Differences

This shows you the differences between two versions of the page.

--- sift:principal_component_analysis:using_principal_component_analysis_in_biomechanics [2024/07/12 13:58] – created sgranger
+++ sift:principal_component_analysis:using_principal_component_analysis_in_biomechanics [2024/11/05 15:03] (current) – wikisysop
@@ Line 1: / Line 1: @@
-====== Using_Principal_Component_Analysis_in_Biomechanics ======
+====== Using Principal Component Analysis in Biomechanics ======
-{{SIFT_PCA_Example.png}}
+{{:SIFT_PCA_Example.png}}
 Sift provides a number of ways to visualize and interact with the results of PCA. An overview of all PCA visualizations can be found in [[Sift:Application:Analyse_Page#Principal_Component_Analysis|Sift - Analyse Page]]. More detail on the mathematics behind PCA can be found on our page: [[Sift:Principal_Component_Analysis:The_Math_of_Principal_Component_Analysis_(PCA)|The Math of Principal Component Analysis (PCA)]].
@@ Line 16: / Line 16: @@
 ==== Further Analysis ====
-Sift has several built in modules to take you further with you PCA Analysis, including:
+Sift has several built in modules to take you further with you PCA Analysis:
-  * [[Sift:Principal_Component_Analysis:T-Squared_and_Q-Test_Dialog|T-Squared Tests:]] Finding outliers using a Multi Variate T-Squared Distribution.
+  * [[sift:principal_component_analysis:outlier_detection_for_pca|Outlier Detection for PCA:]] An overview of the PCA outlier detection methods built into Sift.
-  * [[Sift:Principal_Component_Analysis:T-Squared_and_Q-Test_Dialog|Q-Tests:]] Finding Outliers on small datasets using a Q-Test.
   * [[Sift:Principal_Component_Analysis:Mahalanobis_Distance_and_SPE_Dialog|Mahalanobis Distances:]] Finding outliers through their Mahalanobis Distances.
+  * [[Sift:Principal_Component_Analysis:Mahalanobis_Distance_and_SPE_Dialog|Squared Prediction Error:]] Finding outliers through their SPE.
   * [[Sift:Principal_Component_Analysis:Local_Outlier_Factor_Dialog|Local Outlier Factors:]] Finding outliers through the Local Outlier Factor.
-  * [[Sift:Principal_Component_Analysis:K-Means_Dialog|K-Means Analysis:]] Clustering PCA results through K-Means clustering.
+  * [[sift:tutorials:run_k-means|K-Means Analysis:]] Clustering PCA results through K-Means clustering.
 ==== Tutorials ====
@@ Line 28: / Line 28: @@
 For a step-by-step example of how to use Sift to perform PCA on your data, see the [[Sift:Tutorials:Perform_Principal_Component_Analysis|PCA Tutorial]].
-For a step-by-step example of how to use Sift to perform further statistical testing on PCA results, see the [[Sift:Tutorials:Run_K-Means|Tutorial: Run K-Means]].
+For a step-by-step example of how to use Sift to perform further k-means clustering on PCA results, see the [[Sift:Tutorials:Run_K-Means|Tutorial: Run K-Means]].
+For a step-by-step example of how to use Sift to perform outlier analysis on PCA results, see the [[sift:tutorials:outlier_detection_with_pca|Tutorial: Run PCA Outlier Analysis]].
 For a step-by-step example of processing and analyzing large data sets in Sift and using PCA to distinguish between groups, see the [[Sift:Tutorials:Treadmill_Walking_in_Healthy_Individuals|Tutorial: Treadmill Walking In Healthy Individuals]] and the [[Sift:Tutorials:OpenBiomechanics_Project:Analysis_of_Baseball_Hitters_at_Different_Levels_of_Competition|Tutorial: Analysis of Baseball Hitters]].