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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)

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

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

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)
# 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)

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