T2: Interactive Tucker2 analysis
In ThreeWay: Three-Way Component Analysis

Description Usage Arguments Value Author(s) References See Also Examples

Detects the underlying structure of a three-way array according to the Tucker2 (T2) model.

1 2	T2(dati, laba, labb, labc)

`dati`	Array of order `n x m x p` or matrix or data.frame of order (`n` `x` `mp`) containing the matricized array (frontal slices)
`laba`	Optional vector of length `n` containing the labels of the `A`-mode entities
`labb`	Optional vector of length `m` containing the labels of the `B`-mode entities
`labc`	Optional vector of length `p` containing the labels of the `C`-mode entities

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
`core`	Matricized core array (frontal slices)
`fit`	Fit value expressed as a percentage
`fitValues`	Fit values expressed as a percentage upon convergence for all the runs of the CP algorithm (see `T2func`)
`funcValues`	Function values upon convergence for all the runs of the CP algorithm (see `T2func`)
`cputime`	Computation times for all the runs of the CP algorithm (see `T2func`)
`iter`	Numbers of iterations upon convergence for all the runs of the CP algorithm (see `T2func`)
`fitA`	Fit contributions for the `A`-mode entities (see `T3fitpartitioning`)
`fitB`	Fit contributions for the `B`-mode entities (see `T3fitpartitioning`)
`fitC`	Fit contributions for the `C`-mode entities (see `T3fitpartitioning`)
`fitAB`	Fit contributions for the `A`-and mode `B` component combinations (see `T3fitpartitioning`)
`fitAC`	Fit contributions for the `A`-and mode `C` component combinations (see `T3fitpartitioning`)
`fitBC`	Fit contributions for the `B`-and mode `C` component combinations (see `T3fitpartitioning`)
`laba`	Vector of length `n` containing the labels of the `A`-mode entities
`labb`	Vector of length `m` containing the labels of the `B`-mode entities
`labc`	Vector of length `P` containing the labels of the `C`-mode entities
`Xprep`	Matrix of order (`n` `x` `mp`) containing the matricized array (frontal slices) after preprocessing used for the analysis

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

P. Giordani, H.A.L. Kiers, M.A. Del Ferraro (2014). Three-way component analysis using the R package ThreeWay. Journal of Statistical Software 57(7):1–23. http://www.jstatsoft.org/v57/i07/.
P.M. Kroonenberg (2008). Applied Multiway Data Analysis. Wiley, New Jersey.
L.R Tucker (1966). Some mathematical notes on three-mode factor analysis. Psychometrika 31:279–311.

CP,T3,T1

data(Bus)
# labels for Bus data
laba <- rownames(Bus)
labb <- substr(colnames(Bus)[1:5], 1, 1)
labc <- substr(colnames(Bus)[seq(1,ncol(Bus),5)], 3, 8)
## Not run: 
# interactive T2 analysis
BusT2 <- T2(Bus, laba, labb, labc)
# interactive T2 analysis (when labels are not available)
BusT2 <- T2(Bus)

## End(Not run)