bootstrapT3 {ThreeWay}

Bootstrap percentile intervals for Tucker3

Description

Produces percentile intervals for all output parameters. The percentile intervals indicate the instability of the sample solutions.

Usage

 bootstrapT3(X, A, B, C, G, n, m, p, r1, r2, r3, conv, centopt, normopt, 
  optimalmatch, laba, labb, labc)

Arguments

X

Matrix (or data.frame coerced to a matrix) of order (n x mp) containing the matricized array (frontal slices)

A

Component matrix for the A-mode

B

Component matrix for the B-mode

C

Component matrix for the C-mode

G

Matricized core array (frontal slices)

n

Number of A-mode entities of X

m

Number of B-mode entities of X

p

Number of C-mode entities of X

r1

Number of extracted components for the A-mode

r2

Number of extracted components for the B-mode

r3

Number of extracted components for the C-mode

conv

Convergence criterion

centopt

Centering option (see cent3)

normopt

Normalization option (see norm3)

optimalmatch

Binary indicator (0 if the procedure uses matching via orthogonal rotation towards full solutions, 1 if the procedure uses matching via optimal transformation towards full solutions)

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:

Bint

Bootstrap percentile interval of every element of B

Cint

Bootstrap percentile interval of every element of C

Gint

Bootstrap percentile interval of matricized core array (frontal slices) G

fpint

Bootstrap percentile interval for the goodness of fit index expressed as a percentage

Note

The preprocessing must be done in same way as for sample analysis.
The resampling mode must be the A-mode.
The starting points for every bootstrap solution are two: rational (using SVD) and solution from the observed sample.

Author(s)

Maria Antonietta Del Ferraro [e-mail]
Henk A.L. Kiers [e-mail]
Paolo Giordani [e-mail]

References

H.A.L. Kiers (2004). Bootstrap confidence intervals for three-way methods. Journal of Chemometrics 18:22–36.

See Also

bootstrapCP, percentile95, T3

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)
# T3 solution
BusT3 <- T3funcrep(Bus, 7, 5, 37, 2, 2, 2, 0, 1e-6)
## Not run: 
# Bootstrap analysis on T3 solution using matching via optimal transformation
boot <- bootstrapT3(Bus, BusT3$A, BusT3$B, BusT3$C, BusT3$H, 7, 5, 37, 2, 2, 2, 
 1e-6, 0, 0, 1, laba, labb, labc)
# Bootstrap analysis on T3 solution using matching via orthogonal rotation 
# (when labels are not available)
boot <- bootstrapT3(Bus, BusT3$A, BusT3$B, BusT3$C, BusT3$H, 7, 5, 37, 2, 2, 2, 
 1e-6, 0, 0, 0)

## End(Not run)