Type: Package
Title: Conduct Additional Modeling and Analysis for 'seminr'
Version: 0.9.0
Description: Supplemental functions for estimating and analysing structural equation models including Cross Validated Prediction and Testing (CVPAT, Liengaard et al., 2021 <doi:10.1111/deci.12445>).
Imports: seminr (≥ 2.4.0), stats
License: GPL-3
Encoding: UTF-8
Suggests: testthat (≥ 3.0.0), knitr, rmarkdown, psych, learnr, paran
Config/testthat/edition: 3
URL: https://github.com/sem-in-r/seminr
BugReports: https://github.com/sem-in-r/seminr/issues
RoxygenNote: 7.3.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-01-29 13:34:48 UTC; nicholasdanks
Author: Nicholas Patrick Danks [aut, cre], Soumya Ray [aut, ths]
Maintainer: Nicholas Patrick Danks <nicholasdanks@hotmail.com>
Repository: CRAN
Date/Publication: 2026-01-29 14:30:02 UTC

SEMinR function to compare CVPAT loss of two models

Description

'assess_cvpat' conducts a single model CVPAT assessment against item average and linear model prediction benchmarks.

Usage

assess_cvpat(
  seminr_model,
  testtype = "two.sided",
  nboot = 2000,
  seed = 123,
  technique = predict_DA,
  noFolds = NULL,
  reps = NULL,
  cores = NULL
)

Arguments

seminr_model

The SEMinR model for CVPAT analysis

testtype

Either "two.sided" (default) or "greater".

nboot

The number of bootstrap subsamples to execute (defaults to 2000).

seed

The seed for reproducibility (defaults to 123).

technique

predict_EA or predict_DA (default).

noFolds

Number of folds for k-fold cross validation.

reps

Number of repetitions for cross validation.

cores

Number of cores for parallelization.

Value

A matrix of the estimated loss and results of significance testing.

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.

Examples

# Load libraries
library(seminr)
library(seminrExtras)

# 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))
)

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

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

# Assess the base model ----
assess_cvpat(seminr_model = corp_rep_pls_model_ext,
             testtype = "two.sided",
             nboot = 20,
             seed = 123,
             technique = predict_DA,
             noFolds = 5,
             reps = 1,
             cores = 1)

SEMinR function to compare CVPAT loss of two models

Description

'assess_cvpat_compare' conducts a CVPAT significance test of loss between two models.

Usage

assess_cvpat_compare(
  established_model,
  alternative_model,
  testtype = "two.sided",
  nboot = 2000,
  seed = 123,
  technique = predict_DA,
  noFolds = NULL,
  reps = NULL,
  cores = NULL
)

Arguments

established_model

The base seminr model for CVPAT comparison.

alternative_model

The alternate seminr model for CVPAT comparison.

testtype

Either "two.sided" (default) or "greater".

nboot

The number of bootstrap subsamples to execute (defaults to 2000).

seed

The seed for reproducibility (defaults to 123).

technique

predict_EA or predict_DA (default).

noFolds

Number of folds for k-fold cross validation.

reps

Number of repetitions for cross validation.

cores

Number of cores for parallelization.

Value

A matrix of the estimated loss and results of significance testing.

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.

Examples

# Load libraries
library(seminr)
library(seminrExtras)

# 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))
)

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("COMP", multi_items("comp_", 1:3)),
 composite("LIKE", multi_items("like_", 1:3))
)

# Create structural model ----

corp_rep_sm_ext <- relationships(
 paths(from = c("QUAL", "PERF", "CSOR", "ATTR"), to = c("COMP", "LIKE"))
)
alt_sm <- relationships(
 paths(from = c("QUAL", "PERF", "CSOR"), to = c("COMP", "LIKE"))
)

# Estimate the model ----
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")

alternative_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
assess_cvpat_compare(established_model,
                    alternative_model ,
                    testtype = "two.sided",
                    nboot = 20,
                    seed = 123,
                    technique = predict_DA,
                    noFolds = 5,
                    reps = 1,
                    cores = 1)

SEMinR function to bootstrap calculate the congruence coefficient

Description

'congruence_test' conducts a single bootstrapped congruence test.

Usage

congruence_test(
  seminr_model,
  nboot = 2000,
  seed = 123,
  alpha = 0.05,
  threshold = 1
)

Arguments

seminr_model

The SEMinR model for CVPAT analysis

nboot

The number of bootstrap subsamples to execute (defaults to 2000).

seed

The seed for reproducibility (defaults to 123).

alpha

The required level of alpha for statistical testing (defaults to 0.05).

threshold

The threshold with which to compare significance testing H0: rc < 1 (defaults to 1).

Value

A matrix of the estimated congruence coefficient and results of significance testing.

References

Franke, G. R., Sarstedt, M., & Danks, N. P. (2021). Assessing measure congruence in nomological networks. Journal of Business Research, 130, 318-334.

Examples

# Load libraries
library(seminr)
library(seminrExtras)

# Create measurement model ----
corp_rep_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))
)

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

# Estimate the model ----
corp_rep_pls_model <- estimate_pls(
  data = corp_rep_data,
  measurement_model = corp_rep_mm,
  structural_model  = corp_rep_sm,
  missing = mean_replacement,
 missing_value = "-99")

# Assess the base model ----
congruence_test(seminr_model = corp_rep_pls_model,
                nboot = 20,
                seed = 123,
                alpha = 0.05,
                threshold = 1)