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--- sift:principal_component_analysis:pca_overview [2024/06/19 12:46] – sgranger
+++ sift:principal_component_analysis:pca_overview [2024/07/12 13:27] (current) – removed sgranger
@@ Line 1: / Line 1: @@
-{{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)]].
-this page includes:
-  * visualizing the [[sift:application:analyse_page#variance_explained|variance explained by each pc individually]];
-  * visualizing the [[sift:application:analyse_page#loading_vector|variance explained by each pc at each point in the signal's cycle]];
-  * [[sift:application:analyse_page#workspace_scores|scatter-plotting workspace scores]] in pc-space;
-  * showing the [[sift:application:analyse_page#group_scores|distribution of scores by group]] for each pc;
-  * visualizing the [[sift:application:analyse_page#extreme_plot|mean and extreme values]] that result from reconstructing the underlying data with each pc; and
-  * visualizing how the signals can be [[sift:application:analyse_page#pc_reconstruction|reconstructed from the computed pcs]].
-===== further analysis =====
-sift has several built in modules to take you further with you pca analysis, including:
-  * [[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: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: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.
-===== tutorials =====
-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 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]].
-}}