You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: CBIG_ASDf_CCA_factorBehavior.m
    path of file: ./repos/CBIG/stable_projects/disorder_subtypes/Tang2020_ASDFactors/step3_analyses/behavioralAssociation
    
    the code of the file until where you have to start completion: function [A, strucCorr_score, B, U, V, R, Rp, pVal, Ncca] = CBIG_ASDf_CCA_factorBehavior(ind_scores,...
    id_keep, reg, PAPset, path_subInfo, path_behav_scores, path_factorLoading, factor_order,...
    factor_idx)
% [A, strucCorr_score, B, U, V, R, Rp, pVal, Ncca] =
% CBIG_ASDf_CCA_factorBehavior(ind_scores, id_keep, reg, PAPset, path_subInfo,
% path_behav_scores, path_factorLoading, factor_order, factor_idx)
%
% This function performs CCA between factor loading and behavioral scores
% with permutation test (within site permutation).
%
% Input:
%      - ind_scores:
%           1xQ vector, where Q is the number of behavioral scores. Column
%           indices of behavioral scores in the .csv file specified by
%           path_behav_scores
%      - id_keep:
%           Nx1 cell array. IDs of subjects of interest
%      - reg:
%           NxV matrix, where V is the number of regressors
%      - PAPset:
%           N x Nperm matrix, where Nperm is the number of permutations.
%           Permutation set generated using PALM package.
%           The 1st column should be the original order (non-permuted).
%      - path_subInfo:
%           .csv file containing all subjects' demographic information, e.g., diagnosis, age, sex etc.
%      - path_behav_scores:
%           .csv file containing all behavioral scores
%      - path_factorLoading:
%           .txt file containing all ASD subjects' factor compositions
%      - factor_order:
%           Order of the factors. E.g., [1 3 2] means that the factors will
%           be re-ordered so that the 1st factor indicates factor 1, 3rd
%           factor indicates factor 2, and 2nd factor indicates factor 3.
%      - factor_idx:
%           Index of the factor
%
% Output:
%      - A:
%           Qx1 matrix, where Q is the number of behavioral scores. This is
%           the canonical coefficients for behavioral scores
%      - strucCorr_score:
%           Qx1 matrix, where Q is the number of behavioral scores. This is
%           the structure coefficients for behavioral scores. i.e., the
%           Pearson's correlation between canonical coefficients A and the

    
function [A, strucCorr_score, B, U, V, R, Rp, pVal, Ncca] = CBIG_ASDf_CCA_factorBehavior(ind_scores,...
    id_keep, reg, PAPset, path_subInfo, path_behav_scores, path_factorLoading, factor_order,...
    factor_idx)
% [A, strucCorr_score, B, U, V, R, Rp, pVal, Ncca] =
% CBIG_ASDf_CCA_factorBehavior(ind_scores, id_keep, reg, PAPset, path_subInfo,
% path_behav_scores, path_factorLoading, factor_order, factor_idx)
%
% This function performs CCA between factor loading and behavioral scores
% with permutation test (within site permutation).
%
% Input:
%      - ind_scores:
%           1xQ vector, where Q is the number of behavioral scores. Column
%           indices of behavioral scores in the .csv file specified by
%           path_behav_scores
%      - id_keep:
%           Nx1 cell array. IDs of subjects of interest
%      - reg:
%           NxV matrix, where V is the number of regressors
%      - PAPset:
%           N x Nperm matrix, where Nperm is the number of permutations.
%           Permutation set generated using PALM package.
%           The 1st column should be the original order (non-permuted).
%      - path_subInfo:
%           .csv file containing all subjects' demographic information, e.g., diagnosis, age, sex etc.
%      - path_behav_scores:
%           .csv file containing all behavioral scores
%      - path_factorLoading:
%           .txt file containing all ASD subjects' factor compositions
%      - factor_order:
%           Order of the factors. E.g., [1 3 2] means that the factors will
%           be re-ordered so that the 1st factor indicates factor 1, 3rd
%           factor indicates factor 2, and 2nd factor indicates factor 3.
%      - factor_idx:
%           Index of the factor
%
% Output:
%      - A:
%           Qx1 matrix, where Q is the number of behavioral scores. This is
%           the canonical coefficients for behavioral scores
%      - strucCorr_score:
%           Qx1 matrix, where Q is the number of behavioral scores. This is
%           the structure coefficients for behavioral scores. i.e., the
%           Pearson's correlation between canonical coefficients A and the
%           raw behavioral scores.
%      - B:
%           The canonical coefficient for factor loading.
%      - U:
%           Mx1 matrix, where M is the number of ASD participants. This is
%           the canonical covariate (mode) for behavioral scores.
%      - V:
%           Mx1 matrix, where M is the number of ASD participants. This is the
%           canonical covariate (mode) for factor loading.
%      - R:
%           The canonical correlation (Pearson's correlation between U and V).
%           This is what CCA maximizes.
%      - Rp:
%           Nperm x 1 matrix, where Nperm is the number of permutations.
%           This is the canonical correlations of all permutations.
%      - pVal:
%           p-value from permutation test.
%      - Ncca:
%           The number of significant CCA mode survived after permutation test.
%
%
% Example:
%     [A, strucCorr_score, B, U, V, R, Rp, pVal, Ncca] = CBIG_ASDf_CCA_factorBehavior([1,2,3],
%     id_keep, reg, PAPset, '../examples/input/subInfo_est.csv', '../examples/input/behavior_scores_est.csv',
%     '~/example_output/visualizeFactors/k3/r94/factorComp.txt', [3 2 1], 1)
%
% Written by Siyi Tang and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md

%% Check input variable
if  size(ind_scores,1) > 1
    error('Input argument ''ind_scores'' should be a row vector');
end