Willibrordus Bayu 12/15/2021
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library(openxlsx)
df <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet="3varb")
#standarisasi variabel (centering dan scaling)
df <- scale(df, center = TRUE, scale = TRUE)
head(df, 3)## x1 x2 x3
## 1 -0.906102243 -0.7416508 -0.3747010
## 2 0.007307276 0.1550555 -0.4843659
## 3 -1.125320527 -1.1526412 0.6893456library(openxlsx)
df <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet="3varb")
df <- scale(df, center = TRUE, scale = TRUE)
cormat <- cor(df)
cormat## x1 x2 x3
## x1 1.0000000 0.9869242 0.2994195
## x2 0.9869242 1.0000000 0.2584419
## x3 0.2994195 0.2584419 1.0000000library(openxlsx)
df <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet="3varb")
df <- scale(df, center = TRUE, scale = TRUE)
cormat <- cor(df)
eig <- eigen(cormat)
eig## eigen() decomposition
## $values
## [1] 2.12525679 0.86259104 0.01215218
##
## $vectors
## [,1] [,2] [,3]
## [1,] 0.6704698 -0.2112281 0.71123334
## [2,] 0.6640498 -0.2567210 -0.70223374
## [3,] 0.3309200 0.9431209 -0.03185768library(openxlsx)
df <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet="3varb")
df <- scale(df, center = TRUE, scale = TRUE)
cormat <- cor(df)
eig <- eigen(cormat)
round(eig$values/ncol(df),3)## [1] 0.708 0.288 0.004round(cumsum(eig$values/ncol(df)),3)## [1] 0.708 0.996 1.000pr.out <- prcomp(df, scale. = TRUE, center = TRUE)
pr.out## Standard deviations (1, .., p=3):
## [1] 1.4578260 0.9287578 0.1102369
##
## Rotation (n x k) = (3 x 3):
## PC1 PC2 PC3
## x1 0.6704698 -0.2112281 -0.71123334
## x2 0.6640498 -0.2567210 0.70223374
## x3 0.3309200 0.9431209 0.03185768summary(pr.out)## Importance of components:
## PC1 PC2 PC3
## Standard deviation 1.4578 0.9288 0.11024
## Proportion of Variance 0.7084 0.2875 0.00405
## Cumulative Proportion 0.7084 0.9960 1.00000library(factoextra)## Loading required package: ggplot2
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBafviz_eig(pr.out, addlabels = TRUE)
screeplot(pr.out, type = "line")
abline(h = 1, lty = 3, col = "red")
library(openxlsx)
df <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet="3varb")
df <- scale(df, center = TRUE, scale = TRUE)
pr.out <- prcomp(df, scale. = TRUE, center = TRUE)
pr.out$rotation## PC1 PC2 PC3
## x1 0.6704698 -0.2112281 -0.71123334
## x2 0.6640498 -0.2567210 0.70223374
## x3 0.3309200 0.9431209 0.03185768biplot(pr.out, scale = 0)
library(openxlsx)
df <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet="3varb")
df <- scale(df, center = TRUE, scale = TRUE)
pr.out <- prcomp(df, scale. = TRUE, center = TRUE)
head(df)## x1 x2 x3
## 1 -0.906102243 -0.7416508 -0.3747010
## 2 0.007307276 0.1550555 -0.4843659
## 3 -1.125320527 -1.1526412 0.6893456
## 4 1.541835268 1.2012128 0.5159564
## 5 0.153452799 0.2671437 -1.0220701
## 6 -1.125320527 -1.1526412 -1.0450404df_new <- df %*% pr.out$rotation
df_new[1:6,1:2]## PC1 PC2
## 1 -1.22400335 0.02840327
## 2 -0.05242254 -0.49816513
## 3 -1.29178635 1.18374272
## 4 2.00215943 -0.14744625
## 5 -0.05794123 -1.06493057
## 6 -1.86572942 -0.45199295#Panggil library openxlsx untuk membaca file data Excel
library(openxlsx)
#Baca data pada sheet "3varb" dalam file https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx
#dan simpan data dengan nama df_raw
df_raw <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet = "3varb")
#Tampilkan struktur data
str(df_raw)## 'data.frame': 20 obs. of 3 variables:
## $ x1: num 8.1 10.6 7.5 14.8 11 7.5 11.5 12.2 11.7 9.1 ...
## $ x2: num 10.6 13 9.5 15.8 13.3 9.5 13.4 13.5 13.5 11.3 ...
## $ x3: num 217.7 173.3 648.5 578.3 -44.4 ...#Tampilkan beberapa baris observasi dengan fungsi head()
head(df_raw)## x1 x2 x3
## 1 8.1 10.6 217.7
## 2 10.6 13.0 173.3
## 3 7.5 9.5 648.5
## 4 14.8 15.8 578.3
## 5 11.0 13.3 -44.4
## 6 7.5 9.5 -53.7#Lakukan analisa PCA dengan fungsi prcomp()
#simpan output dengan nama pr.out
pr.out <- prcomp(df_raw, center = TRUE, scale = TRUE, retx = TRUE)
#Tampilkan komponen output fungsi prcomp()
names(pr.out)## [1] "sdev" "rotation" "center" "scale" "x"#Tampilkan output PCA
pr.out## Standard deviations (1, .., p=3):
## [1] 1.4578260 0.9287578 0.1102369
##
## Rotation (n x k) = (3 x 3):
## PC1 PC2 PC3
## x1 0.6704698 -0.2112281 -0.71123334
## x2 0.6640498 -0.2567210 0.70223374
## x3 0.3309200 0.9431209 0.03185768#Tampilkan summary dari output PCA
summary(pr.out)## Importance of components:
## PC1 PC2 PC3
## Standard deviation 1.4578 0.9288 0.11024
## Proportion of Variance 0.7084 0.2875 0.00405
## Cumulative Proportion 0.7084 0.9960 1.00000#Gambarkan scree plot
#Tambahkan garis horizontal sebagai panduan untuk menggunakan kriteria Kaiser
screeplot(pr.out, type = "line")
abline(h = 1, col = "red", lty = 3)
#Gambarkan biplot dengan menggunakan fungsi biplot()
biplot(pr.out, scale = 0) ## Tugas Praktik: 4 Variabel
#Panggil library openxlsx untuk membaca file data Excel
#[1]
library(openxlsx)
#Baca data pada sheet "csdata" dalam file "https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx"
#dan simpan data dengan nama "csdat_raw"
#[2]
csdat_raw <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet = "csdata")
#Tampilkan struktur data
#[3]
str(csdat_raw)## 'data.frame': 900 obs. of 6 variables:
## $ contractcode: chr "AGR-000001" "AGR-000011" "AGR-000030" "AGR-000043" ...
## $ income : num 295 271 159 210 165 220 70 88 163 100 ...
## $ tenor : num 48 36 12 12 36 24 36 48 48 36 ...
## $ dependents : num 5 5 0 3 0 5 3 3 5 6 ...
## $ midoverdue : num 75.5 75.5 0 53 38 15 38 38 38 38 ...
## $ riskrating : num 4 4 1 3 2 1 2 2 2 2 ...#Tampilkan beberapa baris observasi dengan fungsi head()
#[4]
head(csdat_raw)## contractcode income tenor dependents midoverdue riskrating
## 1 AGR-000001 295 48 5 75.5 4
## 2 AGR-000011 271 36 5 75.5 4
## 3 AGR-000030 159 12 0 0.0 1
## 4 AGR-000043 210 12 3 53.0 3
## 5 AGR-000049 165 36 0 38.0 2
## 6 AGR-000063 220 24 5 15.0 1#Tampilkan statistika deskriptif untuk semua variabel dalam data.
#[5]
summary(csdat_raw)## contractcode income tenor dependents
## Length:900 Min. : 70.0 Min. :12.00 Min. :0.000
## Class :character 1st Qu.:121.0 1st Qu.:12.00 1st Qu.:1.000
## Mode :character Median :162.0 Median :24.00 Median :3.000
## Mean :163.3 Mean :29.93 Mean :2.932
## 3rd Qu.:199.0 3rd Qu.:48.00 3rd Qu.:5.000
## Max. :300.0 Max. :48.00 Max. :6.000
## midoverdue riskrating
## Min. : 0.0 Min. :1.000
## 1st Qu.:15.0 1st Qu.:1.000
## Median :53.0 Median :3.000
## Mean :48.1 Mean :2.681
## 3rd Qu.:53.0 3rd Qu.:3.000
## Max. :91.0 Max. :5.000#Gambarkan distribusi Income berdasarkan Dependents
library(ggplot2)
ggplot(csdat_raw, aes(as.factor(dependents), income)) +
geom_boxplot() + xlab("Dependents") + ggtitle("Boxplot Income Berdasarkan Dependents")
#Pisahkan data untuk traning set dan testing set
#untuk tiap-tiap risk rating
#[5]
#Catat indeks/ nomor baris untuk tiap-tiap risk rating
index1 <- which(csdat_raw$riskrating == 1)
index2 <- which(csdat_raw$riskrating == 2)
#Lakukan pencatatan indeks untuk risk rating berikutnya
#[6]
index3 <- which(csdat_raw$riskrating == 3)
index4 <- which(csdat_raw$riskrating == 4)
index5 <- which(csdat_raw$riskrating == 5)
#80% data akan digunakan sebagai traning set.
#Ulangi langkah sampai dengan index5
#[7]
ntrain1 <- round(0.8 * length(index1))
ntrain2 <- round(0.8 * length(index2))
ntrain3 <- round(0.8 * length(index3))
ntrain4 <- round(0.8 * length(index4))
ntrain5 <- round(0.8 * length(index5))
#set seed agar sampling ini bisa direproduksi
set.seed(100)
#sampling data masing-masing rating untuk training set
#Ulangi langkah sampai dengan train5_index
#[8]
train1_index <- sample(index1, ntrain1)
train2_index <- sample(index2, ntrain2)
train3_index <- sample(index3, ntrain3)
train4_index <- sample(index4, ntrain4)
train5_index <- sample(index5, ntrain5)
#menyimpan data ke dalam testing set
#Ulangi langkah sampai dengan test5_index
#[9]
test1_index <- setdiff(index1, train1_index)
test2_index <- setdiff(index2, train2_index)
test3_index <- setdiff(index3, train3_index)
test4_index <- setdiff(index4, train4_index)
test5_index <- setdiff(index5, train5_index)
#Menggabungkan hasil sampling masing-masing risk rating ke dalam training set
csdattrain <- do.call("rbind", list(csdat_raw[train1_index,],
csdat_raw[train2_index,], csdat_raw[train3_index,],
csdat_raw[train4_index,], csdat_raw[train5_index,]))
cstrain <- subset(csdattrain, select =
-c(contractcode,riskrating))
#Menggabungkan hasil sampling masing-masing risk rating ke dalam testing set
csdattest <- do.call("rbind", list(csdat_raw[test1_index,],
csdat_raw[test2_index,], csdat_raw[test3_index,],
csdat_raw[test4_index,], csdat_raw[test5_index,])) #[10]
cstest <- subset(csdattest,
select = -c(contractcode,riskrating)) #[11]
#Menghitung korelasi antar variabel
cor(cstrain)## income tenor dependents midoverdue
## income 1.00000000 -0.07256604 0.2427909 0.1250535
## tenor -0.07256604 1.00000000 0.0334339 0.2333681
## dependents 0.24279088 0.03343390 1.0000000 0.7632659
## midoverdue 0.12505348 0.23336810 0.7632659 1.0000000#Lakukan analisa PCA dengan fungsi prcomp() dan
#simpan output ke dalam obyek dengan nama pr.out
#[12]
pr.out <- prcomp(cstrain, scale = TRUE, center = TRUE)
#Tampilkan output PCA dengan memanggil obyek pr.out
#[13]
pr.out## Standard deviations (1, .., p=4):
## [1] 1.3685609 1.0492944 0.9059637 0.4530475
##
## Rotation (n x k) = (4 x 4):
## PC1 PC2 PC3 PC4
## income -0.2665678 0.6235436 -0.7299486 0.08549929
## tenor -0.1827165 -0.7592266 -0.6015443 -0.16832731
## dependents -0.6675878 0.1100498 0.2569754 -0.69005738
## midoverdue -0.6707330 -0.1505238 0.1981998 0.69869635#Tampilkan summary dari output PCA
#[14]
summary(pr.out)## Importance of components:
## PC1 PC2 PC3 PC4
## Standard deviation 1.3686 1.0493 0.9060 0.45305
## Proportion of Variance 0.4682 0.2752 0.2052 0.05131
## Cumulative Proportion 0.4682 0.7435 0.9487 1.00000#Gambarkan scree plot dengan menggunakan fungsi screeplot()
#[15]
screeplot(pr.out, type = "line", ylim = c(0,2))
#Tambahkan garis horizontal sebagai panduan untuk menggunakan kriteria Kaiser
abline(h = 1, lty = 3, col = "red")
#Gambarkan biplot dengan menggunakan fungsi biplot()
#[16]
biplot(pr.out, scale = 0) #draw first 2 principal components
#Panggil library openxlsx untuk membaca file data Excel
library(openxlsx)
#Baca data pada sheet "cslarge" dalam file "https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx"
#dan simpan data dengan nama "cslarge_raw"
cslarge_raw <- read.xlsx("https://storage.googleapis.com/dqlab-dataset/dqlab_pcadata.xlsx", sheet = "cslarge")
#Tampilkan struktur data
str(cslarge_raw)## 'data.frame': 900 obs. of 10 variables:
## $ contractcode: chr "AGR-000001" "AGR-000011" "AGR-000030" "AGR-000043" ...
## $ income : num 295 271 159 210 165 220 70 88 163 100 ...
## $ tenor : num 48 36 12 12 36 24 36 48 48 36 ...
## $ dependents : num 5 5 0 3 0 5 3 3 5 6 ...
## $ midoverdue : num 76 76 0 53 38 15 38 38 38 38 ...
## $ riskrating : num 4 4 1 3 2 1 2 2 2 2 ...
## $ age : num 55 53 35 45 36 45 21 24 35 26 ...
## $ empyear : num 12 10 5 7 5 8 1 1 6 2 ...
## $ asset : num 893 906 552 791 593 ...
## $ debt : num 4.6984 4.0639 0.05 0.7214 0.0667 ...#Tampilkan beberapa baris observasi dengan fungsi head()
head(cslarge_raw)## contractcode income tenor dependents midoverdue riskrating age empyear
## 1 AGR-000001 295 48 5 76 4 55 12
## 2 AGR-000011 271 36 5 76 4 53 10
## 3 AGR-000030 159 12 0 0 1 35 5
## 4 AGR-000043 210 12 3 53 3 45 7
## 5 AGR-000049 165 36 0 38 2 36 5
## 6 AGR-000063 220 24 5 15 1 45 8
## asset debt
## 1 892.9266 4.69837074
## 2 905.8225 4.06385168
## 3 551.7261 0.05000000
## 4 791.1124 0.72138396
## 5 592.6501 0.06666667
## 6 778.0493 2.59791099#Tampilkan statistika deskriptif untuk semua variabel dalam data frame.
summary(cslarge_raw)## contractcode income tenor dependents
## Length:900 Min. : 70.0 Min. :12.00 Min. :0.000
## Class :character 1st Qu.:121.0 1st Qu.:12.00 1st Qu.:1.000
## Mode :character Median :162.0 Median :24.00 Median :3.000
## Mean :163.3 Mean :29.93 Mean :2.932
## 3rd Qu.:199.0 3rd Qu.:48.00 3rd Qu.:5.000
## Max. :300.0 Max. :48.00 Max. :6.000
## midoverdue riskrating age empyear
## Min. : 0.00 Min. :1.000 Min. :20.00 Min. : 0.000
## 1st Qu.:15.00 1st Qu.:1.000 1st Qu.:29.00 1st Qu.: 3.000
## Median :53.00 Median :3.000 Median :36.00 Median : 5.000
## Mean :48.16 Mean :2.681 Mean :35.88 Mean : 5.153
## 3rd Qu.:53.00 3rd Qu.:3.000 3rd Qu.:42.00 3rd Qu.: 7.000
## Max. :91.00 Max. :5.000 Max. :61.00 Max. :13.000
## asset debt
## Min. : 232.2 Min. : 0.0500
## 1st Qu.: 440.0 1st Qu.: 0.6469
## Median : 555.0 Median : 2.0253
## Mean : 571.0 Mean : 3.8091
## 3rd Qu.: 687.8 3rd Qu.: 5.1902
## Max. :1192.3 Max. :23.5382#Gambarkan distribusi income berdasarkan dependents.
library(ggplot2)
ggplot(cslarge_raw, aes(as.factor(dependents), income)) +
geom_boxplot() + xlab("Dependents") + ggtitle("Boxplot Income Berdasarkan Dependents")
#Gambarkan distribusi debt berdasarkan dependents.
ggplot(cslarge_raw, aes(as.factor(dependents), debt)) +
geom_boxplot() + xlab("Dependents") + ggtitle("Boxplot Debt Berdasarkan Dependents")
#Pisahkan data untuk traning set dan testing set
#untuk tiap-tiap risk rating
#Catat indeks/ nomor baris untuk tiap-tiap risk rating
index1 <- which(cslarge_raw$riskrating == 1)
index2 <- which(cslarge_raw$riskrating == 2)
#Lakukan pencatatan indeks untuk risk rating berikutnya
index3 <- which(cslarge_raw$riskrating == 3)
index4 <- which(cslarge_raw$riskrating == 4)
index5 <- which(cslarge_raw$riskrating == 5)
#80% data akan digunakan sebagai traning set.
ntrain1 <- round(0.8 * length(index1))
ntrain2 <- round(0.8 * length(index2))
ntrain3 <- round(0.8 * length(index3))
ntrain4 <- round(0.8 * length(index4))
ntrain5 <- round(0.8 * length(index5))
#set seed agar sampling ini bisa direproduksi
set.seed(100)
#sampling data masing-masing rating untuk training set
train1_index <- sample(index1, ntrain1)
train2_index <- sample(index2, ntrain2)
train3_index <- sample(index3, ntrain3)
train4_index <- sample(index4, ntrain4)
train5_index <- sample(index5, ntrain5)
#menyimpan data ke dalam testing set
test1_index <- setdiff(index1, train1_index)
test2_index <- setdiff(index2, train2_index)
test3_index <- setdiff(index3, train3_index)
test4_index <- setdiff(index4, train4_index)
test5_index <- setdiff(index5, train5_index)
#Menggabungkan hasil sampling masing-masing risk rating ke dalam training set
cslarge_train <- do.call("rbind", list(cslarge_raw[train1_index,],
cslarge_raw[train2_index,], cslarge_raw[train3_index,],
cslarge_raw[train4_index,], cslarge_raw[train5_index,]))
cstrain <- subset(cslarge_train, select = -c(contractcode,riskrating))
#Menggabungkan hasil sampling masing-masing risk rating ke dalam testing set
cslarge_test <- do.call("rbind", list(cslarge_raw[test1_index,],
cslarge_raw[test2_index,], cslarge_raw[test3_index,],
cslarge_raw[test4_index,], cslarge_raw[test5_index,]))
cstest <- subset(cslarge_test, select = -c(contractcode,riskrating))
#Menghitung korelasi antar variabel
cor(cstrain)## income tenor dependents midoverdue age
## income 1.00000000 -0.07256604 0.2427909 0.12756439 0.98345799
## tenor -0.07256604 1.00000000 0.0334339 0.23170789 -0.07433539
## dependents 0.24279088 0.03343390 1.0000000 0.76422579 0.23110488
## midoverdue 0.12756439 0.23170789 0.7642258 1.00000000 0.12134786
## age 0.98345799 -0.07433539 0.2311049 0.12134786 1.00000000
## empyear 0.97526554 -0.07152033 0.2499218 0.13267309 0.92632487
## asset 0.86275940 -0.07265391 0.1901048 0.09832691 0.93780421
## debt 0.16255937 0.08842241 0.6952961 0.70521333 0.15994524
## empyear asset debt
## income 0.97526554 0.86275940 0.16255937
## tenor -0.07152033 -0.07265391 0.08842241
## dependents 0.24992178 0.19010482 0.69529606
## midoverdue 0.13267309 0.09832691 0.70521333
## age 0.92632487 0.93780421 0.15994524
## empyear 1.00000000 0.74882989 0.16008510
## asset 0.74882989 1.00000000 0.13959529
## debt 0.16008510 0.13959529 1.00000000#Menggambarkan matrik korelasi dengan ggcorrplot
library(ggcorrplot)## Warning: package 'ggcorrplot' was built under R version 4.1.2ggcorrplot(cor(cstrain))
#Lakukan analisa PCA dengan fungsi prcomp() dan
#simpan output ke dalam obyek dengan nama pr.out
pr.out <- prcomp(cstrain, scale = TRUE, center = TRUE)
#Tampilkan output PCA dengan memanggil obyek pr.out
pr.out## Standard deviations (1, .., p=8):
## [1] 1.9886275 1.5061223 0.9869479 0.5697568 0.5168331 0.4513118 0.0807862
## [8] 0.0306914
##
## Rotation (n x k) = (8 x 8):
## PC1 PC2 PC3 PC4 PC5
## income -0.48037618 0.1732370 -0.04956696 0.05692772 -0.21996290
## tenor 0.02300648 -0.1921475 -0.96534791 -0.02622698 -0.04016120
## dependents -0.23802064 -0.5058409 0.19514188 0.46401430 0.04509053
## midoverdue -0.18278120 -0.5638239 -0.03401568 0.29673063 0.22083181
## age -0.48194139 0.1807062 -0.05221971 -0.04070810 0.10019847
## empyear -0.46111279 0.1571628 -0.04049775 0.17088762 -0.57056947
## asset -0.44138937 0.1785155 -0.05058642 -0.22043915 0.71311191
## debt -0.19730574 -0.5196857 0.13958206 -0.78310912 -0.23470343
## PC6 PC7 PC8
## income 0.053169773 0.560979083 6.063182e-01
## tenor -0.168350384 -0.003290186 -9.006944e-05
## dependents -0.657287184 0.004360366 -1.701761e-03
## midoverdue 0.714643123 0.002246697 1.006382e-03
## age 0.002608534 0.386800275 -7.556671e-01
## empyear 0.111545448 -0.623731435 -6.927760e-02
## asset -0.102418913 -0.382877667 2.377850e-01
## debt -0.056134329 -0.004113309 6.814070e-04#Tampilkan summary dari output PCA
summary(pr.out)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.9886 1.5061 0.9869 0.56976 0.51683 0.45131 0.08079
## Proportion of Variance 0.4943 0.2836 0.1218 0.04058 0.03339 0.02546 0.00082
## Cumulative Proportion 0.4943 0.7779 0.8996 0.94022 0.97361 0.99907 0.99988
## PC8
## Standard deviation 0.03069
## Proportion of Variance 0.00012
## Cumulative Proportion 1.00000#Gambarkan scree plot dengan menggunakan fungsi screeplot()
screeplot(pr.out, type = "line", ylim = c(0,2))
#Tambahkan garis horizontal sebagai panduan untuk menggunakan kriteria Kaiser
abline(h = 1, lty = 3, col = "red")
#Gambarkan biplot dengan menggunakan fungsi biplot()
biplot(pr.out, scale = 0) #draw first 2 principal components
#Gambarkan Principal Component dan risk rating dengan menggunakan
#fungsi autoplot() dari package ggfortify.
library(ggfortify)
autoplot(pr.out, data = cslarge_train, colour = 'riskrating',
loadings = TRUE, loadings.label = TRUE, loadings.label.size = 3, scale = 0)
#Gambarkan Principal Component dan risk rating dengan menggunakan
#fungsi fviz_pca_ind() package factoextra.
library(factoextra)
fviz_pca_ind(pr.out, label="none", habillage=cslarge_train$riskrating)
(A <- as.matrix(data.frame(c(1,0,1),c(0,1,1),c(1,1,0))))## c.1..0..1. c.0..1..1. c.1..1..0.
## [1,] 1 0 1
## [2,] 0 1 1
## [3,] 1 1 0e <- eigen(A)
str(e)## List of 2
## $ values : num [1:3] 2 1 -1
## $ vectors: num [1:3, 1:3] -0.577 -0.577 -0.577 0.707 -0.707 ...
## - attr(*, "class")= chr "eigen"e## eigen() decomposition
## $values
## [1] 2 1 -1
##
## $vectors
## [,1] [,2] [,3]
## [1,] -0.5773503 7.071068e-01 0.4082483
## [2,] -0.5773503 -7.071068e-01 0.4082483
## [3,] -0.5773503 4.440892e-16 -0.8164966# Ketik perintah berikut ini untuk membaca help tentang matriks
?matrix## starting httpd help server ... done# Buatlah matriks 3 x 3 dan simpan dengan nama matriks A.
A <- matrix(c(1, 1, 0, 0, -2, 1, 0, 0, 3), nrow = 3, ncol = 3, byrow = TRUE)
# Tuliskan perintah untuk menampilkan matriks A
A## [,1] [,2] [,3]
## [1,] 1 1 0
## [2,] 0 -2 1
## [3,] 0 0 3# Tuliskan perintah R untuk menghitung nilai eigen dan vektor eigen
# dan simpanlah hasilnya dalam variable ev
ev <- eigen(A)
# Tuliskan perintah untuk melihat struktur obyek eigen
str(ev)## List of 2
## $ values : num [1:3] 3 -2 1
## $ vectors: num [1:3, 1:3] 0.0976 0.1952 0.9759 -0.3162 0.9487 ...
## - attr(*, "class")= chr "eigen"# Tuliskan perintah untuk melihat hasil output
ev## eigen() decomposition
## $values
## [1] 3 -2 1
##
## $vectors
## [,1] [,2] [,3]
## [1,] 0.09759001 -0.3162278 1
## [2,] 0.19518001 0.9486833 0
## [3,] 0.97590007 0.0000000 0# Tuliskan perintah untuk mengakses nilai eigen
ev$values## [1] 3 -2 1# Tuliskan perintah untuk mengakses vektor eigen
ev$vectors## [,1] [,2] [,3]
## [1,] 0.09759001 -0.3162278 1
## [2,] 0.19518001 0.9486833 0
## [3,] 0.97590007 0.0000000 0