bootstrapCP: Bootstrap percentile intervals for CANDECOMP/PARAFAC

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

View source: R/bootstrapCP.R

Description

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

Usage

1
2
3
 
 bootstrapCP(X, A, B, C, n, m, p, r, ort1, ort2, ort3, conv, centopt, normopt, 
  scaleopt, maxit, 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

n

Number of A-mode entities of X

m

Number of B-mode entities of X

p

Number of C-mode entities of X

r

Number of extracted components

ort1

Type of constraints on A (see CP)

ort2

Type of constraints on B (see CP)

ort3

Type of constraints on C (see CP)

conv

Convergence criterion

centopt

Centering option (see cent3)

normopt

Normalization option (see norm3)

scaleopt

Scaling option (see renormsolCP)

maxit

Maximal number of iterations

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

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

bootstrapT3, CP, percentile95

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
data(TV)
TVdata=TV[[1]]
labSCALE=TV[[2]]
labPROGRAM=TV[[3]]
labSTUDENT=TV[[4]]
# permutation of the modes so that the A-mode refers to students
TVdata <- permnew(TVdata, 16, 15, 30)
TVdata <- permnew(TVdata, 15, 30, 16)
# CP solution
TVcp <- CPfuncrep(TVdata, 30, 16, 15, 2, 1, 1, 1, 0, 1e-6, 10000)
## Not run: 
# Bootstrap analysis on CP solution
boot <- bootstrapCP(TVdata, TVcp$A, TVcp$B, TVcp$C, 30, 16, 15, 2, 1, 1, 1, 
 1e-6, 0, 0, 0, 10000, labSTUDENT, labSCALE, labPROGRAM)
# Bootstrap analysis on CP solution (when labels are not available)
boot <- bootstrapCP(TVdata, TVcp$A, TVcp$B, TVcp$C, 30, 16, 15, 2, 1, 1, 1, 
 1e-6, 0, 0, 0, 10000)

## End(Not run)

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