Description Usage Arguments Details Value References See Also Examples

This function implements the functional CP-TPA (FCP-TPA) algorithm, that
calculates a smooth PCA for 3D tensor data (i.e. `N`

observations of 2D
images with dimension `S1 x S2`

). The results are given in a
CANDECOMP/PARAFRAC (CP) model format

*X = ∑ d_k u_k %o% v_k %o% w_k*

where
*%o%* stands for the outer product, *d_k* is a scalar and
*u_k, v_k, w_k* are eigenvectors for each direction of the tensor. In
this representation, the outer product *v_k %o% w_k* can
be regarded as the *k*-th eigenimage, while *d_k u_k*
represents the vector of individual scores for this eigenimage and each
observation.

1 2 |

`X` |
The data tensor of dimensions |

`K` |
The number of eigentensors to be calculated. |

`penMat` |
A list with entries |

`alphaRange` |
A list of length 2 with entries |

`verbose` |
Logical. If |

`tol` |
A numeric value, giving the tolerance for relative error values in
the algorithm. Defaults to |

`maxIter` |
A numeric value, the maximal iteration steps. Can be doubled,
if |

`adaptTol` |
Logical. If |

The smoothness of the eigenvectors *v_k, w_k* is induced by penalty
matrices for both image directions, that are weighted by smoothing parameters
*α_{vk}, α_{wk}*. The eigenvectors *u_k* are not smoothed,
hence the algorithm does not induce smoothness along observations.

Optimal smoothing parameters are found via a nested generalized cross
validation. In each iteration of the TPA (tensor power algorithm), the GCV
criterion is optimized via `optimize`

on the interval
specified via `alphaRange$v`

(or `alphaRange$w`

, respectively).

The FCP_TPA algorithm is an iterative algorithm. Convergence is assumed if
the relative difference between the actual and the previous values are all
below the tolerance level `tol`

. The tolerance level is increased
automatically, if the algorithm has not converged after `maxIter`

steps
and if `adaptTol = TRUE`

. If the algorithm did not converge after
`maxIter`

steps (or `2 * maxIter`

) steps, the function throws a
warning.

`d` |
A vector of length |

`U` |
A matrix of dimensions |

`V` |
A
matrix of dimensions |

`W` |
A matrix of dimensions |

G. I. Allen, "Multi-way Functional Principal Components Analysis", IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2013.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
# set.seed(1234)
N <- 100
S1 <- 75
S2 <- 75
# define "true" components
v <- sin(seq(-pi, pi, length.out = S1))
w <- exp(seq(-0.5, 1, length.out = S2))
# simulate tensor data with dimensions N x S1 x S2
X <- rnorm(N, sd = 0.5) %o% v %o% w
# create penalty matrices (penalize first differences for each dimension)
Pv <- crossprod(diff(diag(S1)))
Pw <- crossprod(diff(diag(S2)))
# estimate one eigentensor
res <- FCP_TPA(X, K = 1, penMat = list(v = Pv, w = Pw),
alphaRange = list(v = c(1e-4, 1e4), w = c(1e-4, 1e4)),
verbose = TRUE)
# plot the results and compare to true values
plot(res$V)
points(v/sqrt(sum(v^2)), pch = 20)
legend("topleft", legend = c("True", "Estimated"), pch = c(20, 1))
plot(res$W)
points(w/sqrt(sum(w^2)), pch = 20)
legend("topleft", legend = c("True", "Estimated"), pch = c(20, 1))
``` |

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