glk function

Calculate

This function calculates the and related measures for a given set of tree-ring records. UTF-8

glk(x, overlap = 50, prob = TRUE)
  glk.legacy(x)

Arguments

• x: a data.frame of tree-ring data with records in columns, and years as rows.
• overlap: integer value with minimal length of overlapping growth changes (compared number of tree rings - 1). Comparisons with less overlap are not compared.
• prob: if TRUE then the probability of exceedence of the will be calculated

Details

is a classical agreement test based on sign tests (Eckstein and Bauch, 1969). This function implements as the pairwise comparison of all records in data set. This vectorized implementation is faster than the previous version and follows the original definition (Huber 1942), instead of the incorrect interpretation that has been used in the past (Schweingruber 1988, see Buras/Wilmking 2015 for the correction).

The probability of exceedence (p) for the expresses the chance that the is incorrect. The observed value of the is converted to a z-score and based on the standard normal curve the probability of exceedence is calculated. The result is a matrix of all p-values (Jansma 1995, 60-61, see also Visser 2020).

Note that prior to dplR version 1.7.2, glk did not have the overlap or prob and returned a matrix with just the for all possible combinations of records. That function can still be accessed via glk.legacy.

Returns

The funtions returns a named list of two or three matrices (p_mat is optional if prob = TRUE):

1. glk_mat: matrix with
2. overlap: matrix with number of overlapping growth changes.This is the number of overlapping years minus one.
3. p_mat: matrix of all probabilities of exceedence for all observed values.

The matrices can be extracted from the list by selecting the name or index number. If two curves have less than 3 years of overlap, cannot be computed, and NA is returned. To calculate the global glk of the dataset mean(x$glk_mat, na.rm = TRUE).

Author(s)

Christian Zang. Patched and improved by Mikko Korpela. Improved by Allan Buras. Further improved and expanded by Ronald Visser and Andy Bunn

References

Buras, A. and Wilmking, M. (2015) Correcting the calculation of Gleichläufigkeit, Dendrochronologia 34 , 29-30. DOI: https://doi.org/10.1016/j.dendro.2015.03.003

Eckstein, D. and Bauch, J. (1969) Beitrag zur Rationalisierung eines dendrochronologischen Verfahrens und zur Analyse seiner Aussagesicherheit. Forstwissenschaftliches Centralblatt, 88 (1), 230-250.

Huber, B. (1943) Über die Sicherheit jahrringchronologischer Datierung. Holz als Roh- und Werkstoff 6 , 263-268. DOI: https://doi.org/10.1007/BF02603303

Jansma, E., 1995. RemembeRINGs; The development and application of local and regional tree-ring chronologies of oak for the purposes of archaeological and historical research in the Netherlands, Nederlandse Archeologische Rapporten 19, Rijksdienst voor het Oudheidkundig Bodemonderzoek, Amersfoort

Schweingruber, F. H. (1988) Tree rings: basics and applications of dendrochronology, Kluwer Academic Publishers, Dordrecht, Netherlands, 276 p.

Visser, R.M. (2020) On the similarity of tree-ring patterns: Assessing the influence of semi-synchronous growth changes on the for big tree-ring data sets,Archaeometry, 63 , 204-215 DOI: https://doi.org/10.1111/arcm.12600

See Also

sgc sgc is an alternative for glk)

Examples

library(utils)
  data(ca533)
  ca533.glklist <- glk(ca533)
  ca533.glk_mat <- ca533.glklist$glk_mat
  # calculating the mean GLK including self-similarities
  mean(ca533.glk_mat, na.rm = TRUE) 
  # calculating the mean GLK excluding self-similarities
  mean(ca533.glk_mat[upper.tri(ca533.glk_mat)], na.rm = TRUE)