Help for package seminrExtras

Type:

Package

Title:

Conduct Additional Modeling and Analysis for 'seminr'

Version:

0.2.0

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:

2025-09-03 08:40:16 UTC; nicholasdanks

Author:

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

Maintainer:

Nicholas Patrick Danks <nicholasdanks@hotmail.com>

Repository:

CRAN

Date/Publication:

2025-09-03 14:00:02 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)

# 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)