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ThreeWay (version 1.1.3)

T3: Interactive Tucker3 analysis

Description

Detects the underlying structure of a three-way array according to the Tucker3 (T3) model.

Usage

T3(data, laba, labb, labc)

Arguments

data
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

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
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 T3func)
funcValues
Function values upon convergence for all the runs of the CP algorithm (see T3func)
cputime
Computation times for all the runs of the CP algorithm (see T3func)
iter
Numbers of iterations upon convergence for all the runs of the CP algorithm (see T3func)
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)
Bint
Bootstrap percentile interval of every element of B (see bootstrapT3)
Cint
Bootstrap percentile interval of every element of C (see bootstrapT3)
Kint
Bootstrap percentile interval of every element of core (see bootstrapT3)
fpint
Bootstrap percentile interval for the goodness of fit index expressed as a percentage (see bootstrapT3)
Afull
Component matrix for the A-mode (full data) from split-half analysis (see splithalfT3)
As1
Component matrix for the A-mode (split n.1) from split-half analysis (see splithalfT3)
As2
Component matrix for the A-mode (split n.2) from split-half analysis (see splithalfT3)
Bfull
Component matrix for the B-mode (full data) from split-half analysis (see splithalfT3)
Bs1
Component matrix for the B-mode (split n.1) from split-half analysis (see splithalfT3)
Bs2
Component matrix for the B-mode (split n.2) from split-half analysis (see splithalfT3)
Cfull
Component matrix for the C-mode (full data) from split-half analysis (see splithalfT3)
Cs1
Component matrix for the C-mode (split n.1) from split-half analysis (see splithalfT3)
Cs2
Component matrix for the C-mode (split n.2) from split-half analysis (see splithalfT3)
Kfull
Matricized core array (frontal slices) (full data) from split-half analysis (see splithalfT3)
Ks1
Matricized core array (frontal slices) (split n.1) from split-half analysis (see splithalfT3)
Ks2
Matricized core array (frontal slices) (split n.2) from split-half analysis (see splithalfT3)
Kss1
Matricized core array (frontal slices) (using full data solutions for A,B and C for split n.1) from split-half analysis (see splithalfT3)
Kss2
Matricized core array (frontal slices) (using full data solutions for A,B and C for split n.2) from split-half analysis (see splithalfT3)
Aplot
Coordinates for plots of the A-mode entities
Bplot
Coordinates for plots of the B-mode entities
Cplot
Coordinates for plots of the C-mode entities
CBplot
Coordinates for plots of the C and B-mode entities using the A-mode projected in it as axes (to be added in plot, i.e. coordinates in ($CBplot,$A))
ACplot
Coordinates for plots of the A and C-mode entities using the B-mode projected in it as axes (to be added in plot, i.e. coordinates in ($ACplot,$B))
BAplot
Coordinates for plots of the B and A-mode entities using the C-mode projected in it as axes (to be added in plot, i.e. coordinates in ($BAplot,$C))
A1
Component matrix for the A-mode from Principal Component Analysis of mean values (see pcamean)
B1
Component matrix for the B-mode from Principal Component Analysis of mean values (see pcamean)
C1
Component matrix for the C-mode from Principal Component Analysis of mean values (see pcamean)
A2
Component matrix for the A-mode from Principal Component Analysis of mean values (see pcamean)
B2
Component matrix for the B-mode from Principal Component Analysis of mean values (see pcamean)
C2
Component matrix for the C-mode from Principal Component Analysis of mean values (see pcamean)
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.

See Also

CP,T2,T1

Examples

Run this code
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 T3 analysis
# BusT3 <- T3(Bus, laba, labb, labc)
# # interactive T3 analysis (when labels are not available)
# BusT3 <- T3(Bus)
# ## End(Not run)

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