assess_cvpat {seminrExtras} | R Documentation |
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 crossâvalidated 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)