# CPfuncrep: Algorithm for the Candecomp/Parafac (CP) model In ThreeWay: Three-Way Component Analysis

## Description

Alternating Least Squares algorithm for the minimization of the Candecomp/Parafac loss function.

## Usage

 `1` ``` CPfuncrep(X, n, m, p, r, ort1, ort2, ort3, start, conv, maxit, A, B, C) ```

## Arguments

 `X` Matrix (or data.frame coerced to a matrix) of order (`n` `x` `mp`) containing the matricized array (frontal slices) `n` Number of `A`-mode entities `m` Number of `B`-mode entities `p` Number of `C`-mode entities `r` Number of extracted components `ort1` Type of constraints on `A` (1 for no constraints, 2 for orthogonality constraints, 3 for zero correlations constraints) `ort2` Type of constraints on `B` (1 for no constraints, 2 for orthogonality constraints, 3 for zero correlations constraints) `ort3` Type of constraints on `C` (1 for no constraints, 2 for orthogonality constraints, 3 for zero correlations constraints) `start` Starting point (0 for starting point of the algorithm from SVD's, 1 for random starting point (orthonormalized component matrices), 2 for user specified components `conv` Convergence criterion `maxit` Maximal number of iterations `A` Optional (necessary if start=2) starting value for `A` `B` Optional (necessary if start=2) starting value for `B` `C` Optional (necessary if start=2) starting value for `C`

## Value

A list including the following components:

 `A` Component matrix for the `A`-mode `B` Component matrix for the `B`-mode `C` Component matrix for the `C`-mode `f` Loss function value `fp` Fit value expressed as a percentage `iter` Number of iterations `tripcos` Minimal triple cosine between two components across three component matrices (to inspect degeneracy) `mintripcos` Minimal triple cosine during the iterative algorithm observed at every 10 iterations (to inspect degeneracy) `ftiter` Matrix containing in each row the function value and the minimal triple cosine at every 10 iterations `cputime` Computation time

## Note

The loss function to be minimized is sum(k)|| X(k) - A D(k) B' ||^2, where D(k) is a diagonal matrix holding the `k`-th row of `C`.
`CPfuncrep` is the same as `CPfunc` except that all printings are suppressed. Thus, `CPfuncrep` can be helpful for simulation experiments.

## Author(s)

Maria Antonietta Del Ferraro mariaantonietta.delferraro@yahoo.it
Henk A.L. Kiers h.a.l.kiers@rug.nl
Paolo Giordani paolo.giordani@uniroma1.it

## References

R.A. Harshman (1970). Foundations of the Parafac procedure: models and conditions for an ‘explanatory’ multi-mode factor analysis. UCLA Working Papers in Phonetics 16:1–84.

`CP`, `CPfunc`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```data(TV) TVdata=TV[[1]] # permutation of the modes so that the A-mode refers to students TVdata <- permnew(TVdata, 16, 15, 30) TVdata <- permnew(TVdata, 15, 30, 16) # unconstrained CP solution using two components # (rational starting point by SVD [start=0]) TVcp <- CPfuncrep(TVdata, 30, 16, 15, 2, 1, 1, 1, 0, 1e-6, 10000) # constrained CP solution using two components with orthogonal A-mode # component matrix (rational starting point by SVD [start=0]) TVcp <- CPfuncrep(TVdata, 30, 16, 15, 2, 2, 1, 1, 0, 1e-6, 10000) # constrained CP solution using two components with orthogonal A-mode # component matrix and zero correlated C-mode component matrix # (rational starting point by SVD [start=0]) TVcp <- CPfuncrep(TVdata, 30, 16, 15, 2, 2, 1, 3, 0, 1e-6, 10000) # unconstrained CP solution using two components # (random orthonormalized starting point [start=1]) TVcp <- CPfuncrep(TVdata, 30, 16, 15, 2, 1, 1, 1, 1, 1e-6, 10000) # unconstrained CP solution using two components (user starting point [start=2]) TVcp <- CPfuncrep(TVdata, 30, 16, 15, 2, 1, 1, 1, 2, 1e-6, 10000, matrix(rnorm(30*2),nrow=30), matrix(rnorm(16*2),nrow=16), matrix(rnorm(15*2),nrow=15)) ```

ThreeWay documentation built on May 2, 2019, 9:20 a.m.