ThreeWay (version 1.1.3)

bootstrapT3: 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)

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

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

Author

Maria Antonietta Del Ferraro
Henk A.L. Kiers
Paolo Giordani

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)
if (FALSE) {
# 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)
}