variable_hclust.m

function [] = variable_hclust(dirstring, labels, c, Xc, pc, beh_type)
    % This function performs Ward Hierarchical cluastering on the data
    % and takes as paramters:
    % dirstring: string indicating the directory
    % labels: in the order that they are in the dataset
    % c: clock to help save out this figure's unique string
    % Xc: the cleaned predictor variable matrix for the population
    % pc: the (numeric) IDs for each person
    % beh_type: the type of behavior (AGG, SIB, or BOTH)

    % Figure setup - 1/3 of screen size
    set(0,'DefaultTextInterpreter','none')
    screen_size = get(0, 'Screensize');
    screen_size(4) = screen_size(4) / 3;
    FigH = figure('Position', screen_size);

    e1 = load(strcat(dirstring,'/',beh_type,'people2.mat'));
    e1 = e1.eligible_people;
    e2 = load(strcat(dirstring,'/',beh_type,'people2_highacc.mat'));
    e2 = e2.high_accs;
    
    % Run the clustering for only the high-accuracy individuals
    data = Xc(ismember(pc,e1),:);%(e2)),:);

    % Z-score this subpopulation
    X0 = zscore(data);

    % Get linkage matrix and plot dendrogram
    Z = linkage(X0','ward');
    [H, p, outperm] = dendrogram(Z,0);
    
    % Set axis labels and parameters
    ax = gca;
    ax.XAxis.TickLength = [0 0];
    ax.FontSize= 12;
    title(strcat(" Ward Variable Hierarchical Clustering of data,", ...
        " >80% BCR for ",beh_type))
    ylabel('Distance')
    set(gca,'TickLabelInterpreter', 'none');
    
    % Set the x labels to the permutation from the clustering
    xticklabels(labels(outperm))
    xtickangle(45)

    % Save out figure
    fig = 0; 
    set( findall(fig, '-property', 'fontname'), 'fontname',...
        'Palatino Linotype')
    saveas(FigH, strcat(dirstring,'/',beh_type,c,'_hclust.png'),'png');
end