bootstrapT3: Bootstrap percentile intervals for Tucker3

Description Usage Arguments Value Note Author(s) References See Also Examples

View source: R/bootstrapT3.R

Description

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

Usage

1
2
 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 [email protected]
Henk A.L. Kiers [email protected]
Paolo Giordani [email protected]

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
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)

ThreeWay documentation built on May 29, 2017, 11:52 p.m.