Hi Manpreet,
When you perform PCA using MATLAB's pca function, the score matrix contains the principal component scores for each observation. These scores are the coordinates of the observations in the new principal component space.
Here's an example of how you can do this :
% Example data matrix X (9x7)
% X = [ ... ]; % Your data matrix
% Perform PCA
[coeff, score, latent, tsquared, explained, mu] = pca(X);
% Create a bi-plot
figure;
biplot(coeff(:,1:2), 'Scores', score(:,1:2), 'VarLabels', {'Var1','Var2','Var3','Var4','Var5','Var6','Var7'});
title('PCA Bi-plot');
% Manually add observation labels
obsLabels = {'S1', 'S2', 'S3', 'S4', 'S5', 'S6', 'S7', 'S8', 'S9'};
for i = 1:length(obsLabels)
text(score(i,1), score(i,2), obsLabels{i}, 'VerticalAlignment', 'bottom', 'HorizontalAlignment', 'right');
end
