R Notebook
list.files()## [1] "~$Data Penelitian R.xlsx"
## [2] "19 Mei 2025, SEM PLS with R, Laifa"
## [3] "bang darma vicarious shame"
## [4] "dat.txt"
## [5] "dat_pls_untuk_R.txt"
## [6] "DATA PENELITIAN OKk.xlsx"
## [7] "Data Penelitian R.xlsx"
## [8] "KODE-R-PLS-SEM.Rmd"
## [9] "KODE-R-PLS-SEM_files"
## [10] "KODE R PLS SEM.Rmd"
## [11] "Pengaruh Destination Image terhadap Revisit Intention"
## [12] "Pengaruh E Tourism Quality terhadap Tourist Satisfaction dan Revisit"
## [13] "project smartpls.rar"
## [14] "Rizki et al revisi"
## [15] "Vicarious Shame and Brand Unfollow Behavior"
## [16] "Vicarious Shame and Brand Unfollow.docx"Link referensi PLS SEM dengan R:
https://rpubs.com/Thoriqalayubi12/SEM-PLS
https://sem-in-r.github.io/seminr/
dat <- read.table("dat_pls_untuk_R.txt", header = T, sep = "\t")
dat <- as.data.frame(dat)
datlibrary(seminr)
library(readxl)
library(openxlsx)model_pengukuran <- constructs(
composite("BI", multi_items("BI_", 1:4)),
composite("SBC", multi_items("SBC_", 1:3)),
composite("BO", multi_items("BO_", 1:3)),
composite("VS", multi_items("VS_", 1:5)),
composite("BD", multi_items("BD_", 1:6)),
composite("UI", multi_items("UI_", 1:4)),
composite("SBCxBI", single_item("BIxSBC")),
composite("BOxVS", single_item("VSxBO"))
# composite("SBCxBI", multi_items("BIxSBC_", 1:1)),
#composite("BOxVS", multi_items("VSxBO_", 1:1))
)model_struktural <- relationships(paths(from="BI",
to=c("VS","UI")),
paths(from="VS",
to=c("BD")),
paths(from="BD",
to=c("UI")),
paths(from="SBC",
to=c("VS")),
paths(from="BO",
to=c("BD"))
#paths(from="SBCxBI",
#to=c("VS")),
#paths(from="BOxVS",
#to=c("BD"))
)hasil_PLS <- estimate_pls(data = dat,
measurement_model = model_pengukuran,
structural_model = model_struktural,
inner_weights = path_weighting,
missing = mean_replacement,
missing_value = "-99")## Generating the seminr model## All 384 observations are valid.## Generating the seminr model
## All 344 observations are valid.ringkasan_hasil_PLS <- summary(hasil_PLS)
ringkasan_hasil_PLS##
## Results from package seminr (2.3.6)
##
## Path Coefficients:
## VS UI BD
## R^2 0.454 0.487 0.074
## AdjR^2 0.451 0.485 0.070
## BI 0.351 0.498 .
## VS . . 0.234
## BD . 0.358 .
## SBC 0.543 . .
## BO . . 0.059
##
## Reliability:
## alpha rhoC AVE rhoA
## BI 0.842 0.894 0.679 0.845
## VS 0.868 0.905 0.655 0.869
## BD 0.809 0.863 0.512 0.811
## SBC 0.771 0.867 0.685 0.773
## BO 0.837 0.902 0.754 0.840
## UI 0.863 0.907 0.708 0.863
##
## Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5names(ringkasan_hasil_PLS)## [1] "meta" "iterations" "paths"
## [4] "total_effects" "total_indirect_effects" "loadings"
## [7] "weights" "validity" "reliability"
## [10] "composite_scores" "vif_antecedents" "fSquare"
## [13] "descriptives" "it_criteria"cetak_outer_loading <- ringkasan_hasil_PLS$loadings
cetak_outer_loading## BI VS BD SBC BO UI
## BI_1 0.826 0.000 0.000 0.000 0.000 0.000
## BI_2 0.806 0.000 0.000 0.000 0.000 0.000
## BI_3 0.843 0.000 0.000 0.000 0.000 0.000
## BI_4 0.819 0.000 0.000 0.000 0.000 0.000
## SBC_1 0.000 0.000 0.000 0.834 0.000 0.000
## SBC_2 0.000 0.000 0.000 0.818 0.000 0.000
## SBC_3 0.000 0.000 0.000 0.831 0.000 0.000
## BO_1 0.000 0.000 0.000 0.000 0.870 0.000
## BO_2 0.000 0.000 0.000 0.000 0.863 0.000
## BO_3 0.000 0.000 0.000 0.000 0.872 0.000
## VS_1 0.000 0.807 0.000 0.000 0.000 0.000
## VS_2 0.000 0.829 0.000 0.000 0.000 0.000
## VS_3 0.000 0.802 0.000 0.000 0.000 0.000
## VS_4 0.000 0.807 0.000 0.000 0.000 0.000
## VS_5 0.000 0.802 0.000 0.000 0.000 0.000
## BD_1 0.000 0.000 0.731 0.000 0.000 0.000
## BD_2 0.000 0.000 0.703 0.000 0.000 0.000
## BD_3 0.000 0.000 0.704 0.000 0.000 0.000
## BD_4 0.000 0.000 0.709 0.000 0.000 0.000
## BD_5 0.000 0.000 0.708 0.000 0.000 0.000
## BD_6 0.000 0.000 0.736 0.000 0.000 0.000
## UI_1 0.000 0.000 0.000 0.000 0.000 0.858
## UI_2 0.000 0.000 0.000 0.000 0.000 0.838
## UI_3 0.000 0.000 0.000 0.000 0.000 0.828
## UI_4 0.000 0.000 0.000 0.000 0.000 0.841ringkasan_hasil_PLS$validity## $vif_items
## BI :
## BI_1 BI_2 BI_3 BI_4
## 1.874 1.764 1.897 1.860
##
## VS :
## VS_1 VS_2 VS_3 VS_4 VS_5
## 1.904 2.068 1.853 1.898 1.894
##
## BD :
## BD_1 BD_2 BD_3 BD_4 BD_5 BD_6
## 1.534 1.489 1.464 1.446 1.490 1.519
##
## SBC :
## SBC_1 SBC_2 SBC_3
## 1.573 1.599 1.557
##
## BO :
## BO_1 BO_2 BO_3
## 2.013 1.987 1.889
##
## UI :
## UI_1 UI_2 UI_3 UI_4
## 2.198 2.098 1.938 2.045
##
##
## $htmt
## BI VS BD SBC BO UI
## BI . . . . . .
## VS 0.468 . . . . .
## BD 0.375 0.318 . . . .
## SBC 0.115 0.703 0.428 . . .
## BO 0.324 0.688 0.236 0.606 . .
## UI 0.714 0.309 0.613 0.348 0.205 .
##
## $fl_criteria
## BI VS BD SBC BO UI
## BI 0.824 . . . . .
## VS 0.403 0.809 . . . .
## BD 0.313 0.268 0.715 . . .
## SBC 0.096 0.576 0.338 0.828 . .
## BO 0.275 0.588 0.196 0.487 0.868 .
## UI 0.610 0.267 0.514 0.284 0.174 0.841
##
## FL Criteria table reports square root of AVE on the diagonal and construct correlations on the lower triangle.
##
## $cross_loadings
## BI VS BD SBC BO UI
## BI_1 0.826 0.298 0.265 0.055 0.210 0.516
## BI_2 0.806 0.319 0.220 0.089 0.255 0.476
## BI_3 0.843 0.391 0.312 0.126 0.265 0.532
## BI_4 0.819 0.311 0.227 0.040 0.172 0.481
## SBC_1 0.091 0.494 0.296 0.834 0.339 0.244
## SBC_2 0.052 0.441 0.262 0.818 0.428 0.233
## SBC_3 0.092 0.493 0.280 0.831 0.444 0.228
## BO_1 0.233 0.521 0.167 0.427 0.870 0.165
## BO_2 0.207 0.496 0.161 0.402 0.863 0.128
## BO_3 0.273 0.513 0.182 0.437 0.872 0.160
## VS_1 0.330 0.807 0.226 0.457 0.492 0.221
## VS_2 0.324 0.829 0.264 0.457 0.456 0.232
## VS_3 0.338 0.802 0.203 0.480 0.513 0.241
## VS_4 0.358 0.807 0.214 0.454 0.478 0.195
## VS_5 0.276 0.802 0.177 0.486 0.436 0.190
## BD_1 0.240 0.196 0.731 0.261 0.155 0.374
## BD_2 0.227 0.146 0.703 0.213 0.108 0.349
## BD_3 0.238 0.172 0.704 0.218 0.105 0.370
## BD_4 0.197 0.180 0.709 0.265 0.164 0.386
## BD_5 0.181 0.229 0.708 0.280 0.137 0.321
## BD_6 0.257 0.224 0.736 0.218 0.167 0.399
## UI_1 0.533 0.216 0.437 0.232 0.149 0.858
## UI_2 0.499 0.259 0.406 0.245 0.160 0.838
## UI_3 0.525 0.172 0.425 0.235 0.137 0.828
## UI_4 0.494 0.255 0.462 0.244 0.142 0.841cetak_reliabilitas <- ringkasan_hasil_PLS$reliability
cetak_reliabilitas## alpha rhoC AVE rhoA
## BI 0.842 0.894 0.679 0.845
## VS 0.868 0.905 0.655 0.869
## BD 0.809 0.863 0.512 0.811
## SBC 0.771 0.867 0.685 0.773
## BO 0.837 0.902 0.754 0.840
## UI 0.863 0.907 0.708 0.863
##
## Alpha, rhoC, and rhoA should exceed 0.7 while AVE should exceed 0.5ringkasan_hasil_PLS$paths## VS UI BD
## R^2 0.454 0.487 0.074
## AdjR^2 0.451 0.485 0.070
## BI 0.351 0.498 .
## VS . . 0.234
## BD . 0.358 .
## SBC 0.543 . .
## BO . . 0.059ringkasan_hasil_PLS$fSquare## BI VS BD SBC BO UI
## BI 0.000 0.222 0.000 0.000 0.000 0.434
## VS 0.000 0.000 0.039 0.000 0.000 0.000
## BD 0.000 0.000 0.000 0.000 0.000 0.225
## SBC 0.000 0.532 0.000 0.000 0.000 0.000
## BO 0.000 0.000 0.002 0.000 0.000 0.000
## UI 0.000 0.000 0.000 0.000 0.000 0.000plot(model_struktural)plot(hasil_PLS)plot(ringkasan_hasil_PLS$reliability)
Copyboot_corp_rep <- bootstrap_model(seminr_model = hasil_PLS,
nboot = 1000,
cores = NULL,
seed = 123)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrapped## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedsum_boot_corp_rep <- summary(Copyboot_corp_rep)
print(sum_boot_corp_rep$bootstrapped_paths)## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI 97.5% CI
## BI -> VS 0.351 0.352 0.058 6.057 0.241 0.464
## BI -> UI 0.498 0.492 0.063 7.913 0.369 0.612
## VS -> BD 0.234 0.235 0.056 4.198 0.134 0.348
## BD -> UI 0.358 0.365 0.066 5.409 0.246 0.499
## SBC -> VS 0.543 0.546 0.057 9.580 0.429 0.651
## BO -> BD 0.059 0.063 0.047 1.258 -0.028 0.159X <- specific_effect_significance(boot_seminr_model = Copyboot_corp_rep,
from = "BI",
through = "VS",
to = "BD",
alpha = 0.05)
print(X)## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.08195385 0.08267741 0.02431619 3.37034072 0.04245274
## 97.5% CI
## 0.13665941X <- specific_effect_significance(boot_seminr_model = Copyboot_corp_rep,
from = "VS",
through = "BD",
to = "UI",
alpha = 0.05)
print(X)## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.08371870 0.08646818 0.02842429 2.94532221 0.03966043
## 97.5% CI
## 0.14615188X <- specific_effect_significance(boot_seminr_model = Copyboot_corp_rep,
from = "BI",
through = c("VS","BD"),
to = "UI",
alpha = 0.05)
print(X)## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## 0.02934783 0.03075531 0.01233948 2.37836796 0.01246017
## 97.5% CI
## 0.05961543data_cetak <- cetak_outer_loading
data_cetak <- as.data.frame(data_cetak)
wb <- openxlsx::createWorkbook()
hs1 <- openxlsx::createStyle(fgFill = "#DCE6F1", halign = "CENTER", textDecoration = "italic",
border = "Bottom")
hs2 <- openxlsx::createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
halign = "center", valign = "center", textDecoration = "bold",
border = "TopBottomLeftRight")
openxlsx::addWorksheet(wb, "Outer Loading", gridLines = TRUE)
openxlsx::writeDataTable(wb, "Outer Loading", data_cetak, rowNames = FALSE, startRow = 2, startCol = 2, tableStyle = "TableStyleMedium21")
openxlsx::openXL(wb)data_cetak <- cetak_reliabilitas
data_cetak <- as.data.frame(data_cetak)
wb <- openxlsx::createWorkbook()
hs1 <- openxlsx::createStyle(fgFill = "#DCE6F1", halign = "CENTER", textDecoration = "italic",
border = "Bottom")
hs2 <- openxlsx::createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
halign = "center", valign = "center", textDecoration = "bold",
border = "TopBottomLeftRight")
openxlsx::addWorksheet(wb, "Reliabilitas", gridLines = TRUE)
openxlsx::writeDataTable(wb, "Reliabilitas", data_cetak, rowNames = FALSE, startRow = 2, startCol = 2, tableStyle = "TableStyleMedium21")
openxlsx::openXL(wb)hasil_htmt <- ringkasan_hasil_PLS$validity$htmt
hasil_htmt <- as.data.frame(hasil_htmt)
hasil_htmtdata_cetak <- hasil_htmt
data_cetak <- as.data.frame(data_cetak)
wb <- openxlsx::createWorkbook()
hs1 <- openxlsx::createStyle(fgFill = "#DCE6F1", halign = "CENTER", textDecoration = "italic",
border = "Bottom")
hs2 <- openxlsx::createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
halign = "center", valign = "center", textDecoration = "bold",
border = "TopBottomLeftRight")
openxlsx::addWorksheet(wb, "HTMT", gridLines = TRUE)
openxlsx::writeDataTable(wb, "HTMT", data_cetak, rowNames = TRUE, startRow = 2, startCol = 2, tableStyle = "TableStyleMedium21")
openxlsx::openXL(wb)hasil_fl <- ringkasan_hasil_PLS$validity$fl_criteria
hasil_fl## BI VS BD SBC BO UI
## BI 0.824 . . . . .
## VS 0.403 0.809 . . . .
## BD 0.313 0.268 0.715 . . .
## SBC 0.096 0.576 0.338 0.828 . .
## BO 0.275 0.588 0.196 0.487 0.868 .
## UI 0.610 0.267 0.514 0.284 0.174 0.841
##
## FL Criteria table reports square root of AVE on the diagonal and construct correlations on the lower triangle.data_cetak <- hasil_fl
data_cetak <- as.data.frame(data_cetak)
wb <- openxlsx::createWorkbook()
hs1 <- openxlsx::createStyle(fgFill = "#DCE6F1", halign = "CENTER", textDecoration = "italic",
border = "Bottom")
hs2 <- openxlsx::createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
halign = "center", valign = "center", textDecoration = "bold",
border = "TopBottomLeftRight")
openxlsx::addWorksheet(wb, "Fornel-Larcker", gridLines = TRUE)
openxlsx::writeDataTable(wb, "Fornel-Larcker", data_cetak, rowNames = TRUE, startRow = 2, startCol = 2, tableStyle = "TableStyleMedium21")data_cetak <- as.data.frame(data_cetak)
data_cetak wb <- openxlsx::createWorkbook()
hs1 <- openxlsx::createStyle(fgFill = "#DCE6F1", halign = "CENTER", textDecoration = "italic",
border = "Bottom")
hs2 <- openxlsx::createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
halign = "center", valign = "center", textDecoration = "bold",
border = "TopBottomLeftRight")
openxlsx::addWorksheet(wb, "Fornel-Larcker", gridLines = TRUE)
openxlsx::writeDataTable(wb, "Fornel-Larcker", data_cetak, rowNames = TRUE, startRow = 2, startCol = 2, tableStyle = "TableStyleMedium21")
openxlsx::openXL(wb)hasil_hipotesis <- sum_boot_corp_rep$bootstrapped_paths
hasil_hipotesis <- as.data.frame(hasil_hipotesis)
hasil_hipotesisdata_cetak <- hasil_hipotesis
wb <- openxlsx::createWorkbook()
hs1 <- openxlsx::createStyle(fgFill = "#DCE6F1", halign = "CENTER", textDecoration = "italic",
border = "Bottom")
hs2 <- openxlsx::createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
halign = "center", valign = "center", textDecoration = "bold",
border = "TopBottomLeftRight")
openxlsx::addWorksheet(wb, "Hipotesis", gridLines = TRUE)
openxlsx::writeDataTable(wb, "Hipotesis", data_cetak, rowNames = TRUE, startRow = 2, startCol = 2, tableStyle = "TableStyleMedium21")
openxlsx::openXL(wb)hasil_fsquare <- ringkasan_hasil_PLS$fSquare
hasil_fsquare <- as.data.frame(hasil_fsquare)
data_cetak <- hasil_fsquare
wb <- openxlsx::createWorkbook()
hs1 <- openxlsx::createStyle(fgFill = "#DCE6F1", halign = "CENTER", textDecoration = "italic",
border = "Bottom")
hs2 <- openxlsx::createStyle(fontColour = "#ffffff", fgFill = "#4F80BD",
halign = "center", valign = "center", textDecoration = "bold",
border = "TopBottomLeftRight")
openxlsx::addWorksheet(wb, "F-Square", gridLines = TRUE)
openxlsx::writeDataTable(wb, "F-Square", data_cetak, rowNames = TRUE, startRow = 2, startCol = 2, tableStyle = "TableStyleMedium21")
openxlsx::openXL(wb)model_struktural_moderasi_sbc <- relationships(
paths(from="BI",
to=c("VS")),
# paths(from="VS",
# to=c("BD")),
# paths(from="BD",
# to=c("UI")),
paths(from="SBC",
to=c("VS")),
# paths(from="BO",
# to=c("BD"))
paths(from="SBCxBI",
to=c("VS"))
#paths(from="BOxVS",
#to=c("BD"))
)hasil_PLS_moderasi_sbc <- estimate_pls(data = dat,
measurement_model = model_pengukuran,
structural_model = model_struktural_moderasi_sbc,
inner_weights = path_weighting,
missing = mean_replacement,
missing_value = "-99")## Generating the seminr model## All 384 observations are valid.## Generating the seminr model
## All 344 observations are valid.Copyboot_corp_rep <- bootstrap_model(seminr_model = hasil_PLS_moderasi_sbc,
nboot = 1000,
cores = NULL,
seed = 123)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrapped## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedsum_boot_corp_rep <- summary(Copyboot_corp_rep)
print(sum_boot_corp_rep$bootstrapped_paths)## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## BI -> VS -0.008 0.050 0.199 -0.039 -0.337
## SBC -> VS 0.186 0.240 0.211 0.881 -0.208
## SBCxBI -> VS 0.536 0.457 0.315 1.700 -0.111
## 97.5% CI
## BI -> VS 0.446
## SBC -> VS 0.641
## SBCxBI -> VS 1.095model_struktural_moderasi_bo <- relationships(
#paths(from="BI",
#to=c("VS")),
paths(from="VS",
to=c("BD")),
# paths(from="BD",
# to=c("UI")),
#paths(from="SBC",
#to=c("VS")),
paths(from="BO",
to=c("BD")),
# paths(from="SBCxBI",
# to=c("VS"))
paths(from="BOxVS",
to=c("BD"))
)hasil_PLS_moderasi_bo <- estimate_pls(data = dat,
measurement_model = model_pengukuran,
structural_model = model_struktural_moderasi_bo,
inner_weights = path_weighting,
missing = mean_replacement,
missing_value = "-99")## Generating the seminr model## All 384 observations are valid.## Generating the seminr model
## All 344 observations are valid.Copyboot_corp_rep <- bootstrap_model(seminr_model = hasil_PLS_moderasi_bo,
nboot = 1000,
cores = NULL,
seed = 123)## Bootstrapping model using seminr...## SEMinR Model successfully bootstrapped## Bootstrapping model using seminr...
## SEMinR Model successfully bootstrappedsum_boot_corp_rep <- summary(Copyboot_corp_rep)
print(sum_boot_corp_rep$bootstrapped_paths)## Original Est. Bootstrap Mean Bootstrap SD T Stat. 2.5% CI
## VS -> BD -0.243 -0.193 0.139 -1.744 -0.485
## BO -> BD -0.680 -0.631 0.300 -2.267 -1.271
## BOxVS -> BD 1.113 1.035 0.393 2.831 0.324
## 97.5% CI
## VS -> BD 0.069
## BO -> BD -0.135
## BOxVS -> BD 1.869