pointer | R Documentation |

This function calculates pointer years on a `data.frame`

of
ring-width series using the Becker algorithm. The pointer years are
computed with adjustable thresholds of relative radial growth
variation and number of series displaying similar growth pattern
(i.e. positive or negative variations).

```
pointer(rwl, rgv.thresh = 10, nseries.thresh = 75, round.decimals = 2)
```

`rwl` |
a |

`rgv.thresh` |
a |

`nseries.thresh` |
a |

`round.decimals` |
an |

This calculates pointer years from ring-width series for each year

of the time period covered by the series using the
Becker algorithm. This algorithm is based on, first, the calculation
of the individual relative radial growth variation by comparison of
ring-width of year `t`

to that of year `t`

for
each series, and second, the inter-series comparison of both sign and
magnitude of these variations.
`t`-1

For example, if

and
`rgv.thresh`

are set at 10 and 75 respectively, pointer
years will be defined as those years when at least 75% of the series
present an absolute relative radial growth variation higher than 10%.
`nseries.thresh`

Users unfamiliar with the Becker algorithm should refer to Becker et al. (1994) and Mérian and Lebourgeois (2011) for further details.

A `data.frame`

containing the following columns (each row
corresponds to one position of the window):

`Year` |
Considered year (t). |

`Nb.series` |
Number of available series. |

`Perc.pos` |
Percentage of series displaying a significant positive radial growth variation. |

`Perc.neg` |
Percentage of series displaying a significant negative radial growth variation. |

`Nature` |
Number indicating whether the year is a positive pointer year (1), a negative pointer year (-1) or a regular year (0). |

`RGV_mean` |
Mean radial growth variations over the available series. |

`RGV_sd` |
Standard deviation of the radial growth variations over the available series. |

Pierre Mérian. Improved by Mikko Korpela and Andy Bunn.

Becker, M., Nieminen, T. M., and Gérémia, F. (1994)
Short-term variations and long-term changes in oak productivity in
northeastern France – the role of climate and atmospheric
CO2. *Annals of Forest Science*, **51**(5),
477–492.

Mérian, P. and Lebourgeois, F. (2011) Size-mediated
climate–growth relationships in temperate forests: A multi-species
analysis. *Forest Ecology and Management*,
**261**(8), 1382–1391.

`skel.plot`

```
## Pointer years calculation on ring-width series. Returns a data.frame.
library(utils)
data(gp.rwl)
py <- pointer(rwl=gp.rwl, rgv.thresh=10, nseries.thresh=75,
round.decimals=2)
tail(py)
```

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