GSCA.Basic_Prime

Author:

Gyeongcheol Cho

Description:

• The GSCA.Basic_Prime package enables users to estimate and evaluate basic GSCA models.

Features:

• Estimate GSCA model parameters and calculate their standard errors (SE) along with 95% confidence intervals (CI).
• Assess model performance based on both explanatory and predictive power.
• Handle missing values in the data.
• Compute the PET (Predictor Exclusion Threshold) statistic to evaluate the predictive power of individual predictor components.
• Enable parallel computing for bootstrap sampling.
• Allow users to determine sign-fixing indicators for components.

Installation:

To use this package in MATLAB:

1. Clone or download the repository:

   git clone https://github.com/PsycheMatrica/GSCA.Basic_Prime.git

2. Add the package to your MATLAB path:

    addpath(genpath('GSCA.Basic_Prime'))

Usage:

• For examples on how to use the package, refer to the Run_Example_BasicGSCA.m file. This file demonstrates the implementation of BasicGSCA() using the ACSI dataset.

Compatibility:

• Tested on MATLAB R2023b.
• Likely compatible with earlier MATLAB versions.

Citation (APA):

• If you use GSCA.Basic_Prime in your research or publications, please cite it in APA format as follows:

Cho, G. (2024). GSCA.Basic_Prime: A package for basic generalized structured component analysis [Computer software]. GitHub. https://github.com/PsycheMatrica/GSCA.Basic_Prime