HAS-Motion Software Documentation

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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]].