T2 function

Interactive Tucker2 analysis

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

T2(dati, laba, labb, labc)

Arguments

• 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

Returns

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

References

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.

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

See Also

CP,T3,T1

Examples

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)