Various routines to print Iterative Oblique SSA results

1 2 3 4 |

`x, object` |
object of class ‘iossa.result’ or ‘ossa’ |

`digits` |
integer, used for number formatting |

`...` |
further arguments passed to method |

An object of class ‘iossa.result’ is a list with the following fields:

- converged
logical, whether algorithm has been converged

- iter
the number of OSSA iterations

- cond
numeric vector with two elements, condition numbers of the final column and row inner products

- initial.tau
numeric vector, proportions of high rank components contribution for each of initial series (denotes how well the series is approximated by a series of finite rank)

- tau
numeric vector, proportions of high rank components contribution for each of final series

- initial.wcor
W-correlation matrix of the initial nested decomposition

- wcor
W-correlations matrix of the final nested decomposition

- owcor
oblique W-correlation matrix (see

`owcor`

) of the final nested decomposition- initial.rec
list of initial series (reconstructed initial nested decomposition)

- kappa, maxiter, tol
Iterative O-SSA procedure parameters

Golyandina N. and Shlemov A. (2015): *Variations of Singular Spectrum Analysis
for separability improvement: non-orthogonal decompositions of time series*,
Statistics and Its Interface. Vol.8, No 3, P.277-294.
http://arxiv.org/abs/1308.4022

`Rssa`

for an overview of the package, as well as,
`iossa`

,
`owcor`

,
`summary.ssa`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# Separate three non-separable sines with different amplitudes
N <- 150
L <- 70
omega1 <- 0.05
omega2 <- 0.06
omega3 <- 0.07
F <- 4*sin(2*pi*omega1 * (1:N)) + 2*sin(2*pi*omega2 * (1:N)) + sin(2*pi*omega3 * (1:N))
s <- ssa(F, L)
ios <- iossa(s, nested.groups = list(1:2, 3:4, 5:6), kappa = NULL, maxiter = 100, tol = 1e-3)
print(ios)
print(ios$iossa.result)
``` |

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