6- IV-Tucker
Tucker model is an N-way model. For a scientific overview of the method, click here.
Some specifications:

- It does not allow missing data.

- "Num.factors X" is the place where the user puts the N parameters of X parameters.
- "Num.factors Y" is the place where the user puts the N parameters of Y parameters.

The user has to write "-1" when he does not want to reduce the correponding mode (Tucker2/1 ).

- "Num. common modes" is the number of modes where You want to do the projections. It is automatically done on the first modes.
- "OK" button executes Tucker model.A default plot is returned after run (unless an information appears). The other plots are available in the menu "results".
- "bootstrap" button excutes Tucker model on original and resampled (by bootstrap) data. The user will obtain the same plot as preceeding, but with a bootstrap estimation of stability of the model (convex hulls, etc...). Available only for 3D!
- "Preprocessing" button opens a new window, see below.
- "Postprocessing" button opens a new window, see below.
- "close" button closes the IV-Tucker window.
- Validation window:(Available only for 3D!).Cross-validation is effectued, cutting off alternetively the slabs corresponding to each modality of the 1st mode. Prediction is also tested.After having chosen the maximal parameters, the user has to click on the "OK" button. A surface is plotted .

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Submenus and sub-windows:

preprocess menu:

see parafac window. Here preprocessing is ava ilable for X and Y.

post-processing window:

Tucker solutions are subspaces:we can choose the basis of these subspaces , in order to have the most interpretable solution. Basis can be found in order to maximise different criteria.

- maximise the diagonale of the core (when the core is cubic)
- maximise the slice-wise diagonale of the core (when the 2 first dimensions of the core are equal.
- maximise thesquared variance of the core.
- No rotation:default, i.e. Singular Value Decomposition of the columns of the loadings.

Bootstrap options:

- Bootstrap model : naive (resampling the horizontal slabs of X) and residual (resampling the horizontal slabs of the residuals of Tucker(X)).
- Bootstrap replicates: Number of replicates.