Determines zone boundaries for single influx records using the change-point analysis.

NOTE: as of February 2023, this stand-alone function has been included in the 'tapas' package. See https://github.com/wfinsinger/tapas for further details.

In summary:

The function uses records that were previously interpolated to constant temporal resolution. It determines change points in the proxy AR record (e.g. charcoal-accumulation rates (CHAR) or pollen-accumulation rates (PAR)).

Rationale:

Because proxyAR (# cm-2 yr-1) = proxyConcentration (# cm-3) * sediment-accumulation rates (cm yr-1),

the sediment-accumulation rates might strongly influence proxyAR records.

To test this, the proxy.cpt() function generates iteratively a series of random concentration records, calculates for each of those records accumulation rates (hereafter randomAR records) using the information from the depth-age model, and then looks at change points in the randomAR records.

The assumption is that if change points appear both in the original proxyAR and in the randomAR records, then the influence of the sediment-accumulation rates is probably not negligible.

-- The function requires one input file with seven columns. The input file can, for instace, be generated using the pretreatment() function (as from the paleofire package):

Column 1: CmI = top sample depth, interpolated

Column 2: AgeI = top sample age (as cal yrs BP), interpolated

Column 3: sedArI = sediment accumulation rate as from age-depth model (as cm year-1), interpolated

Column 4: countI = raw data (counts, areas, other)

Column 5: volI = sample volume, interpolated

Column 6: ConcI = concentration (as pieces cm-3), interpolated

Column 7: ArI = influx (or accumulation rate) value (as pieces cm-2 yr-1), interpolated

-- The function requires following parameters (which can be left at default values, except "Name", that needs an input text)

Name = Analysis name (can be name of the site, or that + parameters chosen)

bootstrap = if TRUE, the random concentration record is generated by random sampling with replacement from ConcI; if FALSE the random numbers (rand.i) are generated with the following constrain: min(ConcI) < rand.i < max(ConcI)

q = number of random datasets generated

n.Q = the maximum number of change points

n.screen = a change point in the random datasets is validated if it occurs in more than n.screen datasets.

By default n.screen = q * 0.025 (thus with q=1000 this equals 2.5% chance of occurrence)

Run the cpt_v12_example.r script to see an example application

Suggested citation: Finsinger W., Magyari E.K., Fevre J., Orban I., Pal I., Vincze I., Hubay K, Birks H.H., Braun M., Toth M. (2016) – Holocene fire regimes near the treeline in the Retezat Mts. (Southern Carpathians). Quaternary International. doi: 10.1016/j.quaint.2016.04.029. In press (http://dx.doi.org/10.1016/j.quaint.2016.04.029)