1. Build PCA-PI-PLS Function
###Import Data
semad_import_from_excel = function(file_path="data.xlsx") {
library(readxl)
Variabel=read_excel(file_path, sheet = "Variabel")
Variabel=data.frame(Variabel=Variabel$Variabel)
Indikator=read_excel(file_path, sheet = "Indikator")
Indikator=data.frame(Indikator=Indikator$Indikator)
Data=read_excel(file_path, sheet = "Data")
MIM=read_excel(file_path, sheet = "MIM")
MOM=read_excel(file_path, sheet = "MOM")
return(list(Variabel = Variabel, Indikator = Indikator, Data = Data, MIM = MIM, MOM = MOM))
}
###OLS
ols = function(X, y) {
beta = solve(t(X) %*% X) %*% t(X) %*% y
return(beta)
}
###Power Iteration for find Eigen Vector and Eigen Value
eigen_poweriteration <- function(A, maxiter = 1000, tol = 1e-10) {
n <- nrow(A)
cat("Covariance:\n ")
print(A)
# Inisialisasi vektor awal secara acak
b <- runif(n)
b <- b / sqrt(sum(b^2))
for (i in 1:maxiter) {
# Hitung produk matriks-vektor
cat("iterasi ke-:",i,"\n")
cat("eigen vector:\n ")
print(b)
b_new <- A %*% b
# Normalisasi vektor hasil
b_new <- b_new / sqrt(sum(b_new^2))
cat("eigen vector baru:\n ")
print(b_new)
# Periksa konvergensi
if (sqrt(sum((b_new - b)^2)) < tol) {
break
}
b <- b_new
}
# Hitung eigenvalue sebagai Rayleigh quotient
eigenvalue <- t(b) %*% A %*% b / (t(b) %*% b)
return(list(eigenvalues = eigenvalue, eigenvectors = b))
}
###PCA
pca_semad <- function(data) {
# Langkah 1: Normalisasi Data
data_normalized <- scale(data)
# Langkah 2: Menghitung Matriks Kovariansi
covariance_matrix <- cov(data_normalized)
# Langkah 3: Menghitung Eigenvalues dan Eigenvectors
cat("covariance: ")
print(covariance_matrix)
eigen_values_vectors <- eigen_poweriteration(covariance_matrix)
eigen_values <- eigen_values_vectors$eigenvalues
eigen_vectors <- eigen_values_vectors$eigenvectors
# Langkah 4: Memilih Principal Components
# Pada pendekatan Power Iteration yang terbentuk hanya Principal Component 1 yang paling dominan (sehingga tidak bisa memilih)
# Langkah 5: Transformasi Data ke Ruang Principal Components
cat("cek transformasi:\n")
print(head(data_normalized))
print(eigen_vectors)
transformed_data <- data_normalized %*% eigen_vectors
print(head(transformed_data))
return(list(scores = transformed_data,
rotation = eigen_vectors,
sdev = sqrt(eigen_values)))
}
###PLS Algorithm Modification using PCA-PI (PCA-PI-PLS Algorithm)
plsalgorithm <- function(data, matrix_outer_model, matrix_inner_model, indicator, laten_variables){
#Set Output yang masih kosongan
outputplsalgoritm=list()
indexoutput=0
#Menghitung banyaknya indikator
n_indicator=nrow(matrix_outer_model)
#Menghitung banyaknya variabel laten
n_laten_variables=ncol(matrix_outer_model)
#Menghitung banyaknya observasi
n=nrow(data)
#Standarisasi data dalam bentuk matriks
standardize_data=scale(data)
#Pemetaan_indikator_ke_variabel_laten
indicator_to_laten_variable_map=list()
for (i in 1:n_laten_variables) {
indicator_to_laten_variable_connection=c()
for (j in 1:n_indicator) {
if (matrix_outer_model[j,i]==1 | matrix_outer_model[j,i]==2 | matrix_outer_model[j,i]==3) {
indicator_to_laten_variable_connection=append(indicator_to_laten_variable_connection,j)
}
}
indicator_to_laten_variable_map=append(indicator_to_laten_variable_map,list(indicator_to_laten_variable_connection))
}
#Pemetaan_tipe_outer_model
type_outer_model_map=list()
for (i in 1:n_laten_variables) {
type_outer_model=c()
for (j in 1:n_indicator) {
if (matrix_outer_model[j,i]==1 | matrix_outer_model[j,i]==2 | matrix_outer_model[j,i]==3) {
type_outer_model=append(type_outer_model,matrix_outer_model[j,i])
}
}
type_outer_model_map=append(type_outer_model_map,list(type_outer_model))
}
#Standarisasi data dalam bentuk list
list_indicator=list()
for (i in 1:n_laten_variables) {
part_standardize_data=standardize_data[,indicator_to_laten_variable_map[[i]]]
list_indicator=append(list_indicator,list(part_standardize_data))
}
#Inisialisasi Weight
Weight=list()
for (i in 1:n_laten_variables) {
# 1=single construct, 2=reflective, 3=formative
if (type_outer_model_map[[i]][1]==1) {
vector_one=rep(1,length(indicator_to_laten_variable_map[[i]]))
matrix_one=matrix(vector_one,nrow = length(indicator_to_laten_variable_map[[i]]),ncol = 1)
Weight=append(Weight,list(matrix_one))
} else if (type_outer_model_map[[i]][1]==3 || type_outer_model_map[[i]][1]==2) {
pca_result=pca_semad(list_indicator[[i]])
vector_one=pca_result$rotation[,1]
matrix_one=matrix(vector_one,nrow = length(indicator_to_laten_variable_map[[i]]),ncol = 1)
Weight=append(Weight,list(matrix_one))
}
}
#Menghitung nilai f
f=list()
for (i in 1:n_laten_variables) {
f_i=sqrt(1/(t(Weight[[i]])%*%var(list_indicator[[i]])%*%Weight[[i]]))
f=append(f,list(f_i))
}
#Menghitung Outer Weight
outer_weight=list()
for (i in 1:n_laten_variables) {
outer_weight_i=Weight[[i]]%*%f[[i]]
outer_weight=append(outer_weight,list(outer_weight_i))
}
#Outsite Approximation Awal
Y=list()
for (i in 1:n_laten_variables) {
Y_i=list_indicator[[i]]%*%outer_weight[[i]]
Y=append(Y,list(Y_i))
}
#Cetak Outer Weight Final dan Skor Laten Final
indexoutput=indexoutput+1
outputplsalgoritm[[indexoutput]]=outer_weight
new_text=paste("Outer Weight Final")
names(outputplsalgoritm)[indexoutput]=new_text
cat("Outer Weight","\n")
for(i in seq_along(outer_weight)) {
cat("Variabel Laten ",laten_variables[i,1],"\n")
print(outer_weight[[i]]) # Menampilkan elemen ke-i
cat("\n") # Baris kosong untuk pemisah antar elemen
}
indexoutput=indexoutput+1
outputplsalgoritm[[indexoutput]]=Y
new_text=paste("Y Final")
names(outputplsalgoritm)[indexoutput]=new_text
cat("Skor Variabel Laten","\n")
for(i in seq_along(Y)) {
cat("Variabel Laten ",laten_variables[i,1],"\n")
print(head(Y[[i]])) # Menampilkan elemen ke-i
cat("\n") # Baris kosong untuk pemisah antar elemen
}
#tahap 2
##Membuat daftar kolom eksogen
list_exogenous_columns=list()
for (i in 1:n_laten_variables) {
exogenous_columns=c()
for (j in 1:n_laten_variables) {
if (matrix_inner_model[j,i]==1) {
exogenous_columns=append(exogenous_columns,j)
}
}
list_exogenous_columns=append(list_exogenous_columns,list(exogenous_columns))
}
##Eksogen
exogenous=list()
for (i in 1:n_laten_variables) {
k=length(list_exogenous_columns[[i]])
if (k==0) {
exogenous_i = NULL
} else if (k==1) {
fill=list_exogenous_columns[[i]][1]
exogenous_i = Y[[fill]]
} else if (k>1) {
for (j in 1:k) {
fill=list_exogenous_columns[[i]][j]
# print(fill)
if (j==1) {
exogenous_i = Y[[fill]]
} else {
exogenous_i = cbind(exogenous_i,Y[[fill]])
}
}
}
exogenous=append(exogenous,list(exogenous_i))
}
##Koefisien Jalur
path_coefficient=list()
for (i in 1:n_laten_variables) {
if (is.null(list_exogenous_columns[[i]])) {
path_coefficient_i=NULL
} else {
path_coefficient_i=ols(exogenous[[i]],Y[[i]])
}
path_coefficient=append(path_coefficient,list(path_coefficient_i))
}
indexoutput=indexoutput+1
outputplsalgoritm[[indexoutput]]=path_coefficient
names(outputplsalgoritm)[indexoutput]="Path Coefficient"
cat("Path Coefficient:\n ")
for(i in seq_along(path_coefficient)) {
cat("Variabel Laten ",laten_variables[i,1],"\n")
print(path_coefficient[[i]]) # Menampilkan elemen ke-i
cat("\n") # Baris kosong untuk pemisah antar elemen
}
##Outer Loading
outer_loading=list()
for (i in 1:n_laten_variables) {
if (type_outer_model_map[[i]][1]==2 | type_outer_model_map[[i]][1]==3) {
outer_loading_i=matrix(0,nrow = length(type_outer_model_map[[i]]), ncol = 1)
for (j in 1:length(type_outer_model_map[[i]])) {
outer_loading_i[j,1]=ols(Y[[i]],list_indicator[[i]][,j])
}
outer_loading=append(outer_loading,list(outer_loading_i))
} else if (type_outer_model_map[[i]][1]==1) {
outer_loading_i=1
outer_loading=append(outer_loading,list(outer_loading_i))
}
}
indexoutput=indexoutput+1
outputplsalgoritm[[indexoutput]]=outer_loading
names(outputplsalgoritm)[indexoutput]="Outer Loading"
cat("Outer Loading:\n ")
for(i in seq_along(outer_loading)) {
cat("Variabel Laten ",laten_variables[i,1],"\n")
print(outer_loading[[i]]) # Menampilkan elemen ke-i
cat("\n") # Baris kosong untuk pemisah antar elemen
}
vector_outer_loading=unlist(outer_loading)
##R Square
R_Square=list()
for (i in 1:n_laten_variables) {
if (!is.null(exogenous[[i]]) && !is.null(path_coefficient[[i]])) {
y_actual = Y[[i]]
y_predicted = exogenous[[i]] %*% path_coefficient[[i]]
ss_total = sum((y_actual - mean(y_actual))^2)
ss_residual = sum((y_actual - y_predicted)^2)
r_square = 1 - (ss_residual / ss_total)
R_Square[[i]] = r_square
} else {
R_Square[[i]] = NA
}
}
indexoutput=indexoutput+1
outputplsalgoritm[[indexoutput]]=R_Square
names(outputplsalgoritm)[indexoutput]="R Square"
cat("R Square:\n ")
for(i in seq_along(R_Square)) {
cat("Variabel Laten ",laten_variables[i,1],"\n")
print(R_Square[[i]]) # Menampilkan elemen ke-i
cat("\n") # Baris kosong untuk pemisah antar elemen
}
#tahap 3
##Menghitung m
m=list()
for (i in 1:n_laten_variables) {
pertama=TRUE
for (j in 1:length(indicator_to_laten_variable_map[[i]])) {
if (pertama==TRUE) {
if (type_outer_model_map[[i]][j]==2 | type_outer_model_map[[i]][j]==3) {
m_i=matrix(sum(list_indicator[[i]][,j]))
pertama=FALSE
} else {
m_i=matrix(sum(list_indicator[[i]]))
pertama=FALSE
}
} else if (pertama==FALSE) {
if (type_outer_model_map[[i]][j]==2 | type_outer_model_map[[i]][j]==3) {
m_i=rbind(m_i,matrix(sum(list_indicator[[i]][,j])))
} else {
m_i=rbind(m_i,matrix(sum(list_indicator[[i]])))
}
}
}
m=append(m,list(m_i))
}
##Menghitung nx
nx=list()
for (i in 1:n_laten_variables) {
nx_i=t(outer_weight[[i]])%*%m[[i]]
nx=append(nx,list(nx_i))
}
##Menghitung p0
p0=list()
for (i in 1:n_laten_variables) {
p0_i=m[[i]]-outer_loading[[i]]%*%nx[[i]]
p0=append(p0,list(p0_i))
}
b0=list()
for (i in 1:n_laten_variables) {
if (is.null(list_exogenous_columns[[i]])) {
b0_i=NULL
} else {
n_exogenous=length(list_exogenous_columns[[i]])
nxexo=c()
for (j in 1:n_exogenous) {
nxexo=append(nxexo,nx[[j]])
}
b0_i=nx[[i]]-t(nxexo)%*%path_coefficient[[i]]
}
b0=append(b0,list(b0_i))
}
#Penamaan
names(outputplsalgoritm$"Outer Weight Final")=laten_variables[,1]
names(outputplsalgoritm$"Y Final")=laten_variables[,1]
names(outputplsalgoritm$"Path Coefficient")=laten_variables[,1]
names(outputplsalgoritm$"Outer Loading")=laten_variables[,1]
names(outputplsalgoritm$"R Square")=laten_variables[,1]
#output
return(outputplsalgoritm)
}
2. Generate Data Simulation 1
laten_variables <- data.frame(Variabel = c("X_Eksogen", "Y_Endogen"))
indicator <- data.frame(Indikator = c("x1", "x2", "x3", "y1", "y2", "y3"))
matrix_inner_model <- matrix(0, nrow = 2, ncol = 2)
matrix_inner_model[1, 2] <- 1
matrix_outer_model <- matrix(0, nrow = 6, ncol = 2)
matrix_outer_model[1:3, 1] <- 2
matrix_outer_model[4:6, 2] <- 2
###Generate data function
generate_group_data <- function(n, path_coeff) {
Lx <- rnorm(n, mean = 0, sd = 1.5)
Ly <- (path_coeff * Lx) + rnorm(n, mean = 0, sd = 0.1)
x1 <- 0.90 * Lx + rnorm(n, 0, 0.1); x2 <- 0.90 * Lx + rnorm(n, 0, 0.1); x3 <- 0.90 * Lx + rnorm(n, 0, 0.1)
y1 <- 0.90 * Ly + rnorm(n, 0, 0.1); y2 <- 0.90 * Ly + rnorm(n, 0, 0.1); y3 <- 0.90 * Ly + rnorm(n, 0, 0.1)
return(data.frame(x1, x2, x3, y1, y2, y3))
}
###Generate data
set.seed(771)
data_g1 <- generate_group_data(n = 100, path_coeff = 1)
data_g1$True_Label <- 1
data_g2 <- generate_group_data(n = 100, path_coeff = -1)
data_g2$True_Label <- 2
data_simulasi_full <- rbind(data_g1, data_g2)
print(data_simulasi_full)
## x1 x2 x3 y1 y2 y3
## 1 1.444724011 1.46917273 1.470834715 1.371269242 1.42979955 1.369551241
## 2 0.970484802 0.89226584 0.806292280 0.837141758 0.95938807 0.848705960
## 3 1.483350309 1.44907105 1.315204320 1.208448196 1.38819655 1.333301667
## 4 -0.899766011 -0.72605046 -0.783792872 -0.760552831 -0.57555780 -0.699692703
## 5 -2.061009340 -1.88065448 -2.154854164 -2.319287913 -2.13922717 -2.302704225
## 6 -1.257306440 -1.30508333 -1.327900691 -1.241560718 -1.29127596 -1.271167658
## 7 -0.047782924 0.10063766 -0.045406828 0.158141107 0.15031599 0.118872018
## 8 0.407568309 0.29658974 0.540704317 0.429887802 0.62986276 0.383915935
## 9 0.062280275 0.16336602 0.383161863 0.132201441 0.12164507 -0.023647050
## 10 0.192602344 0.36536375 0.466710977 0.203184624 0.29173865 0.433759003
## 11 -2.174890629 -2.14757548 -2.188150832 -2.420588465 -2.52098127 -2.380343760
## 12 0.768603862 0.89181442 0.860363132 0.849531555 0.90375147 0.981111798
## 13 -0.970934646 -0.98244803 -0.946530336 -1.029073678 -0.83758513 -1.197288383
## 14 -1.909850354 -1.69664524 -1.703574828 -1.759407442 -1.68462864 -1.913311814
## 15 0.986789032 1.00239368 0.964556125 0.895664565 0.90721897 0.976752170
## 16 -0.189574938 -0.38408419 -0.366069934 -0.255566907 -0.14798968 -0.052021792
## 17 -1.611718181 -1.52873861 -1.474883448 -1.566437556 -1.69243763 -1.659129576
## 18 -0.765347845 -1.07545995 -0.937698642 -0.787404088 -0.74729167 -0.772288455
## 19 0.616372605 0.74509290 0.651307330 0.834392772 0.66063765 0.574958927
## 20 -0.714751922 -0.91854944 -0.745759612 -0.882297039 -0.71772423 -0.671850245
## 21 -1.656092817 -1.77704432 -1.779351572 -1.620137117 -1.81792290 -1.681649575
## 22 -0.480570853 -0.47127330 -0.545187519 -0.312860112 -0.14884850 -0.259532796
## 23 -0.870811908 -0.81841272 -0.860404463 -0.502813922 -0.42359636 -0.689985298
## 24 -1.263875896 -1.37379824 -1.431374854 -1.246200536 -1.46481611 -1.282062051
## 25 -2.284784501 -2.28300356 -2.335862745 -2.400223171 -2.50025555 -2.496647457
## 26 0.385027617 0.20032793 0.331389582 0.381254969 0.59735768 0.555216230
## 27 1.302711570 1.51587496 1.381979003 1.326546610 1.48908088 1.339735940
## 28 1.035981843 1.29490027 1.340402247 1.440433507 1.35271749 1.348544423
## 29 0.987981664 1.20422393 1.103220785 0.913264064 0.74449402 1.002389557
## 30 -0.494814625 -0.42403493 -0.343459023 -0.600060344 -0.46106431 -0.399907048
## 31 0.976049619 1.19822060 1.215037110 1.151681085 1.30208496 1.160673532
## 32 -1.415698687 -1.68953933 -1.658569926 -1.415230437 -1.34482061 -1.463442967
## 33 -2.289432021 -2.40184246 -2.309515523 -2.563736586 -2.58585673 -2.612981900
## 34 -0.388307827 -0.41805258 -0.332525266 -0.567078036 -0.30849761 -0.361689518
## 35 -0.543100532 -0.55706529 -0.404341590 -0.562427494 -0.65863210 -0.762472337
## 36 0.702161836 0.68988518 0.751774496 0.845589496 0.68364759 0.646762779
## 37 0.709577128 0.52240524 0.620976371 0.736229502 0.76522432 0.598206182
## 38 -0.693318958 -0.48919942 -0.662562871 -0.545433799 -0.69051519 -0.444300947
## 39 -0.536322149 -0.74800122 -0.759758266 -0.792562501 -0.70685229 -0.604841894
## 40 -0.300190323 -0.20980022 -0.388787169 -0.285685990 -0.17763717 -0.148890051
## 41 2.083693555 1.98405781 1.869399816 2.066391303 2.03961820 1.921825358
## 42 -1.179788315 -1.20101036 -1.245288275 -1.545031854 -1.41778032 -1.306272322
## 43 0.612029410 0.47718184 0.622357934 0.389821812 0.43270170 0.418466997
## 44 -0.201218016 -0.09924536 -0.171514988 -0.175780640 -0.28209158 -0.160601165
## 45 -0.256855369 -0.22072369 -0.358372049 -0.368606321 -0.36071858 -0.218947268
## 46 -2.564432175 -2.44693831 -2.507205097 -2.376954797 -2.28122994 -2.319072460
## 47 -0.837340213 -0.84998391 -0.690944159 -0.804258610 -0.96361170 -0.933147332
## 48 2.607720271 2.45342097 2.493943194 2.707897127 2.58815516 2.507831991
## 49 2.151956030 2.06374632 2.140052929 1.982205905 1.87634530 2.064986522
## 50 0.118834627 0.07831786 0.124450728 0.222710808 0.18996648 0.285994287
## 51 0.182167602 0.12344793 0.065026416 -0.038252772 -0.08504968 -0.014091299
## 52 -0.106849129 0.10051251 -0.133735872 0.074496352 -0.21335306 -0.042010535
## 53 -0.819925498 -0.81247101 -1.006522477 -1.050945861 -0.96484451 -0.898139674
## 54 0.487367986 0.59431016 0.301064757 0.213444850 0.50500830 0.371561611
## 55 1.248723248 0.95242164 1.180829788 0.849099273 1.16568584 0.938029736
## 56 2.465585616 2.12712113 2.500589713 2.169159224 2.15977789 2.211770108
## 57 0.823697699 0.85102702 0.838924024 1.063322954 0.84237589 0.724405142
## 58 -3.536560046 -3.45466550 -3.480156839 -3.299684554 -3.66254383 -3.604383245
## 59 -0.174694307 -0.32887879 -0.185979844 -0.270779565 -0.26840726 -0.396963917
## 60 0.906739710 0.82090661 0.554207190 0.888542568 0.77622023 0.871551112
## 61 2.312294921 2.23362241 2.190388462 2.071950083 2.25000408 1.906664615
## 62 0.402289965 0.58621382 0.449013638 0.314930353 0.21321420 0.090469228
## 63 2.030142946 1.97559351 2.089647920 2.080512754 2.02563675 2.135308267
## 64 -0.005108298 -0.00961999 0.080265709 0.163291815 0.05526585 -0.079181033
## 65 -1.515267813 -1.48688153 -1.425108674 -1.716405201 -1.80805024 -1.952973849
## 66 -0.067153081 -0.17435213 -0.342053537 -0.245596741 -0.49803524 -0.499787035
## 67 2.198725306 2.34991233 2.061285927 2.167693233 2.18333484 2.161843647
## 68 -0.731368108 -0.72309819 -0.504664466 -0.678883182 -0.50321836 -0.724518014
## 69 0.208885028 0.34791394 0.326715646 0.192058475 0.26036464 0.080829586
## 70 0.081523515 -0.09213394 0.075243873 0.091604964 -0.19175706 0.020293249
## 71 -2.716123876 -2.33975360 -2.311673707 -2.545525255 -2.50248184 -2.500687825
## 72 2.271834322 2.24347551 2.296629042 2.281571365 2.20925643 2.400347404
## 73 1.897259576 1.98112602 1.657070027 1.970710885 1.91560768 2.085326539
## 74 -1.213955246 -1.27562906 -1.242204862 -1.200377261 -1.22215479 -1.245779784
## 75 0.883792004 0.73052474 0.882754577 1.016103928 0.94454360 1.138580964
## 76 -2.977973701 -2.82546887 -2.803028800 -2.774955106 -2.84937281 -2.792376032
## 77 2.512919242 2.67399390 2.692705461 2.624685994 2.71370290 2.577710854
## 78 0.771094275 0.85816710 0.569026728 0.743222451 0.82790957 0.830717646
## 79 0.388205111 0.60729722 0.473879967 0.583617192 0.44640801 0.641909388
## 80 2.805571608 2.63399149 2.973998972 2.760200482 2.73360529 2.705319268
## 81 1.311480919 1.00146404 1.236498795 1.422938947 1.12028584 1.436686685
## 82 1.695017354 1.86934353 1.784280720 1.873841604 1.59725470 1.603402964
## 83 0.290132012 0.31295644 0.262355255 0.040646664 0.15989593 0.232864757
## 84 0.770294191 0.79906229 1.005787453 0.712691244 0.63181659 0.824986881
## 85 -0.382512629 -0.38525866 -0.281985245 -0.280208209 -0.35547163 -0.340847987
## 86 -1.030556989 -1.13329947 -1.182321223 -1.246767106 -1.17814995 -1.418730044
## 87 2.814154084 2.81271901 2.623400916 2.625560457 2.46604441 2.667919050
## 88 2.387203286 2.34915047 2.217441785 2.630292811 2.38260058 2.307114039
## 89 -2.070698256 -2.11120972 -1.966219581 -2.145298826 -2.15867986 -2.135114343
## 90 0.131751522 0.16760180 0.136283742 -0.071544550 0.10815670 0.208041540
## 91 -0.160143100 -0.11769673 -0.418791962 -0.104217084 -0.31581082 -0.347651649
## 92 0.399532112 0.43868965 0.564930545 0.538897435 0.63716840 0.434409005
## 93 -0.745727441 -0.88662789 -0.853063969 -0.812713284 -0.99367756 -0.861926497
## 94 0.030015166 0.15025031 0.223389555 0.130715802 0.26336066 0.023458562
## 95 0.368825441 0.23721342 0.173470222 0.269441824 0.23388616 0.354966734
## 96 1.077236458 1.20379816 1.172078523 0.786709102 0.90482056 0.935197246
## 97 0.362933471 0.39673878 0.217193954 0.488251768 0.47559615 0.437656590
## 98 -0.586811727 -0.58610570 -0.777934043 -0.800671869 -0.66578766 -0.862923386
## 99 -1.021655776 -0.81497373 -0.868011515 -0.731441940 -0.99040015 -0.895381031
## 100 1.713255949 1.88464856 1.991367021 1.740865108 1.75899592 1.892105845
## 101 -1.988762006 -1.88129361 -1.768625515 1.782339193 1.69490002 1.757778705
## 102 1.178127617 0.98469084 1.220469802 -0.836911388 -0.96650919 -1.036566636
## 103 -0.912228555 -1.11375400 -1.087310504 0.937499625 0.78297771 0.914631304
## 104 0.506038772 0.83783206 0.742921229 -0.908138087 -1.01125956 -0.849075713
## 105 -2.240983969 -2.30989696 -2.375909608 2.562361154 2.38752362 2.463824417
## 106 -1.078773768 -1.17412725 -1.045298691 1.248106626 1.16033302 1.142501239
## 107 0.165605614 0.22725493 0.328177274 -0.010702322 -0.08675085 -0.110353222
## 108 0.228074402 0.10358939 0.076613631 -0.455386916 -0.41057823 -0.478767089
## 109 -0.842324428 -0.77403119 -0.957257419 0.801073874 0.91247272 0.844748999
## 110 0.044357893 -0.13103272 -0.128930691 0.234953273 0.21605464 0.228579091
## 111 0.131570445 0.03368790 0.072594312 -0.086709419 0.09772059 -0.044791522
## 112 -1.861514183 -1.78416034 -1.891529073 1.907983145 2.12865142 2.249295818
## 113 -1.311334681 -1.18182468 -1.245007298 1.335351483 1.23781973 1.394483794
## 114 0.082012277 0.03549843 -0.023424960 -0.115364635 -0.32033042 -0.115066867
## 115 1.589691913 1.55037232 1.493193069 -1.824112322 -1.81161713 -1.703873700
## 116 -1.169085451 -1.12638990 -1.062833839 1.220634970 1.32982508 1.316440467
## 117 -2.097668859 -1.86400387 -1.901211602 1.975549432 2.07556051 1.886579889
## 118 0.004314268 0.03661516 0.011315662 0.013590166 -0.18224598 -0.002666644
## 119 1.953814464 1.73319984 1.878235581 -1.645952331 -1.71581517 -1.428406676
## 120 0.036714277 0.12582164 -0.009972532 0.009510717 0.13356179 0.252004840
## 121 -1.119113045 -1.11183082 -1.105701457 1.129638554 1.14529420 1.149847007
## 122 -2.083861572 -2.13127972 -2.062273884 2.202362687 2.27071809 2.259025373
## 123 -1.536854636 -1.67916208 -1.428000992 1.277197781 1.39008200 1.463304963
## 124 -1.588415386 -1.60068151 -1.405068417 1.429061779 1.70215660 1.524961242
## 125 1.199578210 1.03647020 1.142439165 -1.053412310 -0.79944706 -0.660642181
## 126 -0.621783436 -0.55433546 -0.694586149 0.491606187 0.59135011 0.691915726
## 127 1.196634512 1.27636214 1.210685042 -1.265098881 -1.19150931 -1.345761587
## 128 1.454883938 1.65612993 1.490592703 -1.381095887 -1.48404402 -1.440830937
## 129 -1.064517993 -1.00662518 -0.931931031 1.028850905 1.01852114 0.945415324
## 130 -1.453727375 -1.51691735 -1.364849267 1.457121753 1.45351612 1.313112822
## 131 1.743934647 1.99190139 1.901963119 -2.023069129 -1.87878081 -1.799608390
## 132 0.922656261 0.78026679 0.938527125 -0.978763951 -0.62843221 -0.789191050
## 133 0.363903756 0.34572789 0.381310675 -0.464731065 -0.38138091 -0.520671068
## 134 1.494496611 1.64262219 1.299799855 -1.488574332 -1.53933245 -1.566444011
## 135 2.266162883 2.02144989 2.186360415 -2.137214583 -2.44723455 -2.462412507
## 136 -0.667570392 -0.61817586 -0.638360590 0.652355874 0.67294936 0.699788457
## 137 -2.323186255 -2.55794267 -2.238414467 2.286384361 2.44046498 2.423790627
## 138 2.543032435 2.34216410 2.206589611 -2.492390737 -2.52345381 -2.460571094
## 139 1.397627360 1.34380199 1.360648174 -1.487120728 -1.53779692 -1.456327083
## 140 -1.565010184 -1.47568003 -1.778651787 1.877592488 1.72357319 1.432690535
## 141 -0.224877453 -0.10052165 -0.181376293 0.204591967 0.13417740 0.208710544
## 142 2.278113198 2.32868441 2.217473365 -2.604861241 -2.52145826 -2.471229857
## 143 -0.959011271 -0.99654936 -0.958173263 0.988453206 0.92746297 0.670757355
## 144 0.454427307 0.54118574 0.434004580 -0.385206325 -0.56211016 -0.323772452
## 145 1.859445873 1.78914556 1.997266226 -2.055918051 -1.82666258 -1.788830990
## 146 0.613999007 0.66662702 0.686034216 -0.719861148 -0.55308022 -0.634860174
## 147 -0.573786056 -0.45151617 -0.407478007 0.499998317 0.59136717 0.459500817
## 148 -0.742620623 -0.69403668 -0.735809747 0.638586477 0.73331721 0.651106114
## 149 0.530921534 0.63530572 0.721870050 -0.726523097 -1.04244708 -0.952737716
## 150 -1.578956115 -1.24971303 -1.242616363 1.650183781 1.51107098 1.689670741
## 151 0.204780329 0.33910651 0.202492876 -0.367484238 -0.59904234 -0.499555696
## 152 -0.686863293 -0.63324616 -0.972037656 0.823875476 0.77163875 0.766924521
## 153 2.521103469 2.35988854 2.408828487 -2.660670955 -2.67823016 -2.649397869
## 154 1.846303621 1.93232432 1.924244562 -1.981776071 -1.69789288 -1.992670180
## 155 0.544483671 0.74744001 0.570991608 -0.493793397 -0.53514155 -0.442223277
## 156 0.369783993 0.42188560 0.374037261 -0.503607217 -0.50923183 -0.559646627
## 157 -0.539870284 -0.53457842 -0.331836661 0.505800048 0.38568030 0.657792759
## 158 0.921424826 1.03044499 0.873375469 -1.376250696 -1.34609257 -1.303495758
## 159 -1.863367282 -1.95074014 -1.940664880 1.674771263 1.62464069 1.844236438
## 160 -0.120347918 -0.05804940 -0.019371029 -0.096704762 0.01184689 0.190951800
## 161 0.717784177 0.78354088 0.785879942 -0.803323084 -0.82530706 -0.789953249
## 162 -1.046259382 -0.91611477 -0.939281270 0.964477424 0.96672396 0.904535994
## 163 -1.326047575 -1.13584169 -1.303109296 1.337313547 1.21220354 1.253399438
## 164 0.171446862 0.06897271 0.066363965 -0.130241316 -0.15157090 -0.270416427
## 165 1.008082372 0.82432537 0.935808190 -0.993304995 -0.88244935 -0.934016054
## 166 1.536321091 1.35695768 1.437085352 -1.228347333 -1.35661650 -1.284061720
## 167 0.555924965 0.54541900 0.518765731 -0.505621407 -0.22204491 -0.334777464
## 168 -0.660917104 -0.73175201 -0.487401462 0.429846643 0.69069440 0.565064244
## 169 -0.290226174 -0.20139326 -0.132789591 0.292934347 0.42760324 0.162901080
## 170 0.094068962 0.08051384 0.016918124 -0.360442807 -0.25511356 -0.155268170
## 171 4.712425861 4.42927652 4.455862051 -4.514246022 -4.42542872 -4.398491283
## 172 -0.365361501 -0.28852953 -0.387361473 0.581905134 0.33121564 0.340898145
## 173 -0.179610171 -0.36982381 -0.249368382 0.553322051 0.43872187 0.310806051
## 174 -2.500564638 -2.78181712 -2.551952209 2.356865577 2.49259033 2.376422147
## 175 -0.089838607 0.08493266 0.052788117 -0.155091776 -0.04315520 -0.242952728
## 176 -1.286979632 -1.21944748 -1.280725629 1.155300798 1.46468756 1.217367275
## 177 1.352679232 1.38519901 1.302822075 -1.459957258 -1.34967444 -1.436834780
## 178 0.010408689 0.01366192 0.270040630 -0.088563909 -0.02600116 -0.121313809
## 179 -1.542988349 -1.48246333 -1.455284621 1.493420196 1.46905568 1.335459688
## 180 0.896649195 0.76881177 0.948480011 -0.732168335 -0.84231166 -0.865483867
## 181 3.386102120 3.46533342 3.386780822 -3.691230498 -3.40254215 -3.380582953
## 182 -0.860151939 -0.99645742 -1.089034610 1.072399087 0.98340115 1.051480260
## 183 0.210933264 0.14644313 0.389565614 -0.313551858 -0.33325779 -0.215986373
## 184 1.993480915 2.05133586 2.048503528 -2.196609415 -2.31491178 -2.079435342
## 185 0.183856965 0.37457919 0.107578277 -0.055397873 -0.25865189 -0.027176516
## 186 -1.032837603 -1.09589848 -1.094696626 1.187636069 1.17500212 1.084726265
## 187 0.234443422 0.08933111 0.124284219 -0.058313569 -0.08640887 -0.029126011
## 188 -0.447571247 -0.23138565 -0.360612537 0.487341270 0.51821024 0.548941879
## 189 1.366824524 1.43263629 1.338361106 -1.128858102 -1.27222470 -0.983362545
## 190 0.162734105 0.35248127 0.193768891 -0.032627635 0.01302099 -0.014105478
## 191 -0.473897244 -0.41255647 -0.440529890 0.735003549 0.66747863 0.683027681
## 192 0.079131034 0.13821512 0.220170161 -0.066926522 0.23875159 -0.077184490
## 193 1.177242773 1.00645312 1.036885757 -1.005239211 -0.95456673 -0.840050626
## 194 -2.440885481 -2.47165005 -2.481184413 2.327760280 2.58520372 2.559593925
## 195 1.110783067 1.06828746 1.050060400 -1.180046642 -1.30983749 -1.529072186
## 196 2.481704914 2.29690196 2.302749816 -2.091116224 -1.98887265 -2.195327279
## 197 -0.131296283 0.10045614 -0.025930839 0.079763442 -0.14024559 0.074332402
## 198 -0.807413949 -0.99949148 -0.896827847 0.812644854 0.79103571 0.560901151
## 199 -0.878669122 -0.84791038 -0.793217081 0.784972382 0.90066991 0.722386786
## 200 0.757807959 0.58870180 0.672241880 -0.971360002 -0.62598174 -0.787691299
## True_Label
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
## 13 1
## 14 1
## 15 1
## 16 1
## 17 1
## 18 1
## 19 1
## 20 1
## 21 1
## 22 1
## 23 1
## 24 1
## 25 1
## 26 1
## 27 1
## 28 1
## 29 1
## 30 1
## 31 1
## 32 1
## 33 1
## 34 1
## 35 1
## 36 1
## 37 1
## 38 1
## 39 1
## 40 1
## 41 1
## 42 1
## 43 1
## 44 1
## 45 1
## 46 1
## 47 1
## 48 1
## 49 1
## 50 1
## 51 1
## 52 1
## 53 1
## 54 1
## 55 1
## 56 1
## 57 1
## 58 1
## 59 1
## 60 1
## 61 1
## 62 1
## 63 1
## 64 1
## 65 1
## 66 1
## 67 1
## 68 1
## 69 1
## 70 1
## 71 1
## 72 1
## 73 1
## 74 1
## 75 1
## 76 1
## 77 1
## 78 1
## 79 1
## 80 1
## 81 1
## 82 1
## 83 1
## 84 1
## 85 1
## 86 1
## 87 1
## 88 1
## 89 1
## 90 1
## 91 1
## 92 1
## 93 1
## 94 1
## 95 1
## 96 1
## 97 1
## 98 1
## 99 1
## 100 1
## 101 2
## 102 2
## 103 2
## 104 2
## 105 2
## 106 2
## 107 2
## 108 2
## 109 2
## 110 2
## 111 2
## 112 2
## 113 2
## 114 2
## 115 2
## 116 2
## 117 2
## 118 2
## 119 2
## 120 2
## 121 2
## 122 2
## 123 2
## 124 2
## 125 2
## 126 2
## 127 2
## 128 2
## 129 2
## 130 2
## 131 2
## 132 2
## 133 2
## 134 2
## 135 2
## 136 2
## 137 2
## 138 2
## 139 2
## 140 2
## 141 2
## 142 2
## 143 2
## 144 2
## 145 2
## 146 2
## 147 2
## 148 2
## 149 2
## 150 2
## 151 2
## 152 2
## 153 2
## 154 2
## 155 2
## 156 2
## 157 2
## 158 2
## 159 2
## 160 2
## 161 2
## 162 2
## 163 2
## 164 2
## 165 2
## 166 2
## 167 2
## 168 2
## 169 2
## 170 2
## 171 2
## 172 2
## 173 2
## 174 2
## 175 2
## 176 2
## 177 2
## 178 2
## 179 2
## 180 2
## 181 2
## 182 2
## 183 2
## 184 2
## 185 2
## 186 2
## 187 2
## 188 2
## 189 2
## 190 2
## 191 2
## 192 2
## 193 2
## 194 2
## 195 2
## 196 2
## 197 2
## 198 2
## 199 2
## 200 2
data_input <- data_simulasi_full[, 1:6]
3. Running PCA-PI-PLS using All Data (Global)
pls_global=plsalgorithm(data = data_input,
matrix_outer_model = matrix_outer_model,
matrix_inner_model = matrix_inner_model,
indicator = indicator,
laten_variables = laten_variables)
## covariance: x1 x2 x3
## x1 1.0000000 0.9948351 0.9950455
## x2 0.9948351 1.0000000 0.9949455
## x3 0.9950455 0.9949455 1.0000000
## Covariance:
## x1 x2 x3
## x1 1.0000000 0.9948351 0.9950455
## x2 0.9948351 1.0000000 0.9949455
## x3 0.9950455 0.9949455 1.0000000
## iterasi ke-: 1
## eigen vector:
## [1] 0.4376506 0.4103228 0.8000607
## eigen vector baru:
## [,1]
## x1 0.5771604
## x2 0.5770827
## x3 0.5778075
## iterasi ke-: 2
## eigen vector:
## [,1]
## x1 0.5771604
## x2 0.5770827
## x3 0.5778075
## eigen vector baru:
## [,1]
## x1 0.5773493
## x2 0.5773298
## x3 0.5773717
## iterasi ke-: 3
## eigen vector:
## [,1]
## x1 0.5773493
## x2 0.5773298
## x3 0.5773717
## eigen vector baru:
## [,1]
## x1 0.5773496
## x2 0.5773303
## x3 0.5773710
## iterasi ke-: 4
## eigen vector:
## [,1]
## x1 0.5773496
## x2 0.5773303
## x3 0.5773710
## eigen vector baru:
## [,1]
## x1 0.5773496
## x2 0.5773303
## x3 0.5773710
## iterasi ke-: 5
## eigen vector:
## [,1]
## x1 0.5773496
## x2 0.5773303
## x3 0.5773710
## eigen vector baru:
## [,1]
## x1 0.5773496
## x2 0.5773303
## x3 0.5773710
## cek transformasi:
## x1 x2 x3
## [1,] 1.0011254 1.0263349 1.0332523
## [2,] 0.6560364 0.6021001 0.5427552
## [3,] 1.0292325 1.0115529 0.9183819
## [4,] -0.7048864 -0.5879463 -0.6308827
## [5,] -1.5498867 -1.4369969 -1.6428596
## [6,] -0.9650573 -1.0137444 -1.0324873
## [,1]
## x1 0.5773496
## x2 0.5773303
## x3 0.5773710
## [,1]
## [1,] 1.767103
## [2,] 1.039744
## [3,] 1.708474
## [4,] -1.110658
## [5,] -2.672988
## [6,] -1.738569
## covariance: y1 y2 y3
## y1 1.0000000 0.9942435 0.9943640
## y2 0.9942435 1.0000000 0.9948101
## y3 0.9943640 0.9948101 1.0000000
## Covariance:
## y1 y2 y3
## y1 1.0000000 0.9942435 0.9943640
## y2 0.9942435 1.0000000 0.9948101
## y3 0.9943640 0.9948101 1.0000000
## iterasi ke-: 1
## eigen vector:
## [1] 0.9377378 0.3323570 0.1009286
## eigen vector baru:
## [,1]
## y1 0.5784723
## y2 0.5770183
## y3 0.5765584
## iterasi ke-: 2
## eigen vector:
## [,1]
## y1 0.5784723
## y2 0.5770183
## y3 0.5765584
## eigen vector baru:
## [,1]
## y1 0.5772873
## y2 0.5773705
## y3 0.5773930
## iterasi ke-: 3
## eigen vector:
## [,1]
## y1 0.5772873
## y2 0.5773705
## y3 0.5773930
## eigen vector baru:
## [,1]
## y1 0.5772849
## y2 0.5773713
## y3 0.5773946
## iterasi ke-: 4
## eigen vector:
## [,1]
## y1 0.5772849
## y2 0.5773713
## y3 0.5773946
## eigen vector baru:
## [,1]
## y1 0.5772849
## y2 0.5773713
## y3 0.5773946
## iterasi ke-: 5
## eigen vector:
## [,1]
## y1 0.5772849
## y2 0.5773713
## y3 0.5773946
## eigen vector baru:
## [,1]
## y1 0.5772849
## y2 0.5773713
## y3 0.5773946
## cek transformasi:
## y1 y2 y3
## [1,] 0.9915833 1.0305803 0.9950764
## [2,] 0.6052728 0.6904871 0.6169446
## [3,] 0.8738221 1.0005026 0.9687593
## [4,] -0.5502681 -0.4192321 -0.5071872
## [5,] -1.6776313 -1.5497175 -1.6709678
## [6,] -0.8981595 -0.9366746 -0.9220760
## [,1]
## y1 0.5772849
## y2 0.5773713
## y3 0.5773946
## [,1]
## [1,] 1.7420053
## [2,] 1.1043027
## [3,] 1.6414622
## [4,] -0.8525612
## [5,] -2.8280414
## [6,] -1.5917047
## Outer Weight
## Variabel Laten X_Eksogen
## [,1]
## [1,] 0.3338964
## [2,] 0.3338852
## [3,] 0.3339087
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 0.3339114
## [2,] 0.3339614
## [3,] 0.3339748
##
## Skor Variabel Laten
## Variabel Laten X_Eksogen
## [,1]
## [1,] 1.0219621
## [2,] 0.6013112
## [3,] 0.9880552
## [4,] -0.6423228
## [5,] -1.5458587
## [6,] -1.0054599
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 1.0076054
## [2,] 0.6387475
## [3,] 0.9494496
## [4,] -0.4931359
## [5,] -1.6357872
## [6,] -0.9206690
##
## Path Coefficient:
## Variabel Laten X_Eksogen
## NULL
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 0.01329007
##
## Outer Loading:
## Variabel Laten X_Eksogen
## [,1]
## [1,] 0.9983114
## [2,] 0.9982780
## [3,] 0.9983483
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 0.9980429
## [2,] 0.9981921
## [3,] 0.9982324
##
## R Square:
## Variabel Laten X_Eksogen
## [1] NA
##
## Variabel Laten Y_Endogen
## [1] 0.000176626
4. Plot PCA-PI-PLS Global Result
###View Laten Score for Exogenous and Endogenous Variables
scores_list <- pls_global[["Y Final"]]
score_x <- as.numeric(scores_list[[1]])
score_y <- as.numeric(scores_list[[2]])
###Plot Global
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
data_plot <- data.frame(Score_X = score_x, Score_Y = score_y)
ggplot(data_plot, aes(x = Score_X, y = Score_Y)) +
geom_point(color = "#3498db", alpha = 0.6, size = 2) + # Titik scatter
geom_abline(intercept = 0, slope = pls_global$`Path Coefficient`$Y_Endogen[1,1], color = "#e74c3c", size = 1) + # Garis regresi
labs(
title = "Scatter Plot Global Model: Exogenous vs Endogenous",
x = "Latent Score Xi (Exogenous)",
y = "Latent Score Eta (Endogenous)"
) +
theme_minimal()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
5. Segmentation with Angle-Base Segmentation
theta=atan(score_y / (score_x + 0.000001))
theta_stadarization=scale(theta)
dist_matrix <- dist(theta_stadarization, method = "euclidean")
hac_model <- hclust(dist_matrix, method = "ward.D2")
plot(hac_model,
main = "Dendrogram HAC (Ward's Method)",
xlab = "Observation",
ylab = "Height (Euclidean Distance)",
sub = "",
labels = FALSE,
hang = -1)
rect.hclust(hac_model, k = 2, border = c("red", "blue"))
abline(h = mean(rev(hac_model$height)[1:2]), col = "green", lty = 2)
predicted_labels <- cutree(hac_model, k = 2)
print("Real Group")
## [1] "Real Group"
print(data_simulasi_full$True_Label)
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
## [112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
print("Estimation Group With ABS Segmentation")
## [1] "Estimation Group With ABS Segmentation"
print(predicted_labels)
## [1] 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
## [112] 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2
true_labels <- data_simulasi_full$True_Label
conf_matrix <- table(True = true_labels, Predicted = predicted_labels)
print(conf_matrix)
## Predicted
## True 1 2
## 1 97 3
## 2 5 95
acc_1 <- sum(diag(conf_matrix)) / sum(conf_matrix)
acc_2 <- (conf_matrix[1,2] + conf_matrix[2,1]) / sum(conf_matrix)
final_accuracy <- max(acc_1, acc_2) * 100
cat("SUCCESS RATE / RECOVERY RATE: ", round(final_accuracy, 2), "%\n")
## SUCCESS RATE / RECOVERY RATE: 96 %
###Partition Per Cluster
data_cluster=split(data_input,predicted_labels)
4. Result PCA-PI-PLS-ABS
library(ggplot2)
library(patchwork)
## Warning: package 'patchwork' was built under R version 4.4.3
print("Result of PCA-PI-PLS-ABS")
## [1] "Result of PCA-PI-PLS-ABS"
###PLS Local 1 with PCA-PI-PLS
print("PCA-PI-PLS Local Group A")
## [1] "PCA-PI-PLS Local Group A"
pls_local_1 <- plsalgorithm(data = data_cluster$`1`,
matrix_outer_model = matrix_outer_model,
matrix_inner_model = matrix_inner_model,
indicator = indicator,
laten_variables = laten_variables)
## covariance: x1 x2 x3
## x1 1.0000000 0.9946603 0.9940536
## x2 0.9946603 1.0000000 0.9946014
## x3 0.9940536 0.9946014 1.0000000
## Covariance:
## x1 x2 x3
## x1 1.0000000 0.9946603 0.9940536
## x2 0.9946603 1.0000000 0.9946014
## x3 0.9940536 0.9946014 1.0000000
## iterasi ke-: 1
## eigen vector:
## [1] 0.8862032 0.1762702 0.4284538
## eigen vector baru:
## [,1]
## x1 0.5781647
## x2 0.5767825
## x3 0.5771027
## iterasi ke-: 2
## eigen vector:
## [,1]
## x1 0.5781647
## x2 0.5767825
## x3 0.5771027
## eigen vector baru:
## [,1]
## x1 0.5773203
## x2 0.5774236
## x3 0.5773068
## iterasi ke-: 3
## eigen vector:
## [,1]
## x1 0.5773203
## x2 0.5774236
## x3 0.5773068
## eigen vector baru:
## [,1]
## x1 0.5773187
## x2 0.5774247
## x3 0.5773073
## iterasi ke-: 4
## eigen vector:
## [,1]
## x1 0.5773187
## x2 0.5774247
## x3 0.5773073
## eigen vector baru:
## [,1]
## x1 0.5773187
## x2 0.5774247
## x3 0.5773073
## iterasi ke-: 5
## eigen vector:
## [,1]
## x1 0.5773187
## x2 0.5774247
## x3 0.5773073
## eigen vector baru:
## [,1]
## x1 0.5773187
## x2 0.5774247
## x3 0.5773073
## cek transformasi:
## x1 x2 x3
## 1 0.9960741 1.0170118 1.0207515
## 2 0.6499535 0.5922835 0.5327028
## 3 1.0242653 1.0022126 0.9064545
## 4 -0.7150375 -0.5991475 -0.6350767
## 5 -1.5625637 -1.4491859 -1.6420022
## 6 -0.9759861 -1.0254410 -1.0346767
## [,1]
## x1 0.5773187
## x2 0.5774247
## x3 0.5773073
## [,1]
## 1 1.751587
## 2 1.024763
## 3 1.693333
## 4 -1.125402
## 5 -2.686833
## 6 -1.752897
## covariance: y1 y2 y3
## y1 1.0000000 0.9939511 0.9948310
## y2 0.9939511 1.0000000 0.9943435
## y3 0.9948310 0.9943435 1.0000000
## Covariance:
## y1 y2 y3
## y1 1.0000000 0.9939511 0.9948310
## y2 0.9939511 1.0000000 0.9943435
## y3 0.9948310 0.9943435 1.0000000
## iterasi ke-: 1
## eigen vector:
## [1] 0.5979573 0.7878073 0.1476713
## eigen vector baru:
## [,1]
## y1 0.5774687
## y2 0.5778756
## y3 0.5767059
## iterasi ke-: 2
## eigen vector:
## [,1]
## y1 0.5774687
## y2 0.5778756
## y3 0.5767059
## eigen vector baru:
## [,1]
## y1 0.5773565
## y2 0.5772633
## y3 0.5774310
## iterasi ke-: 3
## eigen vector:
## [,1]
## y1 0.5773565
## y2 0.5772633
## y3 0.5774310
## eigen vector baru:
## [,1]
## y1 0.5773564
## y2 0.5772620
## y3 0.5774323
## iterasi ke-: 4
## eigen vector:
## [,1]
## y1 0.5773564
## y2 0.5772620
## y3 0.5774323
## eigen vector baru:
## [,1]
## y1 0.5773564
## y2 0.5772620
## y3 0.5774323
## iterasi ke-: 5
## eigen vector:
## [,1]
## y1 0.5773564
## y2 0.5772620
## y3 0.5774323
## eigen vector baru:
## [,1]
## y1 0.5773564
## y2 0.5772620
## y3 0.5774323
## cek transformasi:
## y1 y2 y3
## 1 0.9561596 1.0011989 0.9575002
## 2 0.5670999 0.6591033 0.5803995
## 3 0.8375604 0.9709441 0.9312549
## 4 -0.5966643 -0.4571496 -0.5406671
## 5 -1.7320503 -1.5942910 -1.7012743
## 6 -0.9470315 -0.9776386 -0.9544246
## [,1]
## y1 0.5773564
## y2 0.5772620
## y3 0.5774323
## [,1]
## 1 1.6828905
## 2 1.0430355
## 3 1.5817967
## 4 -0.9205817
## 5 -2.9027048
## 6 -1.6622440
## Outer Weight
## Variabel Laten X_Eksogen
## [,1]
## [1,] 0.3339348
## [2,] 0.3339961
## [3,] 0.3339282
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 0.3339636
## [2,] 0.3339090
## [3,] 0.3340075
##
## Skor Variabel Laten
## Variabel Laten X_Eksogen
## [,1]
## 1 1.0131594
## 2 0.5927469
## 3 0.9794636
## 4 -0.6509588
## 5 -1.5541276
## 6 -1.0139167
##
## Variabel Laten Y_Endogen
## [,1]
## 1 0.9734442
## 2 0.6033291
## 3 0.9149679
## 4 -0.5324974
## 5 -1.6790284
## 6 -0.9615014
##
## Path Coefficient:
## Variabel Laten X_Eksogen
## NULL
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 0.9954932
##
## Outer Loading:
## Variabel Laten X_Eksogen
## [,1]
## [1,] 0.9980899
## [2,] 0.9982732
## [3,] 0.9980702
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 0.9981339
## [2,] 0.9979708
## [3,] 0.9982652
##
## R Square:
## Variabel Laten X_Eksogen
## [1] NA
##
## Variabel Laten Y_Endogen
## [1] 0.9910067
scores_list1 <- pls_local_1[["Y Final"]]
score_x1 <- as.numeric(scores_list1[[1]])
score_y1 <- as.numeric(scores_list1[[2]])
slope_1 <- pls_local_1$`Path Coefficient`$Y_Endogen[1,1]
data_plot1 <- data.frame(Score_X = score_x1, Score_Y = score_y1)
p1 <- ggplot(data_plot1, aes(x = Score_X, y = Score_Y)) +
geom_point(color = "#3498db", alpha = 0.6, size = 2) +
geom_abline(intercept = 0, slope = slope_1, color = "#e74c3c", size = 1) +
labs(
title = "Local Model A (Cluster 1)",
subtitle = paste("Path Coefficient =", round(slope_1, 3)),
x = "Latent Score Ksi",
y = "Latent Score Eta"
) +
theme_minimal() +
theme(plot.title = element_text(face = "bold"))
###PLS Local 2 with PCA-PI-PLS
print("PCA-PI-PLS Local Group B")
## [1] "PCA-PI-PLS Local Group B"
pls_local_2 <- plsalgorithm(data = data_cluster$`2`,
matrix_outer_model = matrix_outer_model,
matrix_inner_model = matrix_inner_model,
indicator = indicator,
laten_variables = laten_variables)
## covariance: x1 x2 x3
## x1 1.0000000 0.9950228 0.9961387
## x2 0.9950228 1.0000000 0.9953523
## x3 0.9961387 0.9953523 1.0000000
## Covariance:
## x1 x2 x3
## x1 1.0000000 0.9950228 0.9961387
## x2 0.9950228 1.0000000 0.9953523
## x3 0.9961387 0.9953523 1.0000000
## iterasi ke-: 1
## eigen vector:
## [1] 0.1030067 0.7340191 0.6712716
## eigen vector baru:
## [,1]
## x1 0.5767039
## x2 0.5777071
## x3 0.5776392
## iterasi ke-: 2
## eigen vector:
## [,1]
## x1 0.5767039
## x2 0.5777071
## x3 0.5776392
## eigen vector baru:
## [,1]
## x1 0.5773787
## x2 0.5772285
## x3 0.5774436
## iterasi ke-: 3
## eigen vector:
## [,1]
## x1 0.5773787
## x2 0.5772285
## x3 0.5774436
## eigen vector baru:
## [,1]
## x1 0.5773797
## x2 0.5772277
## x3 0.5774434
## iterasi ke-: 4
## eigen vector:
## [,1]
## x1 0.5773797
## x2 0.5772277
## x3 0.5774434
## eigen vector baru:
## [,1]
## x1 0.5773797
## x2 0.5772277
## x3 0.5774434
## iterasi ke-: 5
## eigen vector:
## [,1]
## x1 0.5773797
## x2 0.5772277
## x3 0.5774434
## eigen vector baru:
## [,1]
## x1 0.5773797
## x2 0.5772277
## x3 0.5774434
## cek transformasi:
## x1 x2 x3
## 7 -0.07595861 0.03071019 -0.078211550
## 51 0.09001629 0.04738018 0.003310397
## 64 -0.04515669 -0.04986728 0.014560060
## 101 -1.47692871 -1.41770642 -1.350293554
## 102 0.80888556 0.67678574 0.856260025
## 103 -0.69990273 -0.85678025 -0.847346034
## [,1]
## x1 0.5773797
## x2 0.5772277
## x3 0.5774434
## [,1]
## 7 -0.07129293
## 51 0.08123430
## 64 -0.04644972
## 101 -2.45080616
## 102 1.35213527
## 103 -1.38796128
## covariance: y1 y2 y3
## y1 1.0000000 0.9945714 0.9940165
## y2 0.9945714 1.0000000 0.9953521
## y3 0.9940165 0.9953521 1.0000000
## Covariance:
## y1 y2 y3
## y1 1.0000000 0.9945714 0.9940165
## y2 0.9945714 1.0000000 0.9953521
## y3 0.9940165 0.9953521 1.0000000
## iterasi ke-: 1
## eigen vector:
## [1] 0.1565895 0.3435132 0.9260013
## eigen vector baru:
## [,1]
## y1 0.5763850
## y2 0.5772997
## y3 0.5783644
## iterasi ke-: 2
## eigen vector:
## [,1]
## y1 0.5763850
## y2 0.5772997
## y3 0.5783644
## eigen vector baru:
## [,1]
## y1 0.5772120
## y2 0.5774721
## y3 0.5773667
## iterasi ke-: 3
## eigen vector:
## [,1]
## y1 0.5772120
## y2 0.5774721
## y3 0.5773667
## eigen vector baru:
## [,1]
## y1 0.5772137
## y2 0.5774722
## y3 0.5773649
## iterasi ke-: 4
## eigen vector:
## [,1]
## y1 0.5772137
## y2 0.5774722
## y3 0.5773649
## eigen vector baru:
## [,1]
## y1 0.5772137
## y2 0.5774722
## y3 0.5773649
## iterasi ke-: 5
## eigen vector:
## [,1]
## y1 0.5772137
## y2 0.5774722
## y3 0.5773649
## eigen vector baru:
## [,1]
## y1 0.5772137
## y2 0.5774722
## y3 0.5773649
## cek transformasi:
## y1 y2 y3
## 7 0.15643774 0.14084529 0.12335052
## 51 0.01587383 -0.02765715 0.02691530
## 64 0.16012423 0.07279721 -0.02029278
## 101 1.31891610 1.24664021 1.31201170
## 102 -0.55574571 -0.65870954 -0.71466245
## 103 0.71424372 0.59377896 0.70049636
## [,1]
## y1 0.5772137
## y2 0.5774722
## y3 0.5773649
## [,1]
## 7 0.24285051
## 51 0.00873131
## 64 0.12274793
## 101 2.23870599
## 102 -1.11379148
## 103 1.15960411
## Outer Weight
## Variabel Laten X_Eksogen
## [,1]
## [1,] 0.3338510
## [2,] 0.3337630
## [3,] 0.3338878
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 0.3338508
## [2,] 0.3340002
## [3,] 0.3339382
##
## Skor Variabel Laten
## Variabel Laten X_Eksogen
## [,1]
## 7 -0.04122281
## 51 0.04697108
## 64 -0.02685803
## 101 -1.41709865
## 102 0.78182808
## 103 -0.80254329
##
## Variabel Laten Y_Endogen
## [,1]
## 7 0.140460672
## 51 0.005050044
## 64 0.070995351
## 101 1.294830100
## 102 -0.644198361
## 103 0.670695620
##
## Path Coefficient:
## Variabel Laten X_Eksogen
## NULL
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] -0.9944641
##
## Outer Loading:
## Variabel Laten X_Eksogen
## [,1]
## [1,] 0.9985514
## [2,] 0.9982884
## [3,] 0.9986615
##
## Variabel Laten Y_Endogen
## [,1]
## [1,] 0.9979779
## [2,] 0.9984247
## [3,] 0.9982392
##
## R Square:
## Variabel Laten X_Eksogen
## [1] NA
##
## Variabel Laten Y_Endogen
## [1] 0.9889589
scores_list2 <- pls_local_2[["Y Final"]]
score_x2 <- as.numeric(scores_list2[[1]])
score_y2 <- as.numeric(scores_list2[[2]])
slope_2 <- pls_local_2$`Path Coefficient`$Y_Endogen[1,1]
data_plot2 <- data.frame(Score_X = score_x2, Score_Y = score_y2)
p2 <- ggplot(data_plot2, aes(x = Score_X, y = Score_Y)) +
geom_point(color = "#3498db", alpha = 0.6, size = 2) +
geom_abline(intercept = 0, slope = slope_2, color = "#e74c3c", size = 1) +
labs(
title = "Local Model B (Cluster 2)",
subtitle = paste("Path Coefficient =", round(slope_2, 3)),
x = "Latent Score Ksi",
y = "Latent Score Eta"
) +
theme_minimal() +
theme(plot.title = element_text(face = "bold"))
final_plot <- p1 + p2 +
plot_annotation(
title = 'Comparison Local Model: Group A vs Group B',
caption = 'Method: PCA-PI-PLS-ABS'
)
print(final_plot)
