Sift - Analyse Page

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Sift's Analyse page is the go to section to create meaningful analysis on your data. This page holds a group and workspace widget, the same as which is in the Explore Page.

There are 2 main widgets within the Analyse page: the PCA widget and the SPM widget. Both of these pages allow you to do different analysis on your data: PCA is used to decompose your data into a lower-dimensional version of your data, and extract meaning about how to variance occurs within the data, while SPM is used to apply statistical tests across the entirety of your data, instead of relying on their application on summary statistics.

Both of these analytical tools can be launched from the top toolbar, which opens the PCA Dialog and GLM Dialog.

Principal Component Analysis

The PCA page consists of 6 sub-pages related to PCA: Variance Explained, Loading Vector, Workspace Scores, Group Scores, Extreme Plot and PC Reconstruction, which are located on the bottom of the page. Each of these pages provides unique analysis into a section of PCA. Each of these sub-pages can be dragged to reorder or popped-out into its own standalone window by clicking on the double window icon at the top right of the PCA Graph window. Many aspects of each display are customizable, with the user able to specify line styles, colours, and axis labels among other display features.

Variance Explained

The Variance Explained page contains a bar for each principal component (PC) that was calculated. The line along the back of the graph shows the cumulative amount of variance explained. The total variance explained by all of the calculated PCs is written in the top left corner of the graph.

Hovering over a bar causes the percentage explained by that specific PC to be displayed on top of the bar. Hovering over the points on the cumulative amount line causes the PC number, the variance explained by that PC and cumulative variance explained to appear beside the point.

Loading Vector


Workspace Scores


Group Scores


Extreme Plot


PC Reconstruction

Statistical Parametric Mapping

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