How to apply PCA for my dataset

Question

shagun 2024-4-19

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2109166-how-to-apply-pca-for-my-dataset

评论： shagun 2024-4-22

Hi,

I am trying to apply pca on my dataset but I am not able to get bioplot. I just need bioplot for my data. Please help me. I am very new to Matlab.

I have attached by dataset

Thanks

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Answer 1

Taylor 2024-4-19

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https://ww2.mathworks.cn/matlabcentral/answers/2109166-how-to-apply-pca-for-my-dataset#answer_1444646

pcaDemo.mlx

The Reduce Dimensionality Live Task is perfect for this. Select "show code" at the bottom of the task and from there you can modify the code to your needs.

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shagun 2024-4-19

Screenshot 2024-04-19 at 4.13.11 PM.png

thanks, but how should i put variable name on my graph.

Taylor 2024-4-22

You can use the legend, xlabel, and ylabel functions to label your plot.

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Answer 2

shagun 2024-4-22

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https://ww2.mathworks.cn/matlabcentral/answers/2109166-how-to-apply-pca-for-my-dataset#answer_1445881

Screenshot 2024-04-22 at 11.49.39 AM.png

Thank you, But i want my variable name on the graph like same as attached in screenshot.

Thanks

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shagun 2024-4-22

在 MATLAB Online 中打开

I have tried but I am getting this error

load (Datagroup)
% Extract numeric variables from table
numericVars = varfun(@isnumeric,Datagroup,"output","uniform");
numericPredictorTable = Datagroup(:, numericVars);
% Convert table to matrix
numericPredictorMatrix = table2array(varfun(@double, numericPredictorTable));
% Principal Component Analysis
[coeffs, transformedData, ~, ~, explained] = pca(numericPredictorMatrix);
% Compute number of components from explained variance
enoughExplained = cumsum(explained)/sum(explained) >= 95/100;
numberOfComponentsToKeep = find(enoughExplained, 1);
disp("The number of components needed to explain at least 95% of the variance is "+ num2str(numberOfComponentsToKeep))
% Display results
figure
colorMap = colormap("lines");
barPlot = bar(explained);
hold on;
plot(cumsum(explained),"*-","Color",colorMap(3,:));
hold off;
barPlot(1).FaceColor = "flat";
barPlot(1).CData(1:numberOfComponentsToKeep,:) = repmat( ...
    colorMap(2,:),[numberOfComponentsToKeep 1]);
label = ["Selected components"; sprintf("Cumulative explained\nvariance")];
legend(label,"Location","best");
title("Scree Plot of Explained Variances")
xlabel("Principal component")
ylabel("Variance explained (%)")
clear barPlot colorMap label explained
figure
scatter(transformedData(:,1),transformedData(:,2), "Marker", ".");
title("Scatter Plot of Principal Components")
xlabel("Component 1")
ylabel("Component 2")
variableNames = {'TSSLV10', 'TSSLV20', 'TSSLV30', 'TSSLV40', 'TSSLV50', 'TSSLV60', 'TSSLV70', 'TSSLV80', 'TSSLV90', ...
                 'TPLV10', 'TPLV20', 'TPLV30', 'TPLV40', 'TPLV50', 'TPLV60', 'TPLV70', 'TPLV80', 'TPLV90', ...
                 'SRPLV10', 'SRPLV20', 'SRPLV30', 'SRPLV40', 'SRPLV50', 'SRPLV60', 'SRPLV70', 'SRPLV80', 'SRPLV90', ...
                 'RDep10', 'RDep20', 'RDep30', 'RDep40', 'RDep50', 'RDep60', 'RDep70', 'RDep80', 'RDep90', ...
                 'AI10', 'AI20', 'AI30', 'AI40', 'AI50', 'AI60', 'AI70', 'AI80', 'AI90', ...
                 'RD10', 'RD20', 'RD30', 'RD40', 'RD50', 'RD60', 'RD70', 'RD80', 'RD90', 'TRD', 'AI', 'RD', 'ADP (HR)'};
             
figure
biplot(coeffs(:,[1 2],'VarLabels', variableNames));
title("PCA Biplot")
xlabel("Component 1")
ylabel("Component 2")
clear coeffs enoughExplained explained numericPredictorMatrix numericPredictorTable varLabels
transformedData = transformedData(:,1:numberOfComponentsToKeep);
clear numberOfComponentsToKeep
transformedData = array2table(transformedData);
transformedData = [transformedData,Datagroup(:,~numericVars)];
clear numericVars