The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

seminrExtras

S. Ray and N.P. Danks

2025-07-21

SEMinR (Ray & Danks) is a domaign specific language for modeling and estimating structural equation models. This package serves to provide additional extra methods and functions that can be used to analyze PLS-SEM models.

New features implemented with this package CVPAT - assess_cvpat() - assess_cvpat_compare()

The Example model: Corporate Reputation

We are applying our CV-PAT to the corporate reputation example bundled with SEMinR.

plot(established_model)

plot(competing_model)

Example


# Create measurement model ----
corp_rep_mm_ext <- constructs(
  composite("QUAL", multi_items("qual_", 1:8), weights = mode_B),
  composite("PERF", multi_items("perf_", 1:5), weights = mode_B),
  composite("CSOR", multi_items("csor_", 1:5), weights = mode_B),
  composite("ATTR", multi_items("attr_", 1:3), weights = mode_B),
  composite("COMP", multi_items("comp_", 1:3)),
  composite("LIKE", multi_items("like_", 1:3)),
  composite("CUSA", single_item("cusa")),
  composite("CUSL", multi_items("cusl_", 1:3))
)

alt_mm <- constructs(
  composite("QUAL", multi_items("qual_", 1:8), weights = mode_B),
  composite("PERF", multi_items("perf_", 1:5), weights = mode_B),
  composite("CSOR", multi_items("csor_", 1:5), weights = mode_B),
  composite("ATTR", multi_items("attr_", 1:3), weights = mode_B),
  composite("COMP", multi_items("comp_", 1:3)),
  composite("LIKE", multi_items("like_", 1:3)),
  composite("CUSA", single_item("cusa")),
  composite("CUSL", multi_items("cusl_", 1:3))
)

# Create structural model ----
corp_rep_sm_ext <- relationships(
  paths(from = c("QUAL", "PERF", "CSOR", "ATTR"), to = c("COMP", "LIKE")),
  paths(from = c("COMP", "LIKE"), to = c("CUSA", "CUSL")),
  paths(from = c("CUSA"),         to = c("CUSL"))
)
alt_sm <- relationships(
  paths(from = c("QUAL", "PERF", "CSOR", "ATTR"), to = c("COMP", "LIKE")),
  paths(from = c("COMP", "LIKE"), to = c("CUSA")),
  paths(from = c("CUSA"),         to = c("CUSL"))
)


# Estimate the models ----
established_model <- estimate_pls(
  data = corp_rep_data,
  measurement_model = corp_rep_mm_ext,
  structural_model  = corp_rep_sm_ext,
  missing = mean_replacement,
  missing_value = "-99")

competing_model <- estimate_pls(
  data = corp_rep_data,
  measurement_model = alt_mm,
  structural_model  = alt_sm,
  missing = mean_replacement,
  missing_value = "-99")

# Function to compare the Loss of two models
compare_results <- assess_cvpat_compare(established_model = established_model,
                                        alternative_model = competing_model,
                                        testtype = "two.sided",
                                        nboot = 2000,
                                        technique = predict_DA,
                                        seed = 123,
                                        noFolds = 10,
                                        reps = 10,
                                        cores = 1)


print(compare_results,
      digits = 3)

# Assess the base model ----
assess_results <- assess_cvpat(established_model,
                               seed = 123, 
                               cores = 1)
print(assess_results$CVPAT_compare_LM,
      digits = 3)
print(assess_results$CVPAT_compare_IA,
      digits = 3)

Now we compare the results:

# Function to compare the Loss of two models
compare_results <- assess_cvpat_compare(established_model = established_model,
                                        alternative_model = competing_model,
                                        testtype = "two.sided",
                                        nboot = 2000,
                                        technique = predict_DA,
                                        seed = 123,
                                        noFolds = 10,
                                        reps = 10,
                                        cores = 1)

print(compare_results, 
      cores = 1,
      digits = 3)
#>         Base Model Loss Alt Model Loss   Diff Boot T value Boot P Value
#> COMP              1.198          1.195  0.003       -0.460        0.645
#> LIKE              1.923          1.933 -0.010        0.883        0.378
#> CUSA              0.988          0.992 -0.004        0.809        0.419
#> CUSL              1.562          1.715 -0.152        3.286        0.001
#> Overall           1.418          1.459 -0.041        3.293        0.001
#> 
#> CV-PAT as per Sharma et al. (2023).
#>   Both models under comparison have identical endoogenous constructs with identical measurement models.
#>   Purely exogenous constructs can be differ in regards to their relationships with both nomological
#>   partners and measurement indicators.

# Assess the base model ----
assess_results <- assess_cvpat(established_model,
                               seed = 123, 
                               cores = 1)
print(assess_results$CVPAT_compare_LM,
      digits = 3)
#>         PLS Loss LM Loss   Diff Boot T value Boot P Value
#> COMP       1.196   1.238 -0.042        1.308        0.192
#> LIKE       1.915   1.929 -0.014        0.247        0.805
#> CUSA       0.994   0.774  0.220       -2.982        0.003
#> CUSL       1.560   1.713 -0.153        3.838        0.000
#> Overall    1.416   1.414  0.003       -0.104        0.918
#> 
#> CV-PAT as per Sharma et al. (2023).
print(assess_results$CVPAT_compare_IA,
      digits = 3)
#>         PLS Loss IA Loss   Diff Boot T value Boot P Value
#> COMP       1.196   2.023 -0.827        8.580        0.000
#> LIKE       1.915   3.103 -1.187        8.293        0.000
#> CUSA       0.994   1.374 -0.379        5.004        0.000
#> CUSL       1.560   2.663 -1.102        7.572        0.000
#> Overall    1.416   2.290 -0.874       10.301        0.000
#> 
#> CV-PAT as per Sharma et al. (2023).

References

Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2022). Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT. European journal of marketing, 57(6), 1662-1677.
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: coveted,yet forsaken? Introducing a cross‐validated predictive ability test in partial least squares path modeling. Decision Sciences, 52(2), 362-392.
Ray, S., Danks, N.P., Calero Valdez, A. (2024) SEMinR: Domain-specific language for building, estimating, and visualizing structural equation models in R.

Also a quote using >:

“He who gives up [code] safety for [code] speed deserves neither.” (via)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.