how to perform principal component analysis in matlab

main.m
% create a sample data matrix
X = randn(100, 5);

% perform PCA on the data
[coeff, score, latent, tsquared, explained] = pca(X);

% display the coefficients (eigenvectors)
disp('Coefficients (Eigenvectors):');
disp(coeff);

% display the scores (projection of original data onto principal component axes)
disp('Scores (Projection of data onto principal component axes):');
disp(score);

% display the eigenvalues (variances explained by each principal component)
disp('Eigenvalues (Variances explained by each principal component):');
disp(latent);

% display the squared Mahalanobis distances of each observation from the center of the data
disp('Squared Mahalanobis Distances:');
disp(tsquared);

% display the percentage of total variance explained by each principal component
disp('Percentage of Variance Explained:');
disp(explained);
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main.m
% create a sample data matrix
X = randn(100, 5);

% perform PCA on the data with scaling and centering
[score, coeff, latent, tsquared, explained] = pcares(X);

% display the scores (projection of original data onto principal component axes)
disp('Scores (Projection of data onto principal component axes):');
disp(score);

% plot the scree plot
pcaresplot(latent, explained);
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