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Sliding window crossdating analysis using multiple statistical methods

Source: R/trs_crossdate.R
trs_crossdate.Rd

This function performs crossdating analysis using a sliding window with multiple statistical methods: Parallel Variation (GLK/SGC), Baillie & Pilcher t-values, Hollstein t-values, and standard Pearson correlation t-values. It tests series at different temporal lags to find a potential date positions.

Usage

trs_crossdate(
  x,
  y = NULL,
  min_overlap = 30,
  sliding = TRUE,
  top_n = NULL,
  rank_by = NULL,
  pb = TRUE
)

Arguments

x

A data frame of test tree-ring series in rwl format (years as rownames, series as columns). All columns must be numeric.

y

A data frame of reference tree-ring series in rwl format. If NULL, uses x for self-comparison. Default is NULL.

min_overlap

Integer. Minimum number of overlapping years required between series pairs to compute statistics. Must be >= 3. Default is 30.

sliding

Logical. If TRUE, performs sliding window analysis testing multiple temporal lags. If FALSE, tests only at lag 0 (contemporary positioning). Default is TRUE.

top_n

Integer or NULL. If specified, returns only the top n matches per test series (after optional sorting by rank_by). Default is NULL.

rank_by

Character or NULL. Statistic to rank by when sorting results per test series. If NULL, no additional sorting is applied beyond default ordering. Options are "r_pearson", "t_St", "t_BP", "t_Ho", "sgc", "ssgc", "glk". Default is NULL.

pb

Logical. If TRUE (default), displays a progress bar during computation. Set to FALSE to suppress progress bar output, useful when calling the function programmatically or in non-interactive sessions.

Value

A data frame with crossdating results containing the following columns:

series

Name of the test series from x

length

Length of the test series

first

Date of the first ring at the particular lag position

last

Date of the last ring at the particular lag position

reference

Name of the reference series

ref_first

Date of the first ring in the reference series

ref_last

Date of the last ring in the reference series

overlap

Number of overlapping years between series and reference

r_pearson

Pearson correlation coefficient

t_St

Student's t-statistic

t_BP

Baillie & Pilcher t-statistic

t_Ho

Hollstein t-statistic

sgc

Synchronous Growth Change (SGC) statistic

ssgc

Semi-SGC statistic

sgc_p

SGC p-value

glk

Gleichläufigkeit (GLK) statistic

glk_p

GLK p-value

Details

This function implements sliding window crossdating, a fundamental technique in dendrochronology for dating wood samples of unknown age. The process involves:

  1. Determining the range of possible lags based on series overlap

  2. For each lag, shifting the test series temporally

  3. Computing all crossdating statistics

  4. Returning results for each tested position

The sliding window approach allows identification of the temporal position where test and reference series show maximum similarity, which indicates the most likely dating for the test series.

Statistical Methods:

  • t_BP: t-values according to the Baillie & Pilcher (1973) algorithm

  • t_Ho: t-values according to the Hollstein (1980) algorithm

  • sgc, sgc_p: synchronous growth change and associated significance level (Visser 2020)

  • ssgc: semi-synchronous growth change (Visser 2020)

  • glk, glk_p: 'Gleichläufigkeit' or 'percentage of parallel variation' (Buras & Wilmking 2015; Eckstein & Bauch 1969; Huber 1942), and significance level (p-value) associated with the glk-value (Jansma 1995)

  • r_pearson: the Pearson's correlation coefficient

  • t_St: Student's t-value

Sorting and Filtering:

  • If rank_by = NULL: Results are returned with default ordering (series, reference, first)

  • If rank_by is specified: Results are sorted per test series by the chosen statistic (high to low)

  • If top_n is specified: Only the top n matches per test series are returned (after optional sorting)

References

  • Baillie, M.G.L., Pilcher, J.R. (1973) A simple crossdating program for tree-ring research. Tree-Ring Bulletin 33, 7–14. http://hdl.handle.net/10150/260029

  • Buras, A. and Wilmking, M. (2015) Correcting the calculation of Gleichläufigkeit, Dendrochronologia 34, 29-30. 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. https://doi.org/10.1007/BF02603303

  • Hollstein E. (1980) Mitteleuropäische Eichenchronologie. Trierer dendrochronologische Forschungen zur Archäologie und Kunstgeschichte, Trierer Grabungen und Forschungen 11, Mainz am Rhein.

  • 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. https://dspace.library.uu.nl/handle/1874/45149

  • 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 Gleichläufigkeit for big tree-ring data sets, Archaeometry 63, 204-215. https://doi.org/10.1111/arcm.12600

See also

trs_pv, trs_tbp, trs_tho

Examples

if (FALSE) { # \dontrun{
# Create sample data
rwl_test <- trs_pseudo_rwl(n_series = 2, series_length = 80, end_date = 1950)
rwl_ref <- trs_pseudo_rwl(n_series = 3, series_length = 150, end_date = 2000)

# Full sliding window analysis with default ordering
result <- trs_crossdate(rwl_test, rwl_ref, min_overlap = 30)

# Sort by Hollstein t-values
result_sorted <- trs_crossdate(rwl_test, rwl_ref, min_overlap = 30, rank_by = "t_Ho")

# Contemporary positioning only
contemp <- trs_crossdate(rwl_test, rwl_ref, sliding = FALSE)

# Self-comparison for quality control
self_check <- trs_crossdate(rwl_ref, sliding = FALSE)

# Get top 3 matches per series ranked by Hollstein t-values
top_matches <- trs_crossdate(rwl_test, rwl_ref, top_n = 3, rank_by = "t_Ho")
} # }