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--- sift:tutorials:perform_principal_component_analysis [2024/08/01 15:17] – wikisysop
+++ sift:tutorials:perform_principal_component_analysis [2024/11/28 19:15] (current) – [Perform Principal Component Analysis] wikisysop
@@ Line 3: / Line 3: @@
 This tutorial will show you how to use Sift in order to perform [[Sift:Principal_Component_Analysis:Principal_Component_Analysis|Principal Component Analysis]] (PCA) using data from a [[Visual3D:Documentation:Definitions:CMO_Library_|CMO library]]. For a full treatment of waveform-based PCA to find differences in waveform data, see the explanation presented in [[https://us.humankinetics.com/products/research-methods-in-biomechanics-2nd-edition|the Research Methods in Biomechanics textbook]].
-For this tutorial, we will be comparing the the knee flexion angles between participants with osteoarthritis and the normal control group. Our problem is to provide an explanation for differences in knee flexion angles between osteoarthritic walking versus normal walking. We can accomplish this by defining two groups that meet these signal definitions, performing PCA, and interpreting the results.
+For this tutorial, we will be comparing the knee flexion angles between participants with osteoarthritis and the normal control group. Our problem is to provide an explanation for differences in knee flexion angles between osteoarthritic walking versus normal walking. We can accomplish this by defining two groups that meet these signal definitions, performing PCA, and interpreting the results.
+If you prefer, a video tutorial is available outlining the same process. It is available at this link: [[https://youtu.be/6lMsQpSx9BI?feature=shared|Sift Tutorial Video 3: Performing Principal Component Analysis (PCA)]]
 ==== Data ====
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 === Variance Explained ===
-The Variance Explained window, which displays the variance explained by each principal component as well as the cumulative variance for each principal components. It is important to verify that the calculated principal components do explain a significant amount of the data set's variability. A good heuristic to use is that you want enough principal components to explain 95% of the data set's variety, otherwise there will be at least a moderate amount of variation that your analysis has not captured. In this example, our 4 principal components explain 96% of the data set's variability, which is sufficient and we can continue the exploration. If there less than 95% of the data set's variance was explained then we should re-run the analysis with more principal components.
+The Variance Explained window, which displays the variance explained by each principal component as well as the cumulative variance for each principal components. It is important to verify that the calculated principal components do explain a significant amount of the data set's variability. A good heuristic to use is that you want enough principal components to explain 95% of the data set's variety, otherwise there will be at least a moderate amount of variation that your analysis has not captured. In this example, our 4 principal components explain 96% of the data set's variability, which is sufficient and we can continue the exploration. If less than 95% of the data set's variance was explained then we should re-run the analysis with more principal components.
 {{:Sift_pca_tut_Results1.png?800}}