Description Objects from the Class Slots Methods See Also

The parameter object to be specified against CoupledMWCA function.

Objects can be created by calls of the form `new("CoupledMWCAParams", ...)`

.

MWCAParams has four settings as follows. For each setting, the list must have the same structure.

*1. Data-wise setting*
Each item must be a list object that is as long as the number of data and is named after the data.

- Xs:
A list containing multiple high-dimensional arrays.

- mask:
A list containing multiple high-dimensional arrays, in which 0 or 1 values are filled to specify the missing elements.

- weights:
A list containing multiple high-dimensional arrays, in which some numeric values are specified to weigth each data.

*2. Common Model setting*
Each item must be a nested list object that is as long as the number of data and is named after the data.

- common_model:
Each element of the list must be a list corresponding the dimention name of data and common factor matrices name.

*3. Common Factor matrix-wise setting*
Each item must be a list object that is as long as the number of common factor matrices and is named after the factor matrices.

- common_initial:
The initial values of common factor matrices. If nothing is specified, random matrices are used.

- common_algorithms:
Algorithms used to decompose the matricised tensor in each mode.

- common_iteration:
The number of iterations.

- common_decomp:
If FALSE is specified, unit matrix is used as the common factor matrix.

- common_fix:
If TRUE is specified, the common factor matrix is not updated in the iteration.

- common_dims:
The lower dimension of each common factor matrix.

- common_transpose:
Whether the common factor matrix is transposed to calculate core tensor.

- common_coretype:
If "CP" is specified, all the core tensors become diagonal core tensors. If "Tucker" is specified, all the core tensors become dense core tensors.

*4. Specific Model setting*
Each item must be a nested list object that is as long as the number of data and is named after the data.

- specific_model:
Each element of the list must be a list corresponding the dimention name of data and data specific factor matrices name.

*5. Specific Factor matrix-wise setting*
Each item must be a list object that is as long as the number of data specific factor matrices and is named after the factor matrices.

- specific_initial:
The initial values of data specific factor matrices. If nothing is specified, random matrices are used.

- specific_algorithms:
Algorithms used to decompose the matricised tensor in each mode.

- specific_iteration:
The number of iterations.

- specific_decomp:
If FALSE is specified, unit matrix is used as the data specific factor matrix.

- specific_fix:
If TRUE is specified, the data specific factor matrix is not updated in the iteration.

- specific_dims:
The lower dimension of each data specific factor matrix.

- specific_transpose:
Whether the data specific factor matrix is transposed to calculate core tensor.

- specific_coretype:
If "CP" is specified, all the core tensors become diagonal core tensors. If "Tucker" is specified, all the core tensors become dense core tensors.

*6. Other option*
Each item must to be a vector of length 1.

- specific:
Whether data specific factor matrices are also calculated.

- thr:
The threshold to stop the iteration. The higher the value, the faster the iteration will stop.

- viz:
Whether the output is visualized.

- figdir:
When viz=TRUE, whether the plot is output in the directory.

- verbose:
Whether the process is monitored by verbose messages.

- CoupledMWCA
Function to peform CoupledMWCA.

`CoupledMWCAResult-class`

, `CoupledMWCA`

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