# load packages
library(cSEM)
##
## Attaching package: 'cSEM'
## The following object is masked from 'package:stats':
##
## predict
library(DiagrammeR)
library(DiagrammeRsvg)
library(rsvg)
## Linking to librsvg 2.56.3
# Load external function files
source("cSEM/plot_csem.R")
model_Bergami="
# Measurement models
OrgPres <~ cei1 + cei2 + cei3 + cei4 + cei5 + cei6 + cei7 + cei8
OrgIden =~ ma1 + ma2 + ma3 + ma4 + ma5 + ma6
AffJoy =~ orgcmt1 + orgcmt2 + orgcmt3 + orgcmt7
AffLove =~ orgcmt5 + orgcmt6 + orgcmt8
Gender<~ gender
# Structural model
OrgIden ~ OrgPres
AffLove ~ OrgPres+OrgIden+Gender
AffJoy ~ OrgPres+OrgIden+Gender"
csem_results <- csem(.data = BergamiBagozzi2000,.model = model_Bergami,
.disattenuate = T,
.PLS_weight_scheme_inner = 'factorial',
.tolerance = 1e-5,
.resample_method = 'bootstrap',.R = 499)
plot_csem(csem_results,
title = "SEM Diagram")
plot_csem(csem_results,
title = "SEM Diagram",
graph_attrs = "rankdir=LR")
plot_csem(csem_results,
title = "SEM Diagram with No Colors",
remove_colors = TRUE)
plot_csem(csem_results,
title = "SEM Structural Model Diagram",
show_structural_model_only = TRUE,
graph_attrs = "rankdir=LR")
plot_csem(csem_results,
title = "SEM Structural Model Diagram",
show_structural_model_only = TRUE,
graph_attrs = "rankdir=LR",
remove_colors = TRUE)
plot_csem(csem_results,
title = "SEM Diagram",
edge_lengths = 2,
graph_attrs = "layout = fdp")
plot_csem(csem_results,
title = "SEM Diagram",
graph_attrs = "layout = circo") #options: dot, circo, fdp, twopi, neato, osage, patchwork
plot_csem(csem_results,
title = "SEM Diagram",
graph_attrs = "rankdir=LR; splines=polyline") # Or "graph_attrs = c("rankdir=LR", "splines=line")"
# Define ranks for nodes
node_ranks = list(
rank1 = c("OrgPres", "Gender"),
rank2 = c("OrgIden"),
rank3 = c("AffLove", "AffJoy")
)
graph_attrs = c("rankdir=LR", "ranksep=0.5", "nodesep=0.3")
plot_csem(csem_results,
title = "SEM Structural Model Diagram",
node_ranks = node_ranks,
show_structural_model_only = TRUE,
graph_attrs = "rankdir=LR")
plot_csem(csem_results,
title = "SEM Diagram",
graph_attrs = graph_attrs)
plot_csem(csem_results,
title = "SEM Diagram",
node_ranks = node_ranks,
graph_attrs = "rankdir=LR")
plot_csem(csem_results,
title = "SEM Diagram",
node_ranks = node_ranks,
graph_attrs = "rankdir=BT")
# With correlation_lines:
plot_csem(csem_results,
title = "SEM Diagram with all information",
run_correlation_lines = TRUE)
# Save the diagram as a PDF file
plot_csem_save("SEM_Custom_Diagram.pdf")
## Plot saved to SEM_Custom_Diagram.pdf
summarize(csem_results)
## ________________________________________________________________________________
## ----------------------------------- Overview -----------------------------------
##
## General information:
## ------------------------
## Estimation status = Ok
## Number of observations = 305
## Weight estimator = PLS-PM
## Inner weighting scheme = "factorial"
## Type of indicator correlation = Pearson
## Path model estimator = OLS
## Second-order approach = NA
## Type of path model = Linear
## Disattenuated = Yes (PLSc)
##
## Resample information:
## ---------------------
## Resample method = "bootstrap"
## Number of resamples = 499
## Number of admissible results = 497
## Approach to handle inadmissibles = "drop"
## Sign change option = "none"
## Random seed = -320380732
##
## Construct details:
## ------------------
## Name Modeled as Order Mode
##
## OrgPres Composite First order "modeB"
## Gender Composite First order "modeB"
## OrgIden Common factor First order "modeA"
## AffLove Common factor First order "modeA"
## AffJoy Common factor First order "modeA"
##
## ----------------------------------- Estimates ----------------------------------
##
## Estimated path coefficients:
## ============================
## CI_percentile
## Path Estimate Std. error t-stat. p-value 95%
## OrgIden ~ OrgPres 0.3913 0.0611 6.4062 0.0000 [ 0.2895; 0.5318 ]
## AffLove ~ OrgPres -0.1196 0.0958 -1.2487 0.2118 [-0.3135; 0.0684 ]
## AffLove ~ Gender 0.1179 0.0624 1.8903 0.0587 [-0.0013; 0.2332 ]
## AffLove ~ OrgIden -0.4836 0.0795 -6.0860 0.0000 [-0.6207;-0.3335 ]
## AffJoy ~ OrgPres 0.1926 0.0688 2.7996 0.0051 [ 0.0570; 0.3244 ]
## AffJoy ~ Gender -0.1475 0.0475 -3.1036 0.0019 [-0.2321;-0.0543 ]
## AffJoy ~ OrgIden 0.6779 0.0597 11.3610 0.0000 [ 0.5537; 0.7981 ]
##
## Estimated loadings:
## ===================
## CI_percentile
## Loading Estimate Std. error t-stat. p-value 95%
## OrgPres =~ cei1 0.7015 0.0870 8.0675 0.0000 [ 0.4944; 0.8231 ]
## OrgPres =~ cei2 0.7482 0.0881 8.4915 0.0000 [ 0.5042; 0.8586 ]
## OrgPres =~ cei3 0.7957 0.0881 9.0288 0.0000 [ 0.5550; 0.8942 ]
## OrgPres =~ cei4 0.8029 0.0909 8.8367 0.0000 [ 0.5562; 0.9114 ]
## OrgPres =~ cei5 0.8073 0.0815 9.9040 0.0000 [ 0.5981; 0.9115 ]
## OrgPres =~ cei6 0.8958 0.0759 11.8087 0.0000 [ 0.6687; 0.9475 ]
## OrgPres =~ cei7 0.7644 0.0855 8.9451 0.0000 [ 0.5255; 0.8622 ]
## OrgPres =~ cei8 0.7029 0.1015 6.9275 0.0000 [ 0.4436; 0.8292 ]
## Gender =~ gender 1.0000 NA NA NA [ NA; NA ]
## OrgIden =~ ma1 0.6858 0.0511 13.4243 0.0000 [ 0.5825; 0.7734 ]
## OrgIden =~ ma2 0.6735 0.0567 11.8758 0.0000 [ 0.5511; 0.7708 ]
## OrgIden =~ ma3 0.6194 0.0608 10.1903 0.0000 [ 0.4944; 0.7403 ]
## OrgIden =~ ma4 0.8233 0.0439 18.7697 0.0000 [ 0.7277; 0.8970 ]
## OrgIden =~ ma5 0.7817 0.0520 15.0423 0.0000 [ 0.6739; 0.8777 ]
## OrgIden =~ ma6 0.6326 0.0603 10.4871 0.0000 [ 0.5244; 0.7459 ]
## AffLove =~ orgcmt5 0.6262 0.0840 7.4561 0.0000 [ 0.4529; 0.7782 ]
## AffLove =~ orgcmt6 0.3666 0.1110 3.3010 0.0010 [ 0.1212; 0.5594 ]
## AffLove =~ orgcmt8 0.8053 0.0761 10.5815 0.0000 [ 0.6419; 0.9435 ]
## AffJoy =~ orgcmt1 0.6287 0.0580 10.8386 0.0000 [ 0.5050; 0.7267 ]
## AffJoy =~ orgcmt2 0.6829 0.0505 13.5135 0.0000 [ 0.5811; 0.7836 ]
## AffJoy =~ orgcmt3 0.7228 0.0495 14.5912 0.0000 [ 0.6311; 0.8167 ]
## AffJoy =~ orgcmt7 0.6540 0.0551 11.8665 0.0000 [ 0.5387; 0.7534 ]
##
## Estimated weights:
## ==================
## CI_percentile
## Weight Estimate Std. error t-stat. p-value 95%
## OrgPres <~ cei1 0.0522 0.1869 0.2795 0.7798 [-0.3571; 0.3901 ]
## OrgPres <~ cei2 -0.0383 0.1954 -0.1959 0.8447 [-0.4417; 0.3250 ]
## OrgPres <~ cei3 0.3137 0.1920 1.6336 0.1023 [-0.0748; 0.6956 ]
## OrgPres <~ cei4 0.1782 0.2348 0.7588 0.4480 [-0.3384; 0.5792 ]
## OrgPres <~ cei5 0.2341 0.2519 0.9292 0.3528 [-0.2046; 0.7624 ]
## OrgPres <~ cei6 0.3769 0.2554 1.4757 0.1400 [-0.1470; 0.8084 ]
## OrgPres <~ cei7 0.0983 0.2177 0.4513 0.6517 [-0.3606; 0.4831 ]
## OrgPres <~ cei8 -0.0032 0.1967 -0.0164 0.9869 [-0.3743; 0.4021 ]
## Gender <~ gender 1.0000 NA NA NA [ NA; NA ]
## OrgIden <~ ma1 0.2126 0.0147 14.4459 0.0000 [ 0.1855; 0.2420 ]
## OrgIden <~ ma2 0.2087 0.0162 12.8973 0.0000 [ 0.1754; 0.2401 ]
## OrgIden <~ ma3 0.1920 0.0190 10.1191 0.0000 [ 0.1546; 0.2275 ]
## OrgIden <~ ma4 0.2552 0.0162 15.7385 0.0000 [ 0.2229; 0.2861 ]
## OrgIden <~ ma5 0.2423 0.0156 15.5261 0.0000 [ 0.2112; 0.2721 ]
## OrgIden <~ ma6 0.1961 0.0161 12.1479 0.0000 [ 0.1661; 0.2279 ]
## AffLove <~ orgcmt5 0.4469 0.0557 8.0282 0.0000 [ 0.3450; 0.5572 ]
## AffLove <~ orgcmt6 0.2616 0.0725 3.6098 0.0003 [ 0.0978; 0.3939 ]
## AffLove <~ orgcmt8 0.5747 0.0631 9.1081 0.0000 [ 0.4515; 0.7098 ]
## AffJoy <~ orgcmt1 0.3045 0.0244 12.4739 0.0000 [ 0.2530; 0.3494 ]
## AffJoy <~ orgcmt2 0.3307 0.0224 14.7880 0.0000 [ 0.2917; 0.3759 ]
## AffJoy <~ orgcmt3 0.3500 0.0228 15.3315 0.0000 [ 0.3088; 0.3972 ]
## AffJoy <~ orgcmt7 0.3167 0.0275 11.5274 0.0000 [ 0.2641; 0.3684 ]
##
## Estimated construct correlations:
## =================================
## CI_percentile
## Correlation Estimate Std. error t-stat. p-value 95%
## OrgPres ~~ Gender 0.0513 0.0587 0.8738 0.3823 [-0.0725; 0.1581 ]
##
## Estimated indicator correlations:
## =================================
## CI_percentile
## Correlation Estimate Std. error t-stat. p-value 95%
## cei1 ~~ cei2 0.6437 0.0395 16.3154 0.0000 [ 0.5631; 0.7101 ]
## cei1 ~~ cei3 0.4746 0.0482 9.8493 0.0000 [ 0.3773; 0.5540 ]
## cei1 ~~ cei4 0.6404 0.0465 13.7758 0.0000 [ 0.5467; 0.7248 ]
## cei1 ~~ cei5 0.7131 0.0500 14.2755 0.0000 [ 0.6077; 0.7937 ]
## cei1 ~~ cei6 0.5402 0.0547 9.8812 0.0000 [ 0.4254; 0.6422 ]
## cei1 ~~ cei7 0.4307 0.0542 7.9461 0.0000 [ 0.3222; 0.5208 ]
## cei1 ~~ cei8 0.5740 0.0637 9.0098 0.0000 [ 0.4394; 0.6801 ]
## cei2 ~~ cei3 0.7109 0.0324 21.9701 0.0000 [ 0.6407; 0.7658 ]
## cei2 ~~ cei4 0.5871 0.0503 11.6831 0.0000 [ 0.4869; 0.6784 ]
## cei2 ~~ cei5 0.5371 0.0481 11.1587 0.0000 [ 0.4357; 0.6277 ]
## cei2 ~~ cei6 0.6558 0.0441 14.8644 0.0000 [ 0.5566; 0.7341 ]
## cei2 ~~ cei7 0.5527 0.0412 13.4019 0.0000 [ 0.4643; 0.6238 ]
## cei2 ~~ cei8 0.5841 0.0545 10.7258 0.0000 [ 0.4717; 0.6856 ]
## cei3 ~~ cei4 0.5454 0.0523 10.4252 0.0000 [ 0.4285; 0.6449 ]
## cei3 ~~ cei5 0.4234 0.0551 7.6781 0.0000 [ 0.3065; 0.5203 ]
## cei3 ~~ cei6 0.5984 0.0528 11.3274 0.0000 [ 0.4916; 0.6921 ]
## cei3 ~~ cei7 0.6544 0.0376 17.4121 0.0000 [ 0.5772; 0.7255 ]
## cei3 ~~ cei8 0.5174 0.0486 10.6396 0.0000 [ 0.4225; 0.6139 ]
## cei4 ~~ cei5 0.7418 0.0405 18.3243 0.0000 [ 0.6622; 0.8178 ]
## cei4 ~~ cei6 0.5992 0.0496 12.0777 0.0000 [ 0.5002; 0.6858 ]
## cei4 ~~ cei7 0.4587 0.0522 8.7884 0.0000 [ 0.3541; 0.5616 ]
## cei4 ~~ cei8 0.5866 0.0638 9.1892 0.0000 [ 0.4495; 0.7133 ]
## cei5 ~~ cei6 0.6547 0.0434 15.0814 0.0000 [ 0.5571; 0.7281 ]
## cei5 ~~ cei7 0.4752 0.0540 8.7932 0.0000 [ 0.3731; 0.5726 ]
## cei5 ~~ cei8 0.5837 0.0690 8.4565 0.0000 [ 0.4423; 0.7085 ]
## cei6 ~~ cei7 0.7132 0.0427 16.7103 0.0000 [ 0.6260; 0.7939 ]
## cei6 ~~ cei8 0.6031 0.0587 10.2739 0.0000 [ 0.4825; 0.7088 ]
## cei7 ~~ cei8 0.6902 0.0371 18.5922 0.0000 [ 0.6174; 0.7550 ]
##
## ------------------------------------ Effects -----------------------------------
##
## Estimated total effects:
## ========================
## CI_percentile
## Total effect Estimate Std. error t-stat. p-value 95%
## OrgIden ~ OrgPres 0.3913 0.0611 6.4062 0.0000 [ 0.2895; 0.5318 ]
## AffLove ~ OrgPres -0.3089 0.0778 -3.9728 0.0001 [-0.4881;-0.1737 ]
## AffLove ~ Gender 0.1179 0.0624 1.8903 0.0587 [-0.0013; 0.2332 ]
## AffLove ~ OrgIden -0.4836 0.0795 -6.0860 0.0000 [-0.6207;-0.3335 ]
## AffJoy ~ OrgPres 0.4578 0.0571 8.0189 0.0000 [ 0.3547; 0.5820 ]
## AffJoy ~ Gender -0.1475 0.0475 -3.1036 0.0019 [-0.2321;-0.0543 ]
## AffJoy ~ OrgIden 0.6779 0.0597 11.3610 0.0000 [ 0.5537; 0.7981 ]
##
## Estimated indirect effects:
## ===========================
## CI_percentile
## Indirect effect Estimate Std. error t-stat. p-value 95%
## AffLove ~ OrgPres -0.1893 0.0454 -4.1642 0.0000 [-0.2976;-0.1198 ]
## AffJoy ~ OrgPres 0.2652 0.0499 5.3206 0.0000 [ 0.1903; 0.3759 ]
## ________________________________________________________________________________