harga <- read.csv("Data/harga.csv")
head(harga) City Bread Burger Milk Oranges Tomatoes
1 Atlanta 24.5 94.5 73.9 80.1 41.6
2 Baltimore 26.5 91.0 67.5 74.6 33.3
3 Boston 29.7 100.8 61.4 104.0 59.6
4 Buffalo 22.8 86.6 65.3 118.4 61.2
5 Chicago 26.7 86.7 62.7 105.9 60.2
6 Cincinnati 25.3 102.5 63.3 99.3 45.6str(harga)'data.frame': 23 obs. of 6 variables:
$ City : chr "Atlanta " "Baltimore " "Boston " "Buffalo " ...
$ Bread : num 24.5 26.5 29.7 22.8 26.7 25.3 22.8 23.3 24.1 29.3 ...
$ Burger : num 94.5 91 100.8 86.6 86.7 ...
$ Milk : num 73.9 67.5 61.4 65.3 62.7 63.3 52.4 62.5 51.5 80.2 ...
$ Oranges : num 80.1 74.6 104 118.4 105.9 ...
$ Tomatoes: num 41.6 33.3 59.6 61.2 60.2 45.6 60.1 60.8 60.5 71.7 ...library(corrplot)
corrplot(cor(harga[,2:6]), method="number")
library(psych)
KMO(harga[,2:6])Kaiser-Meyer-Olkin factor adequacy
Call: KMO(r = harga[, 2:6])
Overall MSA = 0.52
MSA for each item =
Bread Burger Milk Oranges Tomatoes
0.52 0.58 0.59 0.49 0.48 # Bartlett's Test of Sphericity
cortest.bartlett(harga[,2:6])$chisq
[1] 36.46285
$p.value
[1] 7.006877e-05
$df
[1] 10# Anti image correlation (AIC)
corrplot(KMO(harga[,2:6])$ImCo, method="number") 
# Determinan positif
det(cor(harga[,2:6]))[1] 0.1541406# Principal component analysis (PCA)
pca1 = princomp(harga[,2:6], scores=TRUE, cor=TRUE)
summary(pca1)Importance of components:
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
Standard deviation 1.4841538 1.2325047 0.8824610 0.55357732 0.43935672
Proportion of Variance 0.4405425 0.3038136 0.1557475 0.06128957 0.03860687
Cumulative Proportion 0.4405425 0.7443561 0.9001036 0.96139313 1.00000000scree(harga[,2:6])
# Menentukan faktor loading Analisis faktor loading
loadings(pca1)
Loadings:
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
Bread 0.436 0.484 0.354 0.597 0.306
Burger 0.542 0.292 0.307 -0.657 -0.309
Milk 0.346 0.308 -0.866 -0.163
Oranges 0.410 -0.579 0.108 0.399 -0.571
Tomatoes 0.478 -0.500 -0.137 -0.211 0.677
Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
SS loadings 1.0 1.0 1.0 1.0 1.0
Proportion Var 0.2 0.2 0.2 0.2 0.2
Cumulative Var 0.2 0.4 0.6 0.8 1.0# Rotasi untuk mengkonfirmasi hasil analisis loading
fa1 = factanal(harga[,2:6], factor=2, rotation="varimax")
fa1
Call:
factanal(x = harga[, 2:6], factors = 2, rotation = "varimax")
Uniquenesses:
Bread Burger Milk Oranges Tomatoes
0.239 0.318 0.830 0.420 0.005
Loadings:
Factor1 Factor2
Bread 0.868
Burger 0.195 0.803
Milk 0.135 0.390
Oranges 0.756
Tomatoes 0.985 0.157
Factor1 Factor2
SS loadings 1.605 1.583
Proportion Var 0.321 0.317
Cumulative Var 0.321 0.638
Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 1.16 on 1 degree of freedom.
The p-value is 0.282 # Diagram jalur hasil analisis EFA dan menampilkan faktor loading-nya
fa.diagram(fa1$loadings, digits = 3)
# Spesifikasi model
attach(harga)
model1 <- "
F1 =~ Tomatoes + Oranges
F2 =~ Bread + Burger + Milk
F1 ~~ F2 "library(lavaan)
fitmod = cfa(model1, data = harga)
summary(fitmod, fit.measures = TRUE, standardized = TRUE)lavaan 0.6-19 ended normally after 85 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 11
Number of observations 23
Model Test User Model:
Test statistic 3.642
Degrees of freedom 4
P-value (Chi-square) 0.457
Model Test Baseline Model:
Test statistic 43.007
Degrees of freedom 10
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000
Tucker-Lewis Index (TLI) 1.027
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -367.812
Loglikelihood unrestricted model (H1) -365.991
Akaike (AIC) 757.623
Bayesian (BIC) 770.114
Sample-size adjusted Bayesian (SABIC) 736.072
Root Mean Square Error of Approximation:
RMSEA 0.000
90 Percent confidence interval - lower 0.000
90 Percent confidence interval - upper 0.302
P-value H_0: RMSEA <= 0.050 0.487
P-value H_0: RMSEA >= 0.080 0.469
Standardized Root Mean Square Residual:
SRMR 0.065
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
F1 =~
Tomatoes 1.000 10.659 1.062
Oranges 0.934 0.580 1.611 0.107 9.952 0.715
F2 =~
Bread 1.000 1.622 0.662
Burger 4.700 2.464 1.907 0.056 7.623 1.032
Milk 1.307 0.858 1.523 0.128 2.119 0.312
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
F1 ~~
F2 5.161 4.482 1.151 0.250 0.299 0.299
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Tomatoes -12.966 66.742 -0.194 0.846 -12.966 -0.129
.Oranges 94.906 64.476 1.472 0.141 94.906 0.489
.Bread 3.381 1.581 2.138 0.033 3.381 0.562
.Burger -3.518 27.199 -0.129 0.897 -3.518 -0.064
.Milk 41.714 12.439 3.354 0.001 41.714 0.903
F1 113.605 72.842 1.560 0.119 1.000 1.000
F2 2.631 1.912 1.376 0.169 1.000 1.000fitmeasures(fitmod) npar fmin chisq
11.000 0.079 3.642
df pvalue baseline.chisq
4.000 0.457 43.007
baseline.df baseline.pvalue cfi
10.000 0.000 1.000
tli nnfi rfi
1.027 1.027 0.788
nfi pnfi ifi
0.915 0.366 1.009
rni logl unrestricted.logl
1.011 -367.812 -365.991
aic bic ntotal
757.623 770.114 23.000
bic2 rmsea rmsea.ci.lower
736.072 0.000 0.000
rmsea.ci.upper rmsea.ci.level rmsea.pvalue
0.302 0.900 0.487
rmsea.close.h0 rmsea.notclose.pvalue rmsea.notclose.h0
0.050 0.469 0.080
rmr rmr_nomean srmr
2.823 2.823 0.065
srmr_bentler srmr_bentler_nomean crmr
0.065 0.065 0.080
crmr_nomean srmr_mplus srmr_mplus_nomean
0.080 0.065 0.065
cn_05 cn_01 gfi
60.915 84.843 0.947
agfi pgfi mfi
0.803 0.253 1.008
ecvi
1.115 library(semPlot)
semPaths(fitmod, what='std', layout='tree', title = TRUE,
posCol = 1, nDigits = 3,
edge.label.cex=0.7,
exoVar = FALSE,
sizeMan = 5,
sizeLat = 5)
# Estimasi Reliabilitas alpha cronbach
psych::alpha(harga[,2:6])
Reliability analysis
Call: psych::alpha(x = harga[, 2:6])
raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
0.63 0.67 0.77 0.29 2.1 0.1 67 5.8 0.26
95% confidence boundaries
lower alpha upper
Feldt 0.32 0.63 0.82
Duhachek 0.42 0.63 0.83
Reliability if an item is dropped:
raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
Bread 0.64 0.63 0.68 0.30 1.7 0.110 0.065 0.26
Burger 0.56 0.54 0.63 0.23 1.2 0.107 0.083 0.13
Milk 0.64 0.68 0.78 0.34 2.1 0.091 0.096 0.26
Oranges 0.55 0.65 0.66 0.32 1.9 0.140 0.043 0.32
Tomatoes 0.37 0.59 0.61 0.26 1.4 0.197 0.062 0.27
Item statistics
n raw.r std.r r.cor r.drop mean sd
Bread 23 0.38 0.64 0.56 0.30 25 2.5
Burger 23 0.62 0.77 0.72 0.41 92 7.6
Milk 23 0.42 0.56 0.36 0.20 62 7.0
Oranges 23 0.82 0.61 0.56 0.49 103 14.2
Tomatoes 23 0.86 0.71 0.68 0.71 52 10.3library(lavaan)
library(semPlot)library(readxl)
datasem <- read_excel("Data/Datalikert.xlsx")
head(datasem[,1:5])# A tibble: 6 × 5
Perusahaan Provinsi Pulau A1 A2
<dbl> <chr> <chr> <dbl> <dbl>
1 1 Jawa Barat Jawa 4 5
2 2 Jawa Timur Jawa 5 5
3 3 Jawa Timur Jawa 4 4
4 4 Jawa Barat Jawa 4 4
5 5 Jawa Timur Jawa 4 4
6 6 Jawa Timur Jawa 4 4str(datasem)tibble [300 × 45] (S3: tbl_df/tbl/data.frame)
$ Perusahaan: num [1:300] 1 2 3 4 5 6 7 8 9 10 ...
$ Provinsi : chr [1:300] "Jawa Barat" "Jawa Timur" "Jawa Timur" "Jawa Barat" ...
$ Pulau : chr [1:300] "Jawa" "Jawa" "Jawa" "Jawa" ...
$ A1 : num [1:300] 4 5 4 4 4 4 4 5 4 5 ...
$ A2 : num [1:300] 5 5 4 4 4 4 4 5 4 5 ...
$ A3 : num [1:300] 5 5 4 3 4 5 4 5 3 5 ...
$ A4 : num [1:300] 4 5 4 4 3 4 4 5 3 5 ...
$ A5 : num [1:300] 4 4 4 4 4 4 4 5 3 5 ...
$ A6 : num [1:300] 4 5 4 4 4 4 4 5 3 4 ...
$ A7 : num [1:300] 5 5 5 4 4 4 4 5 3 5 ...
$ A8 : num [1:300] 5 5 5 4 4 4 4 5 3 4 ...
$ Atotal : num [1:300] 36 39 34 31 31 33 32 40 26 38 ...
$ B1 : num [1:300] 4 4 4 4 3 5 3 3 3 4 ...
$ B2 : num [1:300] 4 4 4 3 4 4 3 3 2 4 ...
$ Btotal : num [1:300] 8 8 8 7 7 9 6 6 5 8 ...
$ C1 : num [1:300] 4 4 4 4 4 4 4 5 3 4 ...
$ C2 : num [1:300] 4 4 4 4 4 4 4 4 3 4 ...
$ Ctotal : num [1:300] 8 8 8 8 8 8 8 9 6 8 ...
$ D1 : num [1:300] 4 5 4 4 4 4 4 4 3 4 ...
$ D2 : num [1:300] 4 5 4 3 4 5 4 4 2 4 ...
$ D3 : num [1:300] 4 5 4 4 4 4 4 4 3 4 ...
$ D4 : num [1:300] 4 5 4 5 4 4 4 4 3 4 ...
$ Dtotal : num [1:300] 16 20 16 16 16 17 16 16 11 16 ...
$ E1 : num [1:300] 5 5 4 4 4 4 4 4 3 5 ...
$ E2 : num [1:300] 5 5 4 4 4 5 4 4 3 5 ...
$ E3 : num [1:300] 5 5 4 4 4 5 4 5 4 5 ...
$ E4 : num [1:300] 4 5 4 3 4 5 4 4 3 4 ...
$ E5 : num [1:300] 4 5 4 4 3 5 4 4 3 4 ...
$ E6 : num [1:300] 4 5 4 4 4 4 4 4 3 4 ...
$ E7 : num [1:300] 4 5 4 4 4 5 4 4 3 4 ...
$ E8 : num [1:300] 4 5 4 4 3 5 4 4 3 4 ...
$ E9 : num [1:300] 4 5 4 4 4 4 4 4 3 4 ...
$ E10 : num [1:300] 4 5 4 4 4 5 4 5 3 4 ...
$ E11 : num [1:300] 4 5 4 3 3 5 4 5 3 4 ...
$ E12 : num [1:300] 5 5 4 4 4 5 4 5 3 5 ...
$ Etotal : num [1:300] 52 60 48 46 45 57 48 52 37 52 ...
$ F1 : num [1:300] 5 5 4 4 4 5 4 4 2 4 ...
$ F2 : num [1:300] 4 5 4 4 4 5 4 4 3 3 ...
$ F3 : num [1:300] 4 5 4 4 4 4 4 4 2 3 ...
$ F4 : num [1:300] 4 5 4 4 4 5 4 5 3 4 ...
$ F5 : num [1:300] 4 5 4 4 3 5 4 4 3 3 ...
$ F6 : num [1:300] 4 5 4 4 3 4 4 5 3 4 ...
$ F7 : num [1:300] 4 5 4 4 3 4 4 4 3 4 ...
$ F8 : num [1:300] 4 5 4 4 4 5 4 4 3 4 ...
$ Ftotal : num [1:300] 33 40 32 32 29 37 32 34 22 29 ...attach(datasem)
table(A1)A1
1 2 3 4 5
3 4 37 121 135 barplot(table(A1))
# Spesifikasi Model
sem.model = "
faktor =~ A1 + A2 + A3 + A4
permintaan =~ B1 + B2
industri =~ C1 + C2
strategi =~ D1 + D2 + D3 + D4
regulasi =~ E1 + E2 + E3 + E4 + E5 + E6
kesempatan =~ F1 + F2 + F3 + F4
kesempatan ~ faktor + permintaan + industri + strategi + regulasi"sem.fit = sem(sem.model, data = datasem)
summary(sem.fit, fit.measures=TRUE)lavaan 0.6-19 ended normally after 90 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 59
Number of observations 300
Model Test User Model:
Test statistic 555.757
Degrees of freedom 194
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 7355.210
Degrees of freedom 231
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.949
Tucker-Lewis Index (TLI) 0.940
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4608.159
Loglikelihood unrestricted model (H1) -4330.280
Akaike (AIC) 9334.318
Bayesian (BIC) 9552.841
Sample-size adjusted Bayesian (SABIC) 9365.728
Root Mean Square Error of Approximation:
RMSEA 0.079
90 Percent confidence interval - lower 0.071
90 Percent confidence interval - upper 0.087
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.410
Standardized Root Mean Square Residual:
SRMR 0.035
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|)
faktor =~
A1 1.000
A2 1.266 0.089 14.271 0.000
A3 1.312 0.094 13.991 0.000
A4 1.261 0.091 13.913 0.000
permintaan =~
B1 1.000
B2 1.020 0.063 16.072 0.000
industri =~
C1 1.000
C2 1.035 0.044 23.446 0.000
strategi =~
D1 1.000
D2 0.973 0.033 29.472 0.000
D3 0.972 0.043 22.590 0.000
D4 0.817 0.042 19.325 0.000
regulasi =~
E1 1.000
E2 0.929 0.039 23.666 0.000
E3 0.950 0.043 22.088 0.000
E4 1.015 0.039 25.697 0.000
E5 0.985 0.042 23.464 0.000
E6 0.913 0.045 20.186 0.000
kesempatan =~
F1 1.000
F2 1.006 0.038 26.712 0.000
F3 1.033 0.042 24.672 0.000
F4 0.943 0.046 20.414 0.000
Regressions:
Estimate Std.Err z-value P(>|z|)
kesempatan ~
faktor 0.016 0.111 0.146 0.884
permintaan 0.042 0.059 0.705 0.481
industri 0.129 0.133 0.976 0.329
strategi 0.131 0.091 1.449 0.147
regulasi 0.685 0.077 8.860 0.000
Covariances:
Estimate Std.Err z-value P(>|z|)
faktor ~~
permintaan 0.233 0.034 6.785 0.000
industri 0.327 0.037 8.729 0.000
strategi 0.292 0.035 8.242 0.000
regulasi 0.343 0.039 8.730 0.000
permintaan ~~
industri 0.366 0.043 8.447 0.000
strategi 0.391 0.045 8.713 0.000
regulasi 0.332 0.043 7.797 0.000
industri ~~
strategi 0.437 0.043 10.274 0.000
regulasi 0.416 0.043 9.764 0.000
strategi ~~
regulasi 0.405 0.042 9.580 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.A1 0.323 0.029 11.229 0.000
.A2 0.161 0.018 8.902 0.000
.A3 0.205 0.022 9.430 0.000
.A4 0.198 0.021 9.552 0.000
.B1 0.269 0.032 8.457 0.000
.B2 0.078 0.025 3.161 0.002
.C1 0.122 0.014 8.515 0.000
.C2 0.106 0.014 7.549 0.000
.D1 0.093 0.011 8.749 0.000
.D2 0.063 0.008 7.476 0.000
.D3 0.182 0.017 10.625 0.000
.D4 0.200 0.018 11.219 0.000
.E1 0.145 0.014 10.563 0.000
.E2 0.114 0.011 10.395 0.000
.E3 0.156 0.014 10.845 0.000
.E4 0.091 0.010 9.488 0.000
.E5 0.133 0.013 10.462 0.000
.E6 0.198 0.018 11.224 0.000
.F1 0.139 0.014 9.697 0.000
.F2 0.090 0.011 8.221 0.000
.F3 0.140 0.015 9.540 0.000
.F4 0.233 0.021 10.912 0.000
faktor 0.321 0.047 6.841 0.000
permintaan 0.525 0.065 8.048 0.000
industri 0.480 0.049 9.751 0.000
strategi 0.522 0.050 10.406 0.000
regulasi 0.542 0.055 9.811 0.000
.kesempatan 0.122 0.015 8.068 0.000sem.fit = sem(sem.model, data = datasem, std.lv=TRUE)
summary(sem.fit, fit.measures=TRUE, standardized=TRUE)lavaan 0.6-19 ended normally after 90 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 59
Number of observations 300
Model Test User Model:
Test statistic 555.757
Degrees of freedom 194
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 7355.210
Degrees of freedom 231
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.949
Tucker-Lewis Index (TLI) 0.940
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4608.159
Loglikelihood unrestricted model (H1) -4330.280
Akaike (AIC) 9334.318
Bayesian (BIC) 9552.841
Sample-size adjusted Bayesian (SABIC) 9365.728
Root Mean Square Error of Approximation:
RMSEA 0.079
90 Percent confidence interval - lower 0.071
90 Percent confidence interval - upper 0.087
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.410
Standardized Root Mean Square Residual:
SRMR 0.035
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
faktor =~
A1 0.566 0.041 13.681 0.000 0.566 0.706
A2 0.717 0.038 18.699 0.000 0.717 0.872
A3 0.743 0.041 18.064 0.000 0.743 0.854
A4 0.714 0.040 17.894 0.000 0.714 0.849
permintaan =~
B1 0.725 0.045 16.097 0.000 0.725 0.813
B2 0.739 0.038 19.509 0.000 0.739 0.935
industri =~
C1 0.692 0.036 19.503 0.000 0.692 0.893
C2 0.717 0.036 20.132 0.000 0.717 0.911
strategi =~
D1 0.723 0.035 20.812 0.000 0.723 0.922
D2 0.703 0.033 21.615 0.000 0.703 0.941
D3 0.702 0.038 18.344 0.000 0.702 0.855
D4 0.590 0.036 16.459 0.000 0.590 0.797
regulasi =~
E1 0.736 0.038 19.623 0.000 0.736 0.888
E2 0.684 0.034 19.941 0.000 0.684 0.897
E3 0.699 0.037 18.967 0.000 0.699 0.870
E4 0.747 0.035 21.120 0.000 0.747 0.927
E5 0.725 0.037 19.819 0.000 0.725 0.894
E6 0.673 0.038 17.720 0.000 0.673 0.834
kesempatan =~
F1 0.350 0.022 16.135 0.000 0.771 0.900
F2 0.352 0.021 16.722 0.000 0.776 0.933
F3 0.361 0.022 16.227 0.000 0.796 0.905
F4 0.330 0.022 14.833 0.000 0.727 0.833
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
kesempatan ~
faktor 0.026 0.180 0.146 0.884 0.012 0.012
permintaan 0.086 0.123 0.705 0.481 0.039 0.039
industri 0.256 0.263 0.973 0.331 0.116 0.116
strategi 0.272 0.188 1.447 0.148 0.123 0.123
regulasi 1.443 0.190 7.608 0.000 0.654 0.654
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
faktor ~~
permintaan 0.568 0.046 12.297 0.000 0.568 0.568
industri 0.833 0.025 33.258 0.000 0.833 0.833
strategi 0.715 0.033 21.548 0.000 0.715 0.715
regulasi 0.822 0.023 35.175 0.000 0.822 0.822
permintaan ~~
industri 0.729 0.035 20.610 0.000 0.729 0.729
strategi 0.746 0.032 23.194 0.000 0.746 0.746
regulasi 0.623 0.041 15.291 0.000 0.623 0.623
industri ~~
strategi 0.874 0.020 44.744 0.000 0.874 0.874
regulasi 0.816 0.024 33.446 0.000 0.816 0.816
strategi ~~
regulasi 0.762 0.027 27.976 0.000 0.762 0.762
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.A1 0.323 0.029 11.229 0.000 0.323 0.502
.A2 0.161 0.018 8.902 0.000 0.161 0.239
.A3 0.205 0.022 9.430 0.000 0.205 0.271
.A4 0.198 0.021 9.552 0.000 0.198 0.280
.B1 0.269 0.032 8.457 0.000 0.269 0.339
.B2 0.078 0.025 3.161 0.002 0.078 0.126
.C1 0.122 0.014 8.515 0.000 0.122 0.203
.C2 0.106 0.014 7.549 0.000 0.106 0.171
.D1 0.093 0.011 8.749 0.000 0.093 0.151
.D2 0.063 0.008 7.476 0.000 0.063 0.114
.D3 0.182 0.017 10.625 0.000 0.182 0.270
.D4 0.200 0.018 11.219 0.000 0.200 0.365
.E1 0.145 0.014 10.563 0.000 0.145 0.211
.E2 0.114 0.011 10.395 0.000 0.114 0.195
.E3 0.156 0.014 10.845 0.000 0.156 0.242
.E4 0.091 0.010 9.488 0.000 0.091 0.141
.E5 0.133 0.013 10.462 0.000 0.133 0.201
.E6 0.198 0.018 11.224 0.000 0.198 0.304
.F1 0.139 0.014 9.697 0.000 0.139 0.190
.F2 0.090 0.011 8.221 0.000 0.090 0.130
.F3 0.140 0.015 9.540 0.000 0.140 0.181
.F4 0.233 0.021 10.912 0.000 0.233 0.306
faktor 1.000 1.000 1.000
permintaan 1.000 1.000 1.000
industri 1.000 1.000 1.000
strategi 1.000 1.000 1.000
regulasi 1.000 1.000 1.000
.kesempatan 1.000 0.206 0.206#sem.fit = sem(sem.model, data = datasem, std.lv=TRUE, orthogonal=TRUE)
#summary(sem.fit, fit.measures=TRUE, standardized=TRUE)# Modification Indices
modificationIndices(sem.fit, minimum.value = 10) lhs op rhs mi epc sepc.lv sepc.all sepc.nox
72 faktor =~ D3 10.792 0.143 0.143 0.174 0.174
82 faktor =~ F3 14.022 -0.170 -0.170 -0.193 -0.193
99 permintaan =~ E6 13.919 0.142 0.142 0.176 0.176
112 industri =~ D3 19.393 0.315 0.315 0.383 0.383
134 strategi =~ E3 11.975 -0.144 -0.144 -0.179 -0.179
152 regulasi =~ D3 18.808 0.197 0.197 0.240 0.240
157 regulasi =~ F4 13.142 0.272 0.272 0.312 0.312
168 kesempatan =~ D3 22.896 0.100 0.220 0.268 0.268
175 kesempatan =~ E6 25.214 0.153 0.337 0.418 0.418
176 A1 ~~ A2 15.863 0.068 0.068 0.298 0.298
270 B1 ~~ F4 14.265 0.063 0.063 0.253 0.253
317 D1 ~~ D3 10.752 -0.035 -0.035 -0.272 -0.272
331 D2 ~~ E1 11.098 0.025 0.025 0.257 0.257
347 D3 ~~ E6 12.029 0.042 0.042 0.223 0.223
351 D3 ~~ F4 10.217 -0.043 -0.043 -0.208 -0.208
352 D4 ~~ E1 11.953 -0.038 -0.038 -0.223 -0.223
362 E1 ~~ E2 17.329 0.038 0.038 0.294 0.294
363 E1 ~~ E3 10.360 0.033 0.033 0.220 0.220
364 E1 ~~ E4 12.186 -0.031 -0.031 -0.266 -0.266
371 E2 ~~ E3 11.663 0.032 0.032 0.236 0.236
373 E2 ~~ E5 10.449 -0.028 -0.028 -0.231 -0.231
381 E3 ~~ E6 11.439 -0.039 -0.039 -0.221 -0.221
386 E4 ~~ E5 25.380 0.043 0.043 0.388 0.388
398 E6 ~~ F2 14.478 -0.037 -0.037 -0.275 -0.275
399 E6 ~~ F3 20.998 0.052 0.052 0.310 0.310
405 F2 ~~ F4 24.019 -0.058 -0.058 -0.404 -0.404
406 F3 ~~ F4 14.294 0.050 0.050 0.279 0.279sem.model2 = "
faktor =~ A1 + A2 + A3 + A4
permintaan =~ B1 + B2
industri =~ C1 + C2
strategi =~ D1 + D2 + D3 + D4
regulasi =~ E1 + E2 + E3 + E4 + E5 + E6
kesempatan =~ F1 + F2 + F3 + F4
kesempatan ~ faktor + permintaan + industri + strategi + regulasi
A1 ~~ A2
"sem.fit = sem(sem.model2, data = datasem, std.lv=TRUE)
summary(sem.fit, fit.measures=TRUE, standardized=TRUE)lavaan 0.6-19 ended normally after 94 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 60
Number of observations 300
Model Test User Model:
Test statistic 540.535
Degrees of freedom 193
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 7355.210
Degrees of freedom 231
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.951
Tucker-Lewis Index (TLI) 0.942
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -4600.548
Loglikelihood unrestricted model (H1) -4330.280
Akaike (AIC) 9321.095
Bayesian (BIC) 9543.322
Sample-size adjusted Bayesian (SABIC) 9353.038
Root Mean Square Error of Approximation:
RMSEA 0.077
90 Percent confidence interval - lower 0.070
90 Percent confidence interval - upper 0.085
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.303
Standardized Root Mean Square Residual:
SRMR 0.035
Parameter Estimates:
Standard errors Standard
Information Expected
Information saturated (h1) model Structured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
faktor =~
A1 0.539 0.043 12.660 0.000 0.539 0.672
A2 0.702 0.039 18.009 0.000 0.702 0.854
A3 0.752 0.041 18.363 0.000 0.752 0.864
A4 0.720 0.040 18.060 0.000 0.720 0.855
permintaan =~
B1 0.724 0.045 16.093 0.000 0.724 0.813
B2 0.739 0.038 19.507 0.000 0.739 0.935
industri =~
C1 0.692 0.036 19.469 0.000 0.692 0.892
C2 0.717 0.036 20.171 0.000 0.717 0.912
strategi =~
D1 0.723 0.035 20.813 0.000 0.723 0.922
D2 0.703 0.033 21.613 0.000 0.703 0.941
D3 0.702 0.038 18.345 0.000 0.702 0.855
D4 0.590 0.036 16.460 0.000 0.590 0.797
regulasi =~
E1 0.736 0.038 19.615 0.000 0.736 0.888
E2 0.684 0.034 19.943 0.000 0.684 0.897
E3 0.699 0.037 18.964 0.000 0.699 0.870
E4 0.747 0.035 21.115 0.000 0.747 0.927
E5 0.726 0.037 19.826 0.000 0.726 0.894
E6 0.673 0.038 17.728 0.000 0.673 0.834
kesempatan =~
F1 0.350 0.022 16.137 0.000 0.771 0.900
F2 0.352 0.021 16.726 0.000 0.776 0.933
F3 0.361 0.022 16.232 0.000 0.796 0.905
F4 0.330 0.022 14.836 0.000 0.727 0.833
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
kesempatan ~
faktor 0.031 0.186 0.167 0.867 0.014 0.014
permintaan 0.087 0.122 0.709 0.478 0.039 0.039
industri 0.253 0.267 0.947 0.344 0.115 0.115
strategi 0.272 0.189 1.442 0.149 0.123 0.123
regulasi 1.441 0.190 7.578 0.000 0.654 0.654
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.A1 ~~
.A2 0.068 0.019 3.588 0.000 0.068 0.269
faktor ~~
permintaan 0.573 0.046 12.417 0.000 0.573 0.573
industri 0.837 0.025 33.458 0.000 0.837 0.837
strategi 0.716 0.033 21.421 0.000 0.716 0.716
regulasi 0.824 0.024 34.919 0.000 0.824 0.824
permintaan ~~
industri 0.729 0.035 20.581 0.000 0.729 0.729
strategi 0.746 0.032 23.189 0.000 0.746 0.746
regulasi 0.623 0.041 15.292 0.000 0.623 0.623
industri ~~
strategi 0.874 0.020 44.757 0.000 0.874 0.874
regulasi 0.816 0.024 33.429 0.000 0.816 0.816
strategi ~~
regulasi 0.762 0.027 27.982 0.000 0.762 0.762
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.A1 0.353 0.032 11.133 0.000 0.353 0.549
.A2 0.182 0.020 9.132 0.000 0.182 0.270
.A3 0.192 0.022 8.905 0.000 0.192 0.253
.A4 0.190 0.021 9.171 0.000 0.190 0.268
.B1 0.270 0.032 8.454 0.000 0.270 0.339
.B2 0.078 0.025 3.155 0.002 0.078 0.125
.C1 0.123 0.014 8.573 0.000 0.123 0.205
.C2 0.104 0.014 7.494 0.000 0.104 0.169
.D1 0.093 0.011 8.748 0.000 0.093 0.151
.D2 0.063 0.008 7.481 0.000 0.063 0.114
.D3 0.182 0.017 10.624 0.000 0.182 0.270
.D4 0.200 0.018 11.218 0.000 0.200 0.365
.E1 0.145 0.014 10.565 0.000 0.145 0.211
.E2 0.114 0.011 10.392 0.000 0.114 0.195
.E3 0.157 0.014 10.844 0.000 0.157 0.242
.E4 0.092 0.010 9.490 0.000 0.092 0.141
.E5 0.132 0.013 10.456 0.000 0.132 0.201
.E6 0.197 0.018 11.222 0.000 0.197 0.304
.F1 0.140 0.014 9.700 0.000 0.140 0.190
.F2 0.090 0.011 8.219 0.000 0.090 0.130
.F3 0.140 0.015 9.538 0.000 0.140 0.181
.F4 0.233 0.021 10.912 0.000 0.233 0.306
faktor 1.000 1.000 1.000
permintaan 1.000 1.000 1.000
industri 1.000 1.000 1.000
strategi 1.000 1.000 1.000
regulasi 1.000 1.000 1.000
.kesempatan 1.000 0.206 0.206semPaths(sem.fit)
semPaths(sem.fit, "std",
color = list(lat = "green", man = "yellow"),
edge.color="black")
semPaths(sem.fit, "std",
color = list(lat = "green", man = "yellow"),
edge.color="black", fade=FALSE)
semPaths(sem.fit, "std",
color = list(lat = "green", man = "yellow"),
edge.color="black",
fade=FALSE, residuals=FALSE, exoCov=FALSE)
# source:https://rpubs.com/ifn1411/PLS
# install plspm
#install.packages("plspm")
# load plspm
library(plspm)# load data spainmodel
data(spainfoot)
# first 5 row of spainmodel data
head(spainfoot) GSH GSA SSH SSA GCH GCA CSH CSA WMH WMA LWR LRWL YC RC
Barcelona 61 44 0.95 0.95 14 21 0.47 0.32 14 13 10 22 76 6
RealMadrid 49 34 1.00 0.84 29 23 0.37 0.37 14 11 10 18 115 9
Sevilla 28 26 0.74 0.74 20 19 0.42 0.53 11 10 4 7 100 8
AtleMadrid 47 33 0.95 0.84 23 34 0.37 0.16 13 7 6 9 116 5
Villarreal 33 28 0.84 0.68 25 29 0.26 0.16 12 6 5 11 102 5
Valencia 47 21 1.00 0.68 26 28 0.26 0.26 12 6 5 8 120 6Attack <- c(0, 0, 0)
Defense <- c(1, 0, 0)
Success <- c(1, 0, 0)
model_path <- rbind(Attack, Defense, Success)
colnames(model_path) <- rownames(model_path)
model_path Attack Defense Success
Attack 0 0 0
Defense 1 0 0
Success 1 0 0# graph structural model
innerplot(model_path)
Attack <- c(0, 1, 0)
Defense <- c(0, 0, 0)
Success <- c(1, 1, 0)
model_path2 <- rbind(Attack, Defense, Success)
colnames(model_path2) <- rownames(model_path2)
model_path2 Attack Defense Success
Attack 0 1 0
Defense 0 0 0
Success 1 1 0# graph structural model
innerplot(model_path2, txt.col = "black")
# define latent variable associated with
model_blocks <- list(1:4, 5:8, 9:12)
# vector of modes (reflective)
model_modes <- c("A", "A", "A")
# run plspm analysis
model_pls <- plspm(Data = spainfoot, path_matrix = model_path, blocks = model_blocks, modes = model_modes)
model_plsPartial Least Squares Path Modeling (PLS-PM)
---------------------------------------------
NAME DESCRIPTION
1 $outer_model outer model
2 $inner_model inner model
3 $path_coefs path coefficients matrix
4 $scores latent variable scores
5 $crossloadings cross-loadings
6 $inner_summary summary inner model
7 $effects total effects
8 $unidim unidimensionality
9 $gof goodness-of-fit
10 $boot bootstrap results
11 $data data matrix
---------------------------------------------
You can also use the function 'summary' # Unidimensionality
model_pls$unidim Mode MVs C.alpha DG.rho eig.1st eig.2nd
Attack A 4 0.8905919 0.92456079 3.017160 0.7923055
Defense A 4 0.0000000 0.02601677 2.393442 1.1752781
Success A 4 0.9165491 0.94232868 3.217294 0.5370492plot(model_pls, what = "loadings")
# Loadings and Communilaties
model_pls$outer_model name block weight loading communality redundancy
1 GSH Attack 0.3474771 0.9412506 0.8859527 0.00000000
2 GSA Attack 0.2671782 0.8562398 0.7331465 0.00000000
3 SSH Attack 0.2922077 0.8466039 0.7167381 0.00000000
4 SSA Attack 0.2396012 0.8212987 0.6745316 0.00000000
5 GCH Defense -0.1198790 0.4762965 0.2268583 0.05071506
6 GCA Defense -0.4264164 0.8885714 0.7895590 0.17650898
7 CSH Defense 0.2949470 -0.7297095 0.5324759 0.11903706
8 CSA Defense 0.3898039 -0.8947452 0.8005689 0.17897028
9 WMH Success 0.2484276 0.7884562 0.6216632 0.49452090
10 WMA Success 0.2691511 0.8747163 0.7651285 0.60864477
11 LWR Success 0.2947322 0.9703409 0.9415614 0.74899365
12 LRWL Success 0.2998524 0.9428112 0.8888929 0.70709694# Crossloadings
model_pls$crossloadings name block Attack Defense Success
1 GSH Attack 0.9412506 -0.5139001 0.9019257
2 GSA Attack 0.8562398 -0.3403294 0.7483558
3 SSH Attack 0.8466039 -0.4124617 0.7781795
4 SSA Attack 0.8212987 -0.3455460 0.6308989
5 GCH Defense -0.1302683 0.4762965 -0.1620567
6 GCA Defense -0.4633220 0.8885714 -0.5640722
7 CSH Defense 0.3204993 -0.7297095 0.4850456
8 CSA Defense 0.4235465 -0.8947452 0.5811253
9 WMH Success 0.7126127 -0.4120502 0.7884562
10 WMA Success 0.7720228 -0.7147787 0.8747163
11 LWR Success 0.8454164 -0.5345709 0.9703409
12 LRWL Success 0.8600973 -0.5910943 0.9428112# Coefficient of Determination
model_pls$inner_model$Defense
Estimate Std. Error t value Pr(>|t|)
Intercept 5.504973e-17 0.2076918 2.650549e-16 1.00000000
Attack -4.728148e-01 0.2076918 -2.276521e+00 0.03526176
$Success
Estimate Std. Error t value Pr(>|t|)
Intercept 7.783183e-17 0.1065936 7.301735e-16 1.000000e+00
Attack 8.918971e-01 0.1065936 8.367266e+00 1.285711e-07# Redundancy
model_pls$inner_summary Type R2 Block_Communality Mean_Redundancy AVE
Attack Exogenous 0.0000000 0.7525922 0.0000000 0.7525922
Defense Endogenous 0.2235539 0.5873656 0.1313078 0.5873656
Success Endogenous 0.7954804 0.8043115 0.6398141 0.8043115# Goodness-of-fit
model_pls$gof[1] 0.6034738plot(model_pls, what = "inner", colpos = "#6890c4BB", colneg = "#f9675dBB", txt.col = "black", arr.tcol="black")