532 1533
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1534 1535 1536 1537 1538 1539 1540
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1541 1542 1543 1544 1545 1546 1547
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1548 1549 1550 1551 1552 1553 1554
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1555 1556 1557 1558 1559 1560 1561
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1562 1563 1564 1565 1566 1567 1568
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1569 1570 1571 1572 1573 1574 1575
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1576 1577 1578 1579 1580 1581 1582
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1583 1584 1585 1586 1587 1588 1589
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1590 1591 1592 1593 1594 1595 1596
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1597 1598 1599 1600 1601 1602 1603
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1604 1605 1606 1607 1608 1609 1610
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1611 1612 1613 1614 1615 1616 1617
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1618 1619 1620 1621 1622 1623 1624
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1625 1626 1627 1628 1629 1630 1631
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1632 1633 1634 1635 1636 1637 1638
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1639 1640 1641 1642 1643 1644 1645
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1646 1647 1648 1649 1650 1651 1652
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1653 1654 1655 1656 1657 1658 1659
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1660 1661 1662 1663 1664 1665 1666
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1667 1668 1669 1670 1671 1672 1673
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1674 1675 1676 1677 1678 1679 1680
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1681 1682 1683 1684 1685 1686 1687
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1688 1689 1690 1691 1692 1693 1694
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1695 1696 1697 1698 1699 1700 1701
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1702 1703 1704 1705 1706 1707 1708
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1709 1710 1711 1712 1713 1714 1715
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1716 1717 1718 1719 1720 1721 1722
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1723 1724 1725 1726 1727 1728 1729
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1730 1731 1732 1733 1734 1735 1736
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1737 1738 1739 1740 1741 1742 1743
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1744 1745 1746 1747 1748 1749 1750
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1751 1752 1753 1754 1755 1756 1757
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1758 1759 1760 1761 1762 1763 1764
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1765 1766 1767 1768 1769 1770 1771
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1772 1773 1774 1775 1776 1777 1778
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1779 1780 1781 1782 1783 1784 1785
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1786 1787 1788 1789 1790 1791 1792
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1793 1794 1795 1796 1797 1798 1799
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1800 1801 1802 1803 1804 1805 1806
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1807 1808 1809 1810 1811 1812 1813
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1814 1815 1816 1817 1818 1819 1820
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1821 1822 1823 1824 1825 1826 1827
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1828 1829 1830 1831 1832 1833 1834
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1835 1836 1837 1838 1839 1840 1841
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1842 1843 1844 1845 1846 1847 1848
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1849 1850 1851 1852 1853 1854 1855
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1856 1857 1858 1859 1860 1861 1862
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1863 1864 1865 1866 1867 1868 1869
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1870 1871 1872 1873 1874 1875 1876
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1877 1878 1879 1880 1881 1882 1883
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1884 1885 1886 1887 1888 1889 1890
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1891 1892 1893 1894 1895 1896 1897
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1898 1899 1900 1901 1902 1903 1904
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1905 1906 1907 1908 1909 1910 1911
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1912 1913 1914 1915 1916 1917 1918
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1919 1920 1921 1922 1923 1924 1925
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1926 1927 1928 1929 1930 1931 1932
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1933 1934 1935 1936 1937 1938 1939
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1940 1941 1942 1943 1944 1945 1946
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1947 1948 1949 1950 1951 1952 1953
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1954 1955 1956 1957 1958 1959 1960
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1961 1962 1963 1964 1965 1966 1967
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1968 1969 1970 1971 1972 1973 1974
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1975 1976 1977 1978 1979 1980 1981
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1982 1983 1984 1985 1986 1987 1988
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1989 1990 1991 1992 1993 1994 1995
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 1996 1997 1998 1999 2000 2001 2002
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2003 2004 2005 2006 2007 2008 2009
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2010 2011 2012 2013 2014 2015 2016
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2017 2018 2019 2020 2021 2022 2023
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2024 2025 2026 2027 2028 2029 2030
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2031 2032 2033 2034 2035 2036 2037
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2038 2039 2040 2041 2042 2043 2044
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2045 2046 2047 2048 2049 2050 2051
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2052 2053 2054 2055 2056 2057 2058
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2059 2060 2061 2062 2063 2064 2065
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2066 2067 2068 2069 2070 2071 2072
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2073 2074 2075 2076 2077 2078 2079
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2080 2081 2082 2083 2084 2085 2086
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2087 2088 2089 2090 2091 2092 2093
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2094 2095 2096 2097 2098 2099 2100
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2101 2102 2103 2104 2105 2106 2107
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2108 2109 2110 2111 2112 2113 2114
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2115 2116 2117 2118 2119 2120 2121
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2122 2123 2124 2125 2126 2127 2128
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2129 2130 2131 2132 2133 2134 2135
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2136 2137 2138 2139 2140 2141 2142
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2143 2144 2145 2146 2147 2148 2149
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2150 2151 2152 2153 2154 2155 2156
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2157 2158 2159 2160 2161 2162 2163
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2164 2165 2166 2167 2168 2169 2170
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2171 2172 2173 2174 2175 2176 2177
## -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991 -1.2338991
## 2178 2179 2180 2181 2182 2183 2184
## -1.2338991 1.1861613 1.1861613 1.1861613 1.1861613 1.1861613 1.1861613
## 2185 2186 2187 2188 2189 2190 2191
## 1.1861613 1.1861613 1.1861613 1.1861613 1.1861613 1.1861613 1.1861613
## 2192 2193 2194 2195 2196 2197 2198
## 1.1861613 1.1861613 1.1861613 1.1861613 1.1861613 1.1861613 1.1861613
## 2199 2200 2201
## 1.1861613 1.1861613 1.1861613pred1 <- ifelse(pred >0, 1, 0)
pred_b <- ifelse(pred > 0.5, 1, 0)
#dummy_tai <- makedummies(Titanic1, basal_level = FALSE) #
#glimpse(dummy_tai)
#table(Titanic1$survived, pred_b)
(result <- table(pred, Titanic1$Survived))##
## pred 1 2
## -2.15398514125875 387 75
## -1.3943178751055 154 14
## -1.23389907889122 670 192
## -1.09244276499858 35 13
## -0.376222923517137 118 57
## -0.33277549884534 0 11
## 0.266075204547286 89 76
## 0.685319452743026 0 5
## 1.02574247070053 13 80
## 1.18616126691481 3 20
## 1.32761758080745 17 14
## 2.0438374222889 4 140
## 2.08728484696069 0 13
## 3.10537979854906 0 1##############################################いったん終了し、決定木へ
ロジスティック回帰分析と決定木を構築するアルゴリズム（rの中でセットアップされているTitanicデータを使用.（train）と異なる）
- 決定木を構築するアルゴリズムには CHAID, C4.5 / C5.0 / See5, CART など様々ありますが、ここでは CART を採用します。
- CART で決定木を構築するメソッドは “rpart” です
- 参照： http://rmecab.jp/ranko/chap6.html
- 参照2：
- http://yuranhiko.hatenablog.com/entry/DataAnalysis_R_caret_DecisionTree
data(Titanic)
head(Titanic)## [1] 0 0 35 0 0 0Titanic1 <- expand.table(Titanic)
Titanic.glm <- glm(Survived ~ +Sex + Age+ Class, data = Titanic1, family = "binomial")
summary (Titanic.glm)##
## Call:
## glm(formula = Survived ~ +Sex + Age + Class, family = "binomial",
## data = Titanic1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0812 -0.7149 -0.6656 0.6858 2.1278
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6853 0.2730 2.510 0.0121 *
## SexFemale 2.4201 0.1404 17.236 < 2e-16 ***
## AgeAdult -1.0615 0.2440 -4.350 1.36e-05 ***
## Class2nd -1.0181 0.1960 -5.194 2.05e-07 ***
## Class3rd -1.7778 0.1716 -10.362 < 2e-16 ***
## ClassCrew -0.8577 0.1573 -5.451 5.00e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2769.5 on 2200 degrees of freedom
## Residual deviance: 2210.1 on 2195 degrees of freedom
## AIC: 2222.1
##
## Number of Fisher Scoring iterations: 4- 注目するのは，やはりCoefficientsのところです．
- まず(Intercept) ですが，これは切片です．より具体的にいうと，性別が男性，年齢が子供，等級が1等客室の乗客の場合の推定値です
- 実際にこの条件で予測をおこなってみると以下のようになります．
newData <- data.frame (Sex = "Male", Age = "Child", Class = "1st" )
predict(Titanic.glm, newdata = newData)## 1
## 0.6853195最初にnewDataという名前で，性別が男性，年齢が子供，等級が1等客室の乗客というデータを作っています．predict関数に，先のロジスティック回帰分析の結果と，いまの新規データを指定して実行してみると，予測値が出ます．
predict()関数を二度実行していますが，最初の出力は，先ほどの切片(Intercept)と同じ0.6853ですが，これは対数オッズです．
二度目の実行では，「type = “response”」を追加しています．これは対数オッズを確率に変える指定です． 結果の 0.66，約 66% が生き残る確率になります．
Coefficients: の欄の切片の下は，まず女性の場合対数オッズで2.4201が追加されます．これは対数を外すと(exp(2.4201)を計算すると)，約11で，女性の場合，生存率が 11 倍に跳ね上がることを意味します．
また年齢が成人(Adult)の係数は -1.0615 で負の値です．これは成人の場合，生存率が三分の１に減少することを意味します( exp (-1.06)を計算)．
以下同様に，Class2ndは等級が2等の場合，class3rdは3等の場合，そしてClassCrewは乗務員を表しますが，いずれもマイナスなので，生存率は下がっていることになります
決定木
決定木は，ある選択に影響を与えている要因を探る手法です．
先ほど作成したタイタニック号データを使って実例を示します．
Rで実行するには rpart という追加パッケージが必要
Titanics.rpart <- rpart (Survived ~ Sex + Age+ Class, data = Titanic1)
Titanics.rpart## n= 2201
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 2201 711 No (0.6769650 0.3230350)
## 2) Sex=Male 1731 367 No (0.7879838 0.2120162)
## 4) Age=Adult 1667 338 No (0.7972406 0.2027594) *
## 5) Age=Child 64 29 No (0.5468750 0.4531250)
## 10) Class=3rd 48 13 No (0.7291667 0.2708333) *
## 11) Class=1st,2nd 16 0 Yes (0.0000000 1.0000000) *
## 3) Sex=Female 470 126 Yes (0.2680851 0.7319149)
## 6) Class=3rd 196 90 No (0.5408163 0.4591837) *
## 7) Class=1st,2nd,Crew 274 20 Yes (0.0729927 0.9270073) *- Titanics.rpart <- rpart (Survived ~ Sex + Age+ Class, data = - Titanic1)の部分が，生存か否かに影響のあった変数として，性別，年齢，等級を検討してみることを表す命令です．
- 結果も表示しましたが，これはグラフ化したほうが分かりやすいです．ここでは，決定木を見やすくするパッケージ rpart.plot を使ってグラフ化した結果を表示します
prp(Titanics.rpart, type=2, extra=101,
nn=TRUE, fallen.leaves=TRUE, faclen=0, varlen=0,
shadow.col="grey", branch.lty=3, cex = 1.2, split.cex=1.2,
under.cex = 1.2)
-この図を出す事の別の方法が次に示されている - http://yuranhiko.hatenablog.com/entry/DataAnalysis_R_caret_DecisionTree - https://books.google.co.jp/books?id=eYlZDAAAQBAJ&pg=PT220&lpg=PT220&dq=titanic.rp&source=bl&ots=POQR9RPZYf&sig=ACfU3U0r5QrlXWctZY64-ElwIrNEk6m6hA&hl=ja&sa=X&ved=2ahUKEwi1r9Gw8_XmAhUHfXAKHRIxBywQ6AEwDXoECAkQAQ#v=onepage&q=titanic.rp&f=false - どちらも肝心な所が抜けている。（故意に抜いてある。）
- 下記にならいrpart.plot関数を使えば簡単にきれいにプロットできる事が分かった
- 参考：https://qiita.com/nkjm/items/e751e49c7d2c619cbeab
rpart.plot(Titanics.rpart)
元に戻ってロジスティック回帰分析予測モデルの構築
- ブートストラップ法による予測精度の検証(caret::train()を使用) #integer型をファクター型に変換
Titanic1$Survived <- as.factor(Titanic1$Survived) #integer型をファクター型に変換
train_acc_ver2 = glm(Survived ~ ., data = Titanic1, family = "binomial")#①
#glmは一般化線形モデルの事https://www.marketechlabo.com/r-glm-libraries/
summary(train_acc_ver2)##
## Call:
## glm(formula = Survived ~ ., family = "binomial", data = Titanic1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0812 -0.7149 -0.6656 0.6858 2.1278
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6853 0.2730 2.510 0.0121 *
## Class2nd -1.0181 0.1960 -5.194 2.05e-07 ***
## Class3rd -1.7778 0.1716 -10.362 < 2e-16 ***
## ClassCrew -0.8577 0.1573 -5.451 5.00e-08 ***
## SexFemale 2.4201 0.1404 17.236 < 2e-16 ***
## AgeAdult -1.0615 0.2440 -4.350 1.36e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2769.5 on 2200 degrees of freedom
## Residual deviance: 2210.1 on 2195 degrees of freedom
## AIC: 2222.1
##
## Number of Fisher Scoring iterations: 4train_acc_ver2 <- train(data = Titanic1,Survived ~ .,method = "glm", family = binomial())#②
# ①② どちらの文法でも良いみたいだ。
summary(train_acc_ver2)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0812 -0.7149 -0.6656 0.6858 2.1278
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6853 0.2730 2.510 0.0121 *
## Class2nd -1.0181 0.1960 -5.194 2.05e-07 ***
## Class3rd -1.7778 0.1716 -10.362 < 2e-16 ***
## ClassCrew -0.8577 0.1573 -5.451 5.00e-08 ***
## SexFemale 2.4201 0.1404 17.236 < 2e-16 ***
## AgeAdult -1.0615 0.2440 -4.350 1.36e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2769.5 on 2200 degrees of freedom
## Residual deviance: 2210.1 on 2195 degrees of freedom
## AIC: 2222.1
##
## Number of Fisher Scoring iterations: 4# ブートストラップ法による予測精度の検証(caret::train()を使用)
train_acc_ver2## Generalized Linear Model
##
## 2201 samples
## 3 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 2201, 2201, 2201, 2201, 2201, 2201, ...
## Resampling results:
##
## Accuracy Kappa
## 0.771475 0.4319736#この方法の制度では、Accuracyは、0.8139758になり、ランダムフォレスト法より落ちる
# 提出2
logit_ver2 <- glm(Survived ~ ., data = Titanic1, family = "binomial")
#fitted_ver2 <- predict(logit_ver2, newdata=test,type='response')
#fitted_ver2 <- ifelse(fitted_ver2 > 0.5,1,0)
#solution_ver2 <- data.frame(PassengerId = test$PassengerId, Survived = fitted_ver2)
#write.csv(solution_ver2, file = 'submit_ver2.csv', row.names = F)
#Titanic1SVM（Support Vector Machine）サポートベクターマシンによる回帰分析予測モデルの構築
#http://smrmkt.hatenablog.jp/entry/2012/12/20/232113
サポートベクターマシーンの参考サイト https://qiita.com/koshian2/items/baa51826147c3d538652 https://5ch.pub/cache/view/sim/1152969662?from_media=feed_atom
#R言語でSVMを利用するにはkernlabというパッケージを必要：http://yut.hatenablog.com/entry/20120827/1346024147
library(kernlab)##
## Attaching package: 'kernlab'## The following object is masked from 'package:purrr':
##
## cross## The following object is masked from 'package:ggplot2':
##
## alphadum <- select(.data = alldata,Survived, Pclass,Sex, Embarked, DFamsize, Title, Group, Wom_chd,PassengerId)
#CabinやNamen等分析に使用しないデータの除外は、
#exclude_cols = c("Cabin", "Name")として、dum = alldata[ !names(alldata) %in% exclude_cols ]としても良いようだ
not_dum <- select(.data = alldata, Age, SibSp, Parch, Fare, Famsize)
train2 <- cbind(dum, not_dum)[1:891,]#train2作成,892以降はSurvivedデータがないので削除
test <- cbind(dum, not_dum)[892:1309,]
head(train2)## Survived Pclass Sex Embarked DFamsize Title Group Wom_chd PassengerId Age
## 1 0 3 male S Small Mr No No 1 22
## 2 1 1 female C Small Mrs No Yes 2 38
## 3 1 3 female S Single Miss No Yes 3 26
## 4 1 1 female S Small Mrs No Yes 4 35
## 5 0 3 male S Single Mr No No 5 35
## 6 0 3 male Q Single Mr No No 6 29
## SibSp Parch Fare Famsize
## 1 1 0 7.2500 2
## 2 1 0 71.2833 2
## 3 0 0 7.9250 1
## 4 1 0 53.1000 2
## 5 0 0 8.0500 1
## 6 0 0 8.4583 1head(test)## Survived Pclass Sex Embarked DFamsize Title Group Wom_chd PassengerId
## 892 <NA> 3 male Q Single Mr No No 892
## 893 <NA> 3 female S Small Mrs No Yes 893
## 894 <NA> 2 male Q Single Mr No No 894
## 895 <NA> 3 male S Single Mr No No 895
## 896 <NA> 3 female S Small Mrs No Yes 896
## 897 <NA> 3 male S Single Mr No Yes 897
## Age SibSp Parch Fare Famsize
## 892 34.5 0 0 7.8292 1
## 893 47.0 1 0 7.0000 2
## 894 62.0 0 0 9.6875 1
## 895 27.0 0 0 8.6625 1
## 896 22.0 1 1 12.2875 3
## 897 14.0 0 0 9.2250 1sapply(train2, class)## Survived Pclass Sex Embarked DFamsize Title
## "factor" "factor" "factor" "factor" "factor" "factor"
## Group Wom_chd PassengerId Age SibSp Parch
## "factor" "factor" "integer" "numeric" "integer" "integer"
## Fare Famsize
## "numeric" "numeric"#方法0：http://yut.hatenablog.com/entry/20120827/1346024147
model <- ksvm(Survived ~ ., data=train)
model## Support Vector Machine object of class "ksvm"
##
## SV type: eps-svr (regression)
## parameter : epsilon = 0.1 cost C = 1
##
## Gaussian Radial Basis kernel function.
## Hyperparameter : sigma = 0.0364785576784555
##
## Number of Support Vectors : 152
##
## Objective Function Value : -78.1619
## Training error : 0.443059#方法1https://data-science.gr.jp/implementation/iml_r_svm.html
classifier=svm(Survived ~ ., data=train, method="C-classification", kernel="radial", gamma=0.1258925,cost=2.511886)
#prediction<-predict(classifier,train)
#table(predict(classifier, train), train$Survived)
#方法2:https://qiita.com/stkdev/items/f79af90db4799370e3aa
#model2 <- ksvm(formula, data=train, kernel ="rbfdot", kpar=list(sigma = 0.1), type=NULL, cross = 0,)
#predict関数で予測データを評価：
#prediction<-predict(model,train2)
#prediction
#方法3：http://kefism.hatenablog.com/entry/2017/04/22/203740：わかりやすい説明→次節に詳述
#予測結果と正解との比較
#table(prediction,train2$Survived)
#solution <- data.frame(PassengerID = test$PassengerId, Survived = prediction)
#solution
#write.csv(solution, file = 'test_Solution.csv', row.names = F)
#prediction2 = predict(model,train2)
#(result <- table(prediction2, train2$Survived))# ()で括って内容表示
#(accuracy_prediction = sum(diag(result)) / sum(result))SVM（Support Vector Machine）サポートベクターマシンによ機械学習予測モデルの構築その2-1
-1）前処理 http://kefism.hatenablog.com/entry/2017/04/22/203740
# Survivedと変数xのクロス集計の右側に
# 変数x内のカテゴリー比率を加えたものを表示する関数
S_table <- function(x, survived){
tbl <- table(x, survived)
row_sum <- apply(tbl, 1, sum)
s_ratio <- tbl[,2]/row_sum
return(cbind(tbl, s_ratio))
}
# クロス割合を表示する関数
# (クロス集計表の要素が割合)
rate_table <- function(x, y){
tbl <- table(x, y)
row_sum <- apply(tbl, 1, sum)
s_ratio <- tbl/row_sum
return(s_ratio)
}
d <- read.csv("train.csv")
d_t <- read.csv("test.csv")
# Cabinの処理
train <- d %>%
separate("Cabin", into=c("Cabin1", "Cabin2", "Cabin3", "Cabin4"), sep=" ") %>%
mutate(Cabin1 = substr(Cabin1, 1, 1),
Cabin2 = substr(Cabin2, 1, 1),
Cabin3 = substr(Cabin3, 1, 1),
Cabin4 = substr(Cabin4, 1, 1)) %>%
mutate(Cabin1 = if_else(Cabin1=="", "U", Cabin1),
Cabin2 = if_else(is.na(Cabin2), "U", Cabin2),
Cabin3 = if_else(is.na(Cabin3), "U", Cabin3),
Cabin4 = if_else(is.na(Cabin4), "U", Cabin4))## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 889 rows [1, 2,
## 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].# Nameの処理
train <- train %>%
separate("Name", into=c("Last_Name", "Title"), sep=",") %>%
mutate(Title = gsub("\\..+$", "", Title)) %>%
mutate(Title = gsub(" ", "", Title))
sum(train$Survived)/nrow(train)## [1] 0.3838384library(ggplot2)
# 2変数の関係の確認
# 対 Survived
# Pclass
S_table(train$Pclass, train$Survived)## 0 1 s_ratio
## 1 80 136 0.6296296
## 2 97 87 0.4728261
## 3 372 119 0.2423625# Sex
S_table(train$Sex, train$Survived)## 0 1 s_ratio
## female 81 233 0.7420382
## male 468 109 0.1889081# Title
S_table(train$Title, train$Survived)## 0 1 s_ratio
## Capt 1 0 0.0000000
## Col 1 1 0.5000000
## Don 1 0 0.0000000
## Dr 4 3 0.4285714
## Jonkheer 1 0 0.0000000
## Lady 0 1 1.0000000
## Major 1 1 0.5000000
## Master 17 23 0.5750000
## Miss 55 127 0.6978022
## Mlle 0 2 1.0000000
## Mme 0 1 1.0000000
## Mr 436 81 0.1566731
## Mrs 26 99 0.7920000
## Ms 0 1 1.0000000
## Rev 6 0 0.0000000
## Sir 0 1 1.0000000
## theCountess 0 1 1.0000000# Embarked
S_table(train$Embarked, train$Survived)## 0 1 s_ratio
## 0 2 1.0000000
## C 75 93 0.5535714
## Q 47 30 0.3896104
## S 427 217 0.3369565# SibSp
S_table(train$SibSp, train$Survived)## 0 1 s_ratio
## 0 398 210 0.3453947
## 1 97 112 0.5358852
## 2 15 13 0.4642857
## 3 12 4 0.2500000
## 4 15 3 0.1666667
## 5 5 0 0.0000000
## 8 7 0 0.0000000# Parch
S_table(train$Parch, train$Survived)## 0 1 s_ratio
## 0 445 233 0.3436578
## 1 53 65 0.5508475
## 2 40 40 0.5000000
## 3 2 3 0.6000000
## 4 4 0 0.0000000
## 5 4 1 0.2000000
## 6 1 0 0.0000000# Cabin1
S_table(train$Cabin1, train$Survived)## 0 1 s_ratio
## A 8 7 0.4666667
## B 12 35 0.7446809
## C 24 35 0.5932203
## D 8 25 0.7575758
## E 8 24 0.7500000
## F 5 8 0.6153846
## G 2 2 0.5000000
## T 1 0 0.0000000
## U 481 206 0.2998544#S_table(train$Family_size, train$Survived)#Family_sizem未定義
# 気になった変数
table(train$Cabin1, train$Pclass)##
## 1 2 3
## A 15 0 0
## B 47 0 0
## C 59 0 0
## D 29 4 0
## E 25 4 3
## F 0 8 5
## G 0 0 4
## T 1 0 0
## U 40 168 479rate_table(train$Embarked, train$Pclass)## y
## x 1 2 3
## 1.00000000 0.00000000 0.00000000
## C 0.50595238 0.10119048 0.39285714
## Q 0.02597403 0.03896104 0.93506494
## S 0.19720497 0.25465839 0.54813665rate_table(train$Embarked, train$Cabin1) ## y
## x A B C D E F
## 0.000000000 1.000000000 0.000000000 0.000000000 0.000000000 0.000000000
## C 0.041666667 0.130952381 0.125000000 0.077380952 0.029761905 0.005952381
## Q 0.000000000 0.000000000 0.025974026 0.000000000 0.012987013 0.012987013
## S 0.012422360 0.035714286 0.055900621 0.031055901 0.040372671 0.017080745
## y
## x G T U
## 0.000000000 0.000000000 0.000000000
## C 0.000000000 0.000000000 0.589285714
## Q 0.000000000 0.000000000 0.948051948
## S 0.006211180 0.001552795 0.799689441#Age (散布図)
plot(train$Age, train$Survived+runif(nrow(train), -0.3, 0.3))
# Age (密度トレース)
dat <- train %>%
select(Survived, Age) %>%
mutate(Survived = as.factor(Survived))
g <- ggplot(dat, aes(Age, colour=Survived, fill=Survived, alpha=0.5)) +
geom_density()
plot(g)## Warning: Removed 177 rows containing non-finite values (stat_density).
# Fare (散布図)
plot(train$Fare, train$Survived+runif(nrow(train), -0.3, 0.3))
# Fare (密度トレース)
dat <- train %>%
select(Survived, Fare) %>%
mutate(Survived = as.factor(Survived))
g <- ggplot(dat, aes(Fare, colour=Survived, fill=Survived, alpha=0.5)) +
geom_density()
plot(g)
# PclassとFareの密度トレース
dat <- train %>%
select(Pclass, Fare) %>%
mutate(Pclass = as.factor(Pclass))
g <- ggplot(dat, aes(Fare, colour=Pclass, fill=Pclass, alpha=0.5)) +
geom_density()
plot(g)
# 3変数の関係の確認
# SurvivedとFareとPclass
plot(train$Fare, train$Pclass+runif(nrow(train), -0.3, 0.3),col=ifelse(train$Survived == 1, "red", "blue"))
#nrow(train) trainの行数
# SurvivedとFareとSex
train$Sex <- as.integer(train$Sex)
train$Sex <- ifelse(train$Sex >1, 2, 1)
#ｓｅｘ2値化#train$Sex+runif...の所はsexをinteger型にしないと演算ができない。
plot(train$Fare, train$Sex+runif(nrow(train), -0.3, 0.3),col=ifelse(train$Survived == 1, "red", "blue"))
# SurvivedとFareとPclass
dat <- train %>%
select(Survived, Pclass, Fare) %>%
mutate(Survived = as.factor(Survived),
Pclass = as.factor(Pclass))
g <- ggplot(dat, aes(Fare, colour=Pclass, fill=Pclass, alpha=0.5)) +
geom_density() +
facet_wrap(~Survived, nrow=2)
plot(g)
SVM（Support Vector Machine）サポートベクターマシンによる機械学習予測モデルの構築その2-2
-２）データフレームを渡すとすべてのカラムについてダミー変数化してくれる関数、convDummies http://kefism.hatenablog.com/entry/2017/04/22/203740 http://kefism.hatenablog.com/entry/2017/02/12/120858
convDummies <- function(data, is.drop = FALSE){
library(dummies)
N <- ncol(data)
row_names <- names(data)
names_list <- c()
new_data <- rep(NA, nrow(data))
for(n in 1:N){
unique_value <- sort(unique(data[,n]))
dummied_data <- dummy(data[,n])
if(is.drop == TRUE){
new_data <- cbind(new_data, dummied_data[,-ncol(dummied_data)])
names_list <- c(names_list,
paste(row_names[n], unique_value, sep = ".")[-ncol(dummied_data)])
} else {
new_data <- cbind(new_data, dummied_data)
names_list <- c(names_list, paste(row_names[n], unique_value, sep = "."))
}
}
new_data <- as.data.frame(new_data)
names(new_data) <- c("temp", names_list)
return(new_data[,-1])
}# データ整形関数
createData <- function(){
library(dplyr)
library(tidyr)
library(ranger)
train <- read.csv("train.csv")
test <- read.csv("test.csv")
train$Embarked[train$Embarked == ""] <- "S"
Survived <- train$Survived
X <- train %>%
select(-Survived) %>%
bind_rows(test) %>%
select(-PassengerId, -Ticket)
# Cabinの処理
X <- X %>%
separate("Cabin", into=c("Cabin1", "Cabin2", "Cabin3", "Cabin4"), sep=" ") %>%
mutate(Cabin1 = substr(Cabin1, 1, 1),
Cabin2 = substr(Cabin2, 1, 1),
Cabin3 = substr(Cabin3, 1, 1),
Cabin4 = substr(Cabin4, 1, 1)) %>%
mutate(Cabin1 = if_else(Cabin1=="", "U", Cabin1),
Cabin2 = if_else(is.na(Cabin2), "U", Cabin2),
Cabin3 = if_else(is.na(Cabin3), "U", Cabin3),
Cabin4 = if_else(is.na(Cabin4), "U", Cabin4)) %>%
select(-c(Cabin2, Cabin3, Cabin4))
X <- X %>%
mutate(is_Cabin = if_else(Cabin1 == "U", "No", "Yes"))
# Nameの処理
Name_list <- c("Master", "Miss", "Mr", "Mrs", "Rev")
X <- X %>%
separate("Name", into=c("Last_Name", "Title"), sep=",") %>%
mutate(Title = gsub("\\..+$", "", Title)) %>%
mutate(Title = gsub(" ", "", Title)) %>%
mutate(Title = if_else(Title %in% Name_list, Title, "Otherwise"))
# Family_sizeの追加
X <- X %>%
mutate(Family_size = SibSp + Parch +1)
# Familly_sizeで家族をカテゴリー分け
X <- X %>%
mutate(Family_type = if_else(Family_size == 1, "singleton",
if_else(2 <= Family_size & Family_size <= 4, "middle", "large")))
# Ageの欠損値補完
age_for_na <-
na.omit(X) %>%
group_by(Pclass, Title) %>%
summarise(age_ave = mean(Age))
X <- X %>%
left_join(age_for_na) %>%
mutate(Age = if_else(is.na(Age), age_ave, Age)) %>%
select(-age_ave)
X$Age[is.na(X$Age)] <- mean(X$Age[!is.na(X$Age) & X$SibSp == 0 & X$Parch == 0])
X <- X %>%
mutate(Age_desc = if_else(0 <= Age & Age <= 6, "0_6",
if_else(7 <= Age & Age <=10, "7_10",
if_else(11 <= Age & Age <= 15, "11_15",
if_else(16 <= Age & 20 <= Age, "16_20",
if_else(21 <= Age & Age <= 30, "21_30", "30_"))))))
# Fareの欠損値補完と正規化
X$Fare[is.na(X$Fare)] <- 0.0
X$Fare <- (X$Fare - mean(X$Fare))/sd(X$Fare)
# カテゴリー変数のダミー化
df_category <- X %>%
select(Sex, Title, Pclass, Cabin1, is_Cabin, Family_type, Age_desc, Embarked)
X_continuous <- X %>%
select(-c(Sex, Title, Pclass, Cabin1, is_Cabin, Family_type, Age_desc, Embarked))
X_dummy <- convDummies(df_category, is.drop = TRUE)
X <- cbind(X_continuous, X_dummy)
# trainデータとtestデータに分割
train_new <- X[1:891,]
train_new <- data.frame(Survived = Survived) %>%
cbind(train_new)
test_new <- X[892:1309,]
# 自分以外の家族の生存割合を追加
fam_suv <- train_new %>%
group_by(Last_Name) %>%
summarise(f_n = n(), s_n = sum(Survived))
train_new <- train_new %>%
left_join(fam_suv) %>%
mutate(fam_suv_rate = if_else(f_n==1, 0, (s_n-Survived)/(f_n-1))) %>%
select(-f_n, -s_n, -Last_Name)
test_new <- test_new %>%
left_join(fam_suv) %>%
mutate(fam_suv_rate = if_else(Family_size==1, 0, s_n/f_n)) %>%
select(-f_n, -s_n, -Last_Name)
fam_suv_df <- train_new %>%
select(-Survived)
fam_suv_df_na <- test_new[is.na(test_new$fam_suv_rate),] %>%
select(-fam_suv_rate)
fam_suv.fit <- ranger(fam_suv_rate~., fam_suv_df[,29:31])
pred_fam_suv <- predict(fam_suv.fit, fam_suv_df_na)
test_new$fam_suv_rate[is.na(test_new$fam_suv_rate)] <- pred_fam_suv$predictions
train_test <- list()
train_test[[1]] <- train_new
train_test[[2]] <- test_new
return(train_test)
}-2:あらたに追加したfam_suv_rateが乗客の生存に影響を与えていそうかをちょっと確認
# 先程作成したデータ前処理関数で前処理をする。
data <- createData()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 1304 rows [1, 2,
## 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].## Joining, by = c("Pclass", "Title")## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored## Joining, by = "Last_Name"Joining, by = "Last_Name"train <- data[[1]]
test <- data[[2]]
# Surviveとfam_sv_rateの散布図
plot(train$fam_suv_rate+runif(nrow(train), -0.02, 0.02),
train$Survived+runif(nrow(train), -0.3, 0.3))
# Surviveとfam_sv_rateの密度トレース
dat <- train %>%
select(Survived, fam_suv_rate) %>%
mutate(Survived = as.factor(Survived))
g <- ggplot(dat, aes(fam_suv_rate, colour=Survived, fill=Survived, alpha=0.5)) +
geom_density()
plot(g)
-3:予測モデルの構築
# SVMモデルを構築するためのライブラリ
library(e1071)
# 先程作成したデータ前処理関数で前処理をする。
data <- createData()## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector## Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector
## Warning in bind_rows_(x, .id): binding character and factor vector, coercing
## into character vector## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 1304 rows [1, 2,
## 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].## Joining, by = c("Pclass", "Title")## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored
## Warning in model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE):
## non-list contrasts argument ignored## Joining, by = "Last_Name"Joining, by = "Last_Name"train <- data[[1]]
test <- data[[2]]
# 変数の重要度をみる
rf.fit <- ranger(as.factor(Survived)~., train, num.trees=4000,
importance = 'impurity')
# 変数の重要度を可視化
pd <- data.frame(Variable = names(rf.fit$variable.importance),
Importance = as.numeric(rf.fit$variable.importance)) %>%
arrange(Importance)
p <- ggplot(pd, aes(x=factor(Variable, levels=unique(Variable)), y=Importance)) +
geom_bar(stat="identity") +
xlab("Variables") +
coord_flip()
plot(p)
-4:ハイパーパラメータのチューニング SVMのハイパーパラメータcostとgammaをチューニングします。
# パラメータチューニング
# Cross Validation
svm.df <- train[(c("Survived", as.character(pd$Variable[17:31])))]
svm.df_t <- test[as.character(pd$Variable[17:31])]
tune_res <- tune.svm(as.factor(Survived)~., data=svm.df)
tune_res$best.model##
## Call:
## best.svm(x = as.factor(Survived) ~ ., data = svm.df)
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: radial
## cost: 1
##
## Number of Support Vectors: 391-5:予測:cost: 1,gamma: 0.06666667が良いことが分かった
svm.fit <- svm(as.factor(Survived)~., data=svm.df, cost=1, gamma=0.06666667)
# 予測
pred <- predict(svm.fit, svm.df_t)
prediction <- data.frame(PassengerId = 892:1309,
Survived = pred)
write.csv(prediction, "prediction.csv", row.names = FALSE)※余談：runif関数について http://www.f.waseda.jp/sakas/R/ROR/orSimulation.html runif(10)を実行するといつも同じ値になる。（擬似乱数）初期状態が同じならば、生成される関数値は同じになるのは当然 初期状態を変えるために set.seed()という関数が用意されています。任意の整数を引数として指定して関数を実行すると、初期状態が違う乱数が生成されます。
set.seed(2)
runif(nrow(train), -0.3,0.3)## [1] -0.1890706440 0.1214244216 0.0439958009 -0.1991688478 0.2663036033
## [6] 0.2660849752 -0.2225046140 0.2000692894 -0.0191888907 0.0299902450
## [11] 0.0316044402 -0.1566631443 0.1563079879 -0.1915079396 -0.0568306913
## [16] 0.2121290718 0.2858390937 -0.1645047233 -0.0331144625 -0.2550123452
## [21] 0.0971392551 -0.0674702744 0.2021335061 -0.2096991356 -0.0916366506
## [26] -0.0067360612 -0.2104518823 -0.0857624460 0.2775864290 -0.2205767980
## [31] -0.2937512848 -0.2012146548 0.1861152867 0.2213166221 0.0085690578
## [36] 0.0763177719 0.2066574018 -0.1290776554 0.1003353886 -0.2097181488
## [41] 0.2890367174 -0.1217935589 -0.2309495548 -0.2020794769 0.2664250849
## [46] 0.1769182941 0.2848127386 -0.0905469536 0.0011819285 0.1862383576
## [51] -0.2957345772 -0.2911836534 0.1100420537 0.2578321331 -0.1347592809
## [56] 0.1871158173 0.1715273477 0.2933412933 0.0683717458 0.1261114381
## [61] 0.1620167139 0.2321904943 0.0750730378 -0.1438199788 0.2154438707
## [66] -0.0375071988 -0.0671131454 -0.0230993369 -0.1687948840 -0.2604386937
## [71] -0.1345793840 -0.1137713415 -0.2746946844 -0.1891959221 -0.1899760631
## [76] 0.1532774499 -0.1271641607 0.2207067974 -0.0584143584 0.0436110021
## [81] -0.0896144550 0.1031993570 -0.2849697861 -0.0593393773 -0.1800140820
## [86] 0.2139150005 0.2829092571 -0.1057665379 0.1399148600 -0.0959590559
## [91] 0.2860531108 -0.0617901529 -0.0720006724 0.0362325781 -0.0217150925
## [96] -0.1819339040 -0.0438339584 -0.2441848875 -0.2308145239 -0.0359810075
## [101] -0.1794391623 -0.0434165563 0.2883599890 0.1973532753 -0.1278156870
## [106] 0.0575501381 0.2393831677 -0.0279737998 -0.2115493324 -0.2227938104
## [111] -0.2852061974 0.1417868250 -0.0759848613 0.0446261643 0.1951968080
## [116] 0.1882174043 0.2236178042 -0.2336670603 0.2716201421 0.0414012486
## [121] -0.2778789176 -0.1528254502 0.2873308795 0.2314423394 -0.1554102613
## [126] 0.1543269421 0.0377019112 -0.1169381429 0.1161924521 -0.0984326377
## [131] -0.1763343334 0.2515657535 -0.2863125301 0.2782558467 -0.1104808539
## [136] 0.0993650505 0.0201259820 0.1906780317 -0.1888418941 -0.0602894691
## [141] -0.1929280438 -0.1287394736 0.0776818962 -0.1199398310 -0.0337956231
## [146] 0.1381205640 0.1008981219 -0.1130057992 -0.0128531502 -0.1251539094
## [151] -0.1906954719 -0.0835424501 0.2422800186 -0.0635714295 0.1679283164
## [156] -0.1295046321 0.2122324602 -0.1966545189 -0.2524414907 -0.1247610136
## [161] 0.2042227067 0.2671608483 -0.2730208799 0.1550297446 -0.1218672012
## [166] 0.0906326633 -0.2490061294 0.2852886450 -0.2918250411 0.0233216710
## [171] 0.2794690810 -0.2393559335 -0.1459491944 0.2370883327 -0.0672334855
## [176] 0.1765712669 -0.0903619441 -0.2167534159 0.0903053901 0.0264025847
## [181] 0.2671464284 0.0430474479 0.2721801535 0.1681517992 -0.2290293274
## [186] 0.2022670853 -0.2477044094 0.1491599775 -0.2608910057 -0.2533826584
## [191] -0.2369497389 -0.0559881869 0.2849787079 -0.2010999025 0.0018301413
## [196] -0.1790674777 -0.1801668910 -0.1931189333 -0.1356799909 -0.2216278556
## [201] 0.2152149518 0.0870962171 0.0616791995 0.2831884271 -0.0740056599
## [206] 0.1914686923 -0.1639567443 -0.1760948986 -0.1834264835 -0.1556590767
## [211] 0.2878331832 0.0145500905 0.1958224325 0.1025312975 0.2866289021
## [216] -0.2654109289 -0.0979553056 -0.2924357942 -0.0822862286 -0.1741834198
## [221] -0.2087276186 0.2614874458 -0.0593442252 -0.0084981326 0.1088918677
## [226] 0.2124212919 0.2477503031 -0.0144854352 0.1282081798 0.1978520882
## [231] 0.1054787127 -0.0961790083 0.2344979410 0.0172975556 0.2246024824
## [236] -0.1448058433 0.0254676396 -0.1703992768 -0.1699676459 -0.0155755246
## [241] 0.2356404170 -0.1189698240 0.0331313878 0.1531297300 0.2738552545
## [246] 0.2725868915 -0.0999811659 0.0100104634 -0.2110862453 -0.0859082406
## [251] 0.1227397890 -0.2517566155 -0.1490634745 -0.2691388071 0.1431151816
## [256] -0.2917448956 -0.2745870976 -0.1309079228 -0.2755700339 0.0793683135
## [261] 0.1529048000 -0.2694320956 0.0777983122 -0.0857346324 0.1848848440
## [266] 0.1029985449 -0.2868677091 0.1137233801 0.2324127652 0.0223255588
## [271] 0.2310158151 0.2485047065 0.2093879683 -0.1238944585 0.1705118470
## [276] -0.0037788056 0.2895443960 -0.1540793516 -0.2562002883 -0.1610060421
## [281] -0.1320529851 0.2825761913 0.0953974689 0.1020672807 -0.1740968835
## [286] 0.2943734713 0.0204528603 0.0446236366 0.1633438419 -0.0857463415
## [291] -0.1460032609 -0.1101324742 0.1466661677 -0.0002004147 0.1252855112
## [296] -0.0510621287 -0.1740532704 -0.2514824109 -0.2044259290 0.0024537646
## [301] 0.2012644033 -0.0075300103 -0.0403608651 0.2284149508 0.1589470284
## [306] -0.2670834081 -0.1804693436 0.1120948132 0.2395504290 -0.2725567861
## [311] -0.2462291446 -0.0354739144 0.1667793886 -0.2159489438 0.1072492156
## [316] -0.2714411942 0.0633886535 -0.0299133348 0.1486685046 -0.1937102862
## [321] 0.0929142694 -0.2015349298 -0.1429717668 -0.2429775688 -0.0633239024
## [326] -0.1720690610 0.0843188403 -0.1454282062 -0.2432315743 -0.2577609952
## [331] -0.1869862181 0.2238830924 0.2886621566 -0.0654481794 -0.2892608933
## [336] -0.2732732377 -0.2353414480 0.0421805697 0.2034112556 0.1890178956
## [341] 0.2171090904 0.2119000912 0.1797026309 -0.2881609064 0.0136026578
## [346] -0.0818928993 0.0761908899 0.2495507437 -0.1903077181 -0.0308458089
## [351] -0.1457152285 0.0564711386 -0.0621087138 0.2241626448 -0.1950113758
## [356] 0.1857062396 0.1765336662 0.0629305563 -0.2687038487 0.1147933690
## [361] -0.2091538313 -0.1796556069 -0.2379077311 -0.2924028852 0.0915234620
## [366] -0.1547761502 -0.2226387978 -0.2650062163 0.1240973872 -0.2816685678
## [371] 0.2281945146 -0.2902133260 0.0060382026 0.0866016262 0.1180620108
## [376] 0.0947599331 -0.1460876753 0.2986634491 0.1156461068 -0.2696097999
## [381] -0.2390007702 -0.0119002858 -0.0183583206 -0.1402824573 -0.2464433639
## [386] 0.0668764409 -0.0630016064 0.0237714558 0.2169635748 0.1514445524
## [391] 0.1549281623 0.2543376319 -0.1026180916 -0.2061744521 -0.1708822089
## [396] -0.2323058822 -0.1825293452 0.2145966166 -0.1633757021 0.1602125610
## [401] 0.0702852979 0.0414384765 -0.2076177307 -0.2791000708 0.2987720741
## [406] 0.2013442101 0.0519372162 -0.0965299945 -0.1998699371 0.1844460906
## [411] 0.0896704813 0.1233673445 -0.0291055987 -0.2566695525 -0.0822216623
## [416] 0.0882255414 0.1353408708 -0.0465199835 0.0548694350 0.1787397342
## [421] 0.2085673390 -0.2996020672 -0.1200100830 0.1163321359 0.2782614869
## [426] -0.2221412205 -0.2545451348 0.0405277080 0.0330905586 -0.2630354761
## [431] 0.1045033875 0.2484104334 0.2326643231 -0.1051181733 -0.2443970076
## [436] 0.1986693744 -0.2236675791 0.2212167327 0.2718578864 -0.0005141676
## [441] 0.0928432783 -0.1837243399 0.1577135017 0.0284499298 -0.1717007599
## [446] 0.1150601621 0.2836880457 0.0336870548 0.0168469619 -0.1700435960
## [451] 0.1083391540 0.1476101001 -0.0214785731 -0.2175491625 0.1886277675
## [456] 0.1992967555 -0.0874139516 0.2872397021 0.2928210134 0.0807954007
## [461] 0.2652914269 -0.2915955225 -0.2848010459 -0.0082088082 -0.1392888113
## [466] 0.1584474343 -0.0071673805 0.1481384077 -0.2744605767 -0.0356143034
## [471] -0.2123604937 -0.0348340228 -0.1295314616 -0.2558254539 0.1112104347
## [476] 0.1768910756 0.0229256972 -0.2852473036 -0.1330080519 0.2045679503
## [481] -0.0195535117 0.1561268457 0.2988384456 0.1666984503 0.2277402474
## [486] -0.0038845717 0.1252307672 -0.0074570433 0.1692245498 0.2470868248
## [491] -0.1284046534 0.0650226833 0.0246002199 0.2978700672 0.0665068705
## [496] -0.1737649446 -0.1094422619 0.2435162312 -0.1059151753 0.0365436673
## [501] 0.1152033279 0.0359741505 -0.0943852758 -0.1593014999 -0.0422383457
## [506] 0.1420203969 -0.0752059186 -0.0067925003 0.1803070205 0.1098257740
## [511] 0.1114111322 -0.2432111329 -0.0715862396 -0.0558164409 -0.0673288455
## [516] 0.0551562019 0.2019388166 0.0123086661 -0.0408082282 0.2308376522
## [521] 0.2462196853 -0.2678759521 -0.0235056971 0.1303968131 -0.0757631608
## [526] -0.2820546411 -0.2833420912 0.1193726332 -0.1447184537 0.2344085336
## [531] -0.2514953960 0.2733831445 0.2839615507 -0.2159054293 0.1066835771
## [536] 0.1747184118 0.1206333193 -0.1368288889 -0.0087806513 0.2262811436
## [541] -0.2980425392 0.2748733391 -0.0112760432 0.2760915484 0.0011337443
## [546] -0.2865889200 -0.2338617596 0.1485396636 0.0594384194 0.1682578103
## [551] 0.2674650550 -0.0852622446 -0.0085004393 0.0158583669 0.2090150254
## [556] 0.0006967514 -0.0932625290 0.0497394817 0.0160835755 0.2441993960
## [561] 0.2874691049 -0.2911982474 0.1451990719 -0.0135248513 0.2397680135
## [566] -0.1127263305 -0.1410379906 0.0311277159 0.2469271524 -0.2355446008
## [571] 0.2716775863 0.0485354758 0.2277287163 0.1840785263 0.0014790128
## [576] 0.2016153135 0.2424844551 -0.2984926205 -0.0222257939 -0.1837024505
## [581] -0.2222989821 -0.2204269190 0.1329086701 0.0530459813 0.0213144969
## [586] 0.1542241232 -0.0552632878 -0.2476510167 0.2589569977 0.1339092473
## [591] -0.0830193012 -0.0367285298 0.0973580726 -0.2807823805 -0.2892134918
## [596] 0.1434717500 -0.2488388836 -0.1619192700 -0.1518951176 -0.2838120463
## [601] -0.0748817888 -0.2468426969 -0.0742831655 -0.1651150113 0.1870433275
## [606] -0.2150638415 -0.2821881057 -0.1651025060 0.1607822641 0.0826344185
## [611] 0.1712370771 -0.0998331440 0.0463476299 -0.1472040262 0.1929127500
## [616] -0.1382123896 0.2356688193 0.0990696701 -0.1512709738 0.2901268777
## [621] 0.1181033647 0.1001589430 -0.1081099078 -0.0933729455 -0.1153118384
## [626] -0.2851534281 -0.2854851998 0.1862859966 -0.1292839250 -0.1644206752
## [631] 0.1096345848 -0.0720058382 0.2329435792 0.0881624054 0.0281059074
## [636] 0.0990836370 0.0211244374 -0.1951137580 -0.0477006014 -0.0048005829
## [641] -0.1145781659 0.2458241716 0.2172421165 -0.1335653284 0.1929836400
## [646] 0.0716354230 0.0853665928 0.1489561717 0.0696357891 -0.1860674185
## [651] 0.1211556919 -0.0448999977 0.2002049788 -0.1064510709 0.2831061083
## [656] -0.0204656067 0.2601323033 0.0792053367 -0.0734486128 -0.2255976560
## [661] 0.1970640179 0.0073148672 -0.0209589704 0.2541606400 -0.0460829433
## [666] -0.2863643581 0.1602436738 -0.2820543719 0.2088721750 -0.2400570162
## [671] -0.0723904506 -0.0605929359 0.1668509786 -0.2843727688 -0.0051341701
## [676] 0.2065824433 0.2593317533 0.2190019950 0.1036843367 -0.1804090559
## [681] 0.0416706499 -0.2003143708 0.0904267101 -0.0944777376 -0.2407695647
## [686] -0.1725943761 0.0549236760 0.0104496516 0.1585696247 0.1023349789
## [691] -0.0595559394 -0.2619509366 -0.0750811096 -0.2632771133 0.2023832286
## [696] -0.1281735599 0.1777397814 0.0733246836 0.0894382178 0.0143962255
## [701] 0.2563539885 0.2395265318 0.1484649599 0.0330876026 -0.0865868730
## [706] -0.2669484854 0.2635314537 -0.0522737059 0.2940464768 -0.2956182340
## [711] 0.2514942921 -0.2043645458 -0.1960263296 -0.1209975920 0.1785974795
## [716] -0.1737054585 0.1612774299 0.0213460905 0.0294196987 -0.1379250818
## [721] 0.2640421454 0.2334450867 0.2536015957 -0.1188679925 -0.0840669740
## [726] 0.1714424296 -0.1144004926 0.2995619662 -0.2821705624 -0.1167444434
## [731] -0.2236673907 0.0627975589 -0.1056402539 0.2652025571 0.2278013835
## [736] -0.1112495920 -0.0753520971 0.0028333389 0.0099722482 -0.1799233365
## [741] -0.0421435200 -0.0664708673 0.2933015338 0.1125254754 -0.1735134093
## [746] -0.0309378189 0.2344620547 0.0757687412 -0.0082432066 -0.1259318206
## [751] -0.1476933827 -0.0652792328 -0.0591310329 0.2852489325 0.2966083776
## [756] -0.0541752726 0.0376911629 -0.2813151794 0.1643917494 0.2909411590
## [761] -0.0993443796 -0.0246176046 -0.1619820493 0.2526240780 0.0342663154
## [766] 0.0677026969 -0.1576213407 -0.0191056482 0.2435966724 0.0505414566
## [771] -0.2653723009 -0.1900502410 0.0110454412 -0.0633438871 -0.0068314721
## [776] -0.2091202939 0.2527146405 0.1299421156 0.1779014417 -0.0784092918
## [781] 0.0436434675 -0.0664656139 0.2458831773 0.0856466908 0.2312291910
## [786] -0.2585672912 0.1850105734 -0.2595106067 -0.0278057184 -0.1919364480
## [791] 0.0804817472 0.1579425146 -0.2026822182 -0.2060639053 0.2221674653
## [796] -0.1687693267 0.0545328016 0.1205038589 0.1925039290 0.1303872050
## [801] -0.0508535673 0.0188632546 -0.2980522715 -0.1487401660 -0.2068626480
## [806] 0.2273038748 -0.1778933023 0.0501170425 0.1826840318 -0.1411700506
## [811] -0.0565543630 -0.0717849308 -0.1554292375 0.2052418226 -0.2957403871
## [816] -0.0141507807 0.2247094297 0.0401271912 0.0004947703 0.2247126899
## [821] 0.0145763744 -0.2444693387 -0.2192570978 -0.2741874450 0.0477163587
## [826] -0.2550485609 -0.2344557425 -0.2838116993 -0.0302056655 0.1816674925
## [831] -0.2432275336 -0.2869640152 0.2195980376 0.0493223644 -0.2059258566
## [836] 0.0086421057 0.2654784174 0.2154439696 0.0686830020 0.0727376918
## [841] 0.1621244155 0.0355340781 -0.2372189110 -0.2035629782 0.2526106919
## [846] 0.2152976308 0.1610215763 -0.2870440645 0.2400178852 -0.0467666420
## [851] 0.1893723168 -0.2004057457 0.1213977266 -0.2120497143 0.1701905304
## [856] -0.1666168068 -0.2292200984 0.2703756181 0.0811964471 -0.0177397768
## [861] -0.2277463670 -0.0118830853 -0.0791761067 0.2454015871 -0.2082764195
## [866] 0.2133254907 0.2444288203 0.2399153521 0.0089673033 -0.0499317866
## [871] -0.0184947308 -0.1816461455 -0.1021323158 0.2732335354 0.0508950157
## [876] 0.0711406654 -0.0028270637 0.0109117570 -0.1048551772 0.0316904319
## [881] -0.2893301361 -0.2658164094 -0.0667593932 -0.2339644711 -0.2651434111
## [886] -0.1844370855 0.0699131310 0.0561750791 0.0586114075 0.2439574598
## [891] -0.0326856384————————————————————————-
ランダムフォレストでデータを分析するアルゴリズム
- 参考：https://qiita.com/nkjm/items/e751e49c7d2c619cbeab
- 手始めに使用するデータを選択する。Titanichはダメ、cordatahはダミー変数のため使えない。
- 質的変数／カテゴリカルデータはダミー変数に似ているが全く違う
- 質的変数を量的変数として扱う事ができるようにするのが（https://markezine.jp/article/detail/20790）
- 0,1のデータをダミー変数という。
- 以下の2行のおまじないで、あっという間に質的変数／カテゴリカルデータをダミー変数に加工できます！
- tmp <- dummyVars(~., data=sample1)
- sample1.dummy <- as.data.frame(predict(tmp, sample1))
- 1行目dummyVarsの“~.” ……このニョロニョロの後ろの “.”（ピリオド) は、全ての質的変数を対象とすることを意味します。
- data=sample1……対象とするデータはsample1ですよ！の意味です。
- 2行目は1行目で作成したtmpを使って、sample1の質的変数をダミー変数に変換して、sample1.dummyに格納します。
#ランダムフォレストで使用するデータ - Titanics.rpart - Titanic - Titanichはtraingが統計処理されたデータでありこの演習には不向き - cordataは、グラフィック用に処理されたデータでありtrainのPclasswを3区分したり、ｓｅｘを2区分するなど一部質的化したが、Fareh・年齢は量的データのままであり、氏名はそのままであり、欠落のあるデータは補完してある。 - ダミー変数ummy_varn等はカテゴリーデータをintegerデータに置き換えたものであり以下の論点に合わないらしいので使わない - lldataを使っても良いが、（makedummies()を使用してダミー変数）を実施する前のdumとnot_dum結合した、 - train2を使用する
dum <- select(.data = alldata,Survived, Pclass,Sex, Embarked, DFamsize, Title, Group, Wom_chd,PassengerId)
#CabinやNamen等分析に使用しないデータの除外は、
#exclude_cols = c("Cabin", "Name")として、dum = alldata[ !names(alldata) %in% exclude_cols ]としても良いようだ
not_dum <- select(.data = alldata, Age, SibSp, Parch, Fare, Famsize)
train2 <- cbind(dum, not_dum)[1:891,]#train2作成,892以降はSurvivedデータがないので削除
test <- cbind(dum, not_dum)[892:1309,]
#sapply(train2, class)
#■トレーニングデータ（Survivedgが分かっているものを訓練用とモデル検証テストデータにランダムに分ける）
#割合は適当に訓練用70%、テスト用30%としておこう。
#乱数の再現：set.seed()
#set.seed(100)
#runif(5)
#runif(5)
#df.rows = nrow(iris) # 150
#train.rate = 0.7 # 訓練データの比率
#train.index <- sample(df.rows, df.rows * train.rate)
#df.train = iris[train.index,] # 訓練データ
#df.test = iris[-train.index,] # テストデータ
#cat("train=", nrow(df.train), " test=", nrow(df.test))
sapply(train2, class)## Survived Pclass Sex Embarked DFamsize Title
## "factor" "factor" "factor" "factor" "factor" "factor"
## Group Wom_chd PassengerId Age SibSp Parch
## "factor" "factor" "integer" "numeric" "integer" "integer"
## Fare Famsize
## "numeric" "numeric"head(train2)## Survived Pclass Sex Embarked DFamsize Title Group Wom_chd PassengerId Age
## 1 0 3 male S Small Mr No No 1 22
## 2 1 1 female C Small Mrs No Yes 2 38
## 3 1 3 female S Single Miss No Yes 3 26
## 4 1 1 female S Small Mrs No Yes 4 35
## 5 0 3 male S Single Mr No No 5 35
## 6 0 3 male Q Single Mr No No 6 29
## SibSp Parch Fare Famsize
## 1 1 0 7.2500 2
## 2 1 0 71.2833 2
## 3 0 0 7.9250 1
## 4 1 0 53.1000 2
## 5 0 0 8.0500 1
## 6 0 0 8.4583 1head(test)## Survived Pclass Sex Embarked DFamsize Title Group Wom_chd PassengerId
## 892 <NA> 3 male Q Single Mr No No 892
## 893 <NA> 3 female S Small Mrs No Yes 893
## 894 <NA> 2 male Q Single Mr No No 894
## 895 <NA> 3 male S Single Mr No No 895
## 896 <NA> 3 female S Small Mrs No Yes 896
## 897 <NA> 3 male S Single Mr No Yes 897
## Age SibSp Parch Fare Famsize
## 892 34.5 0 0 7.8292 1
## 893 47.0 1 0 7.0000 2
## 894 62.0 0 0 9.6875 1
## 895 27.0 0 0 8.6625 1
## 896 22.0 1 1 12.2875 3
## 897 14.0 0 0 9.2250 1#ランダムフォーレストにかける。引数はhttps://www1.doshisha.ac.jp/~mjin/R/Chap_32/32.html参照
model = randomForest(Survived ~ ., data = train2)
#分析がうまくいったら、作成した予測モデル（model）の精度をまず確認してみます。
#randomForest()は与えられたデータフレームから学習データを自動的にサンプリング（ランダムに選択）して学習をおこない
#ます。このとき、最終的に学習に使われなかったデータが残存するため、このデータを使って答え合わせをすることができます。その答え合わせの結果で簡易的に予測モデルの精度を確認することができるわけです。
model##
## Call:
## randomForest(formula = Survived ~ ., data = train2)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 3
##
## OOB estimate of error rate: 16.5%
## Confusion matrix:
## 0 1 class.error
## 0 493 56 0.1020036
## 1 91 251 0.2660819model$importance## MeanDecreaseGini
## Pclass 28.566202
## Sex 37.939797
## Embarked 8.646572
## DFamsize 13.395351
## Title 56.628547
## Group 4.941077
## Wom_chd 35.584599
## PassengerId 41.471730
## Age 35.823323
## SibSp 9.548199
## Parch 5.772066
## Fare 50.672617
## Famsize 15.648935varImpPlot(model)
予測結果
prediction = predict(model,test)
solution <- data.frame(PassengerID = test$PassengerId, Survived = prediction)
#solution
write.csv(solution, file = 'test_Solution.csv', row.names = F)train2データを予測値と実測値に分けた精度の検証を行う
#prediction2は文字列行列であるため、interger型変換しなしと、pred_b <- ifelse(pred > 0.5, 1, 0)の演算ができない。
prediction2 = predict(model,train2)
(result <- table(prediction2, train2$Survived))# ()で括って内容表示##
## prediction2 0 1
## 0 538 43
## 1 11 299(accuracy_prediction = sum(diag(result)) / sum(result))## [1] 0.9393939提出用に予測値と実測値一つの表にまとめ,精度の検証結果をPassengerId共にcsv出力
#提出用にPassengerIdと予想したpredictionの列を持つdata.frameを作成する
solution <- data.frame(PassengerID = train2$PassengerId, Survived =train2$Survived, predictSurvived = prediction2)
head(solution)## PassengerID Survived predictSurvived
## 1 1 0 0
## 2 2 1 1
## 3 3 1 1
## 4 4 1 1
## 5 5 0 0
## 6 6 0 0write.csv(solution, file = 'train2Solution.csv', row.names = F)
#http://smrmkt.hatenablog.jp/entry/2012/12/20/232113
#https://momonoki2017.blogspot.com/2018/04/r007-riris.html
#http://yuranhiko.hatenablog.com/entry/DataAnalysis_R_caret_LinearRegression(脱線1：Rで3D）
#library(rgl)
#open3d() # 3Dグラフィックのウィンドウのみを生成
# とりあえず単純な球体に乱子の画像を貼り付ける
#spheres3d (c(0,0,0), texture= "c\temp\ani.png", radius=1, color="white",alpha=0.8)
# この状態で，画像の適当な場所でマウスの左クリックを押したまま回転できます
# ホイールを使うと拡大もできます
# 自動的に回転させてみる(この場合ImageMagickをインストールしている必要はありません)
#if (!rgl.useNULL()) play3d(spin3d(axis=c(0,0,1), rpm=8), duration=20 )
# C:\temp というフォルダを用意して，anime.gifを作成（ImageMagickが必要です）
# Mac の場合は
# Sys.setenv(PATH=paste("/opt/local/bin", Sys.getenv("PATH"), sep=":")
# としてImageMagickのconvertプログラムの場所を指定する必要があります
#movie3d(spin3d(), ,fps = 16,duration=5,movie = "c\temp\ani")
# 作成されたanime.gifはブラウザで開きます．Internet ExplorerやFirefoxのアイコンにドラッグ＆ドロップします．(脱線2：cordataの使い方）
# (脱線：cordataの使い方）
#よく教科書に載っている「相関係数がρのときの散布図」をRで作る。
#http://ryotamugiyama.com/wp-content/uploads/2016/01/corr_scatter.html
#cordata関数は、散布図のの作成に優れている
#cormat <- alldata dum <- cordata %>% select(Survived, Pclass,Sex, Embarked, Dfamsize, Title, group, Wom_chd)
#select(Survived, Pclass, Sex, Embarked, DFamsize, Title, group, Wom_chd)
r2norm <- function(n, mu, sigma, rho) {
tmp <- rnorm(n)
x <- mu+sigma*tmp
y <- rho*x + sqrt(1-rho^2)*rnorm(n)
return(data.frame(x=x,y=y))
}time <- seq(-1, 1, length=21)
size <- 5000
cordata <- data.frame()
for (i in time){
cor <- i
cordata_mini <- r2norm(size, 0, 1, i)
cordata_mini$cor <- cor
cordata <- rbind(cordata, cordata_mini)
}corsample <- data.frame()
for (i in time){
corsub <- cor(cordata$x[cordata$cor == i], cordata$y[cordata$cor == i])
cortrue <- i
corsub <- cbind(cortrue, corsub)
corsample <- rbind(corsample, corsub)
}
## 95%信頼区間も確認しておく
corsample$lowerCI <- corsample$corsub - 1.96*sqrt((1 - corsample$corsub^2)/(size - 2))
corsample$upperCI <- corsample$corsub + 1.96*sqrt((1 - corsample$corsub^2)/(size - 2))
colnames(corsample) <- c("True", "Sample", "lower 95% CI", "upper 95% CI")
kable(corsample)| True | Sample | lower 95% CI | upper 95% CI |
|---|---|---|---|
| -1.0 | -1.0000000 | -1.0000000 | -1.0000000 |
| -0.9 | -0.9019475 | -0.9139201 | -0.8899750 |
| -0.8 | -0.7949512 | -0.8117707 | -0.7781317 |
| -0.7 | -0.7002343 | -0.7200269 | -0.6804417 |
| -0.6 | -0.5767970 | -0.5994445 | -0.5541496 |
| -0.5 | -0.5120474 | -0.5358613 | -0.4882336 |
| -0.4 | -0.3777884 | -0.4034580 | -0.3521189 |
| -0.3 | -0.3124347 | -0.3387709 | -0.2860985 |
| -0.2 | -0.2099618 | -0.2370679 | -0.1828556 |
| -0.1 | -0.0969085 | -0.1245021 | -0.0693149 |
| 0.0 | -0.0295103 | -0.0572223 | -0.0017982 |
| 0.1 | 0.1006581 | 0.0730748 | 0.1282414 |
| 0.2 | 0.1932507 | 0.1660491 | 0.2204522 |
| 0.3 | 0.3082779 | 0.2819040 | 0.3346517 |
| 0.4 | 0.4113374 | 0.3860673 | 0.4366075 |
| 0.5 | 0.5066561 | 0.4827538 | 0.5305584 |
| 0.6 | 0.5898091 | 0.5674207 | 0.6121975 |
| 0.7 | 0.6990309 | 0.6792056 | 0.7188561 |
| 0.8 | 0.7981846 | 0.7814833 | 0.8148860 |
| 0.9 | 0.8999498 | 0.8878623 | 0.9120373 |
| 1.0 | 1.0000000 | 1.0000000 | 1.0000000 |
cordata1 <- subset(cordata, cor > -1)
p1 <- ggplot(cordata1, aes(x = x, y = y)) +
geom_point() +
facet_wrap(~cor) +
xlim(-4,4) +
ylim(-4,4)
p1## Warning: Removed 8 rows containing missing values (geom_point).
#いくつかピックアップして抜き出すと次のようになる。
cordata$cor <- round(cordata$cor, digits = 1)
cordata2 <- subset(cordata, abs(cor) == 0.9 | abs(cor) == 0.7 | abs(cor) == 0.5 | abs(cor) == 0.3 | abs(cor) == 0)
p2 <- ggplot(cordata2, aes(x = x, y = y)) +
geom_point() +
facet_wrap(~cor) +
xlim(-4,4) +
ylim(-4,4)
p2## Warning: Removed 4 rows containing missing values (geom_point).