PLSPM.Basic_Prime/Run_Example_PLSPM_Basic.m at main · PsycheMatrica/PLSPM.Basic_Prime · GitHub

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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Illustration for PLSPM.Basic_Prime package                              %
%   Author: Gyeongcheol Cho & Heungsun Hwang &                            %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Description:                                                            %
%   - This code aims to illustrate how to use PLSPM.Basic_Prime package.  %
%   - The dataset is a replica of the ACSI data used in Cho & Hwang       %
%     (2024).                                                             %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% References                                                              %
%     * Cho, G., Hwang, H. (2024) Generalized Structured Component        %
%         Analysis Accommodating Convex Components: A Knowledge-Based     %
%         Multivariate Method with Interpretable Composite Indexes.       %
%         Psychometrika 89, 241–266.                                      %
%         https://doi.org/10.1007/s11336-023-09944-3                      %
%     * Hwang, H., Takane, Y. & Tenenhaus, A. (2015) An Alternative       %
%         Estimation Procedure for Partial Least Squares Path Modeling.   %
%         Behaviormetrika 42, 63–78. https://doi.org/10.2333/bhmk.42.63   %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

help BasicPLSPM()

Data=readtable('ACSI_774_Replica.csv');
W0=[1 1 1 0 0 0 0 0 0 0 0 0 0 0 ; ...
   0 0 0 1 1 1 0 0 0 0 0 0 0 0 ; ...
   0 0 0 0 0 0 1 1 0 0 0 0 0 0 ; ...
   0 0 0 0 0 0 0 0 1 1 1 0 0 0 ; ...
   0 0 0 0 0 0 0 0 0 0 0 1 0 0 ; ...
   0 0 0 0 0 0 0 0 0 0 0 0 1 1 ]';
C0=zeros(6,14);%W0';
B0=[0 1 1 1 0 0;...
    0 0 1 1 0 0;...
    0 0 0 1 0 0;...
    0 0 0 0 1 1;...
    0 0 0 0 0 1;...
    0 0 0 0 0 0];
N_Boot=1000;
Max_iter = 1000;
Min_limit = 10^(-6);
Flag_Parallel = false;
modetype=ones(1,6); % 1 = mode A, 2 = mode B
scheme=3; %  1 = centroid, 2 = factorial, 3 = path weighting
correct_type=ones(1,6); %
Opt_Missing=0; % 0 = None, 1 = List-wise deletion, 2 = mean imputation, 3 = pairwise correlation
ind_sign = [1,4,7,9,12,13];
Results=PLSPM_Basic(Data{:,:}, W0, B0, modetype,scheme,correct_type,ind_sign,N_Boot,Max_iter,Min_limit,Flag_Parallel,Opt_Missing);
INI=Results.INI;
TABLE=Results.TABLE;
ETC=Results.ETC;
INI
INI.Converge
INI.iter
INI.W
INI.C
INI.B
INI.CVscore
TABLE
TABLE.W
TABLE.C
TABLE.B
ETC