assess_cvpat_compare {seminrExtras} | R Documentation |
SEMinR function to compare CV-PAT loss of two models
Description
'assess_cvpat_compare' conducts a CV-PAT 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 CV-PAT comparison. |
alternative_model |
The alternate 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)