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

View source: R/auto.l1tp.smooth.R

It is a heuristic procedure which tries to figure out positions of
period and cohort effects in the data. It also uses a few steps to estimate
model's parameters. The procedure is supposed to outperform `autoSmoothAPC`

slightly.

1 2 3 4 5 | ```
signifAutoSmoothAPC(data, p.value = 0.05, cornerLength = 7,
lower = c(0.01, 0.01, 0.01, 1, 0.001, 1, 0.001), upper = c(1.2, 1.8, 1.2,
12, 0.4, 12, 0.4), init = c(0.1, 0.1, 0.2, 4, 0.001, 4, 0.001),
reltol = 0.001, trace = F, control = list(nnzlmax = 1e+06, nsubmax =
2e+06, tmpmax = 2e+05), weights = NULL)
``` |

`data` |
Demographic data (log mortality) presented as a matrix. Row numbers represent ages and column numbers represet time. |

`p.value` |
P-value used to test the period and the cohort effects for significance. The lower the value the fewer diagonals and years will be used to find cohort and period effects. |

`cornerLength` |
Minimal length of a diagonal to be considered for cohort effects. |

`lower` |
Lowest possible values for the optimization procedure. |

`upper` |
Highest possible values for the optimization procedure. |

`init` |
Initial values for the optimization procedure. |

`reltol` |
Relative tolerance parameter to be supplied to |

`trace` |
Controls if tracing is on. |

`control` |
The control data passed directly to |

`weights` |
Define how much every observation effect the resulting smooth surface.
The parameter must have same dimentions as |

A list of six components: smooth surface, period effects, cohort effects, parameters used for smoothing, diagonals used for cohort effects and years used for period effects.

Alexander Dokumentov

http://robjhyndman.com/publications/mortality-smoothing/

1 2 3 4 5 6 7 8 |

smoothAPC documentation built on May 18, 2018, 5:04 p.m.

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