Help for package seminrExtras

Type:

Package

Title:

Conduct Additional Modeling and Analysis for 'seminr'

Version:

0.1.0

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 (≥ 2.3.0), stats

License:

GPL-3

Encoding:

UTF-8

Suggests:

testthat (≥ 3.0.0), knitr, rmarkdown

Config/testthat/edition:

URL:

https://github.com/sem-in-r/seminr

BugReports:

https://github.com/sem-in-r/seminr/issues

RoxygenNote:

7.3.2

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2025-07-21 12:16:30 UTC; nicholasdanks

Author:

Soumya Ray [aut, ths], Nicholas Patrick Danks [aut, cre]

Maintainer:

Nicholas Patrick Danks <nicholasdanks@hotmail.com>

Repository:

CRAN

Date/Publication:

2025-07-22 11:01:27 UTC

SEMinR function to compare CV-PAT loss of two models

Description

'assess_cvpat' conducts a single model CV-PAT assessment against item average and linear model 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 CV-PAT 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

Mumber 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)

# 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)