Description Usage Arguments Value References See Also
This function clusters along one way of a three-way array (as specified by margin
) while
decomposing along the other two dimensions. Four types of clusterings are allowed based on the
respective two-way slices of the array: on the overall means, row margins, column margins and the
interactions between rows and columns. Which clusterings can be fit is determined by the vector
delta
, with four binary elements. All orthogonal models are fitted.
The nonorthogonal case delta = (1, 1, 0, 0)
returns an error. See the reference for further details.
1 2 3 4 5 |
data |
A three-way array representing the data. |
margin |
An integer giving the single subscript of |
delta |
A four-element binary vector (logical or numeric) indicating which sum-to-zero constraints must be enforced. |
nclust |
A vector of length four giving the number of clusters for the overall mean, the row margins, the column margins and the interactions (in that order) respectively. Alternatively, a vector of length one, in which case all components will have the same number of clusters. |
ndim |
The required rank for the approximation of the interactions (a scalar). |
fixed |
One of |
nstart |
The number of random starts to use for the interaction clustering. |
starts |
A list containing starting configurations for the cluster membership vector. If not
supplied, random initializations will be generated (passed to |
nstart.kmeans |
The number of random starts to use in |
alpha |
Numeric value in [0, 1] which determines how the singular values are distributed
between rows and columns (passed to |
parallel |
Logical indicating whether to parallel over different starts or not
(passed to |
maxit |
The maximum number of iterations allowed in the interaction clustering. |
verbose |
Integer controlling the amount of information printed: 0 = no information, 1 = Information on random starts and progress, and 2 = information is printed after each iteration for the interaction clustering. |
method |
The method for calculating cluster agreement across random starts, passed on
to |
type |
One of |
sep.nclust |
Logical indicating how nclust should be used across different |
... |
Additional arguments passed to |
Returns an object of S3 class lsbclust
which has slots:
|
Object of class |
|
Object of class |
|
Object of class |
|
Object of class |
|
The function call used to create the object |
|
The value of |
|
Breakdown of the degrees-of-freedom across the different subproblems |
|
Breakdown of the loss across subproblems |
|
Time taken in seconds to calculate the solution |
|
Matrix of cluster membership per observation for all cluster types |
Schoonees, P.C., Groenen, P.J.F., Van de Velden, M. Least-squares Bilinear Clustering of Three-way Data. Econometric Institute Report, EI2014-23.
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