pointer.norm | R Documentation |

The function calculates event and pointer years on a `data.frame`

with tree-ring series using the normalization in a moving window method introduced by Cropper (1979; cf. Schweingruber et al. 1990, Jetschke et al. 2019). This method locally z-transforms tree growth in year

within a symmetric moving window of `i`

years, thereby providing the number of standard deviations that tree growth deviates in individual years (Cropper values, Ct) from the window average. To identify event years, one absolute threshold on the number of standard deviations can be set (Cropper 1979), or, alternatively, three intensity classes (Neuwirth et al. 2007). Threshold values for defining event and pointer years can be adjusted.
`n`

Prior to the calculation of event and pointer years with `pointer.norm`

, a 13-year weighted low-pass filter, as described by Fritts (1976), may be applied on the tree-ring series using `lowpass13`

. According to Cropper (1979), such a filter improves the detection of event and pointer years for complacent series, whereas for sensitive series filtering has little effect. Alternatively, a flexible detrending (e.g., using a cubic-smoothing spline with a 50% frequency cut-off at 15 years) may be applied prior to `pointer.norm`

.

pointer.norm(data, period = NULL, window = 13, method.thresh = c("Cropper", "Neuwirth"), C.thresh = 0.75, N.thresh = c(1, 1.28, 1.645), series.thresh = 75, make.plot = FALSE)

`data` |
a |

`period` |
a |

`window` |
an |

`method.thresh` |
a |

`C.thresh` |
a (positive) |

`N.thresh` |
a |

`series.thresh` |
a |

`make.plot` |
a |

The function locally z-transforms tree growth in year

within a symmetric moving window of `i`

years. For `n``method.thresh`

`"Cropper"`

, event years are defined as those years having absolute Cropper values (Ct) above a specified threshold (defaults to |Ct| > 0.75). For `method.thresh`

`"Neuwirth"`

, three classes of distinct growth deviations can be defined, being 'weak', 'strong' and 'extreme' (defaults to |Ct| > 1, |Ct| > 1.28, and |Ct| > 1.645). The window size can be adjusted (defaults to 13 years), as well as the minimum percentage of trees that should display a positive (or negative) event year for that year to be considered as positive (or negative) pointer year (defaults to 75%).

Note that the resulting time series are truncated by

at both ends inherent to the calculation method.
`(window-1)/2`

The function returns a `list`

containing the following components:

for

`method.thresh`

`"Cropper"`

:

`Cvalues` |
a |

`EYvalues` |
a |

`out` |
a |

`spec.param` |
a |

for

`method.thresh`

`"Neuwirth"`

:

`Cvalues` |
a |

`EYvalues` |
a |

`out` |
a |

`spec.param` |
a |

Marieke van der Maaten-Theunissen and Ernst van der Maaten.

Cropper, J.P. (1979) Tree-ring skeleton plotting by computer. *Tree-Ring Bulletin* 39: 47-59.

Jetschke, G., van der Maaten, E. and van der Maaten-Theunissen, M. (2019) Towards the extremes: A critical analysis of pointer year detection methods. *Dendrochronologia* 53: 55-62.

Fritts, H.C. (1976) Tree rings and climate. Academic Press Inc. (London) Ltd.

Neuwirth, B., Schweingruber, F.H. and Winiger, M. (2007) Spatial patterns of central European pointer years from 1901 to 1971. *Dendrochronologia* 24: 79-89.

Schweingruber, F.H., Eckstein, D., Serre-Bachet, F. and BrÃ¤ker, O.U. (1990) Identification, presentation and interpretation of event years and pointer years in dendrochronology. *Dendrochronologia* 8: 9-38.

## Calculate pointer years on detrended tree-ring series using method.thresh "Cropper" ## and a user-defined threshold for event-year definition of 1 data(s033) detr_s033 <- detrend(s033, method = "Spline", nyrs = 30) pyc <- pointer.norm(detr_s033, method.thresh = "Cropper", C.thresh = 1) head(pyc$out) ## Calculate pointer years on detrended tree-ring series using method.thresh "Neuwirth" ## and a user-defined window of 11 years data(s033) detr_s033 <- detrend(s033, method = "Spline", nyrs = 30) pyn <- pointer.norm(detr_s033, window = 11, method.thresh = "Neuwirth", make.plot = TRUE) head(pyn$out)

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