calSGCED {numOSL}

SGC Equivalent dose calculation and selection

Description

Calculating and selecting equivalent doses in a batch model according to the "standardised growth curve" (SGC) method suggested by Roberts and Duller (2004) or the "global standardised growth curve" (gSGC) method suggested by Li et al. (2015, 2016).

Usage

calSGCED(obj_analyseBIN, SGCpars, model, origin, avgDev, 
         method = "SGC", SAR.Cycle = "N", errMethod = "sp", 
         Tn.above.3BG = TRUE, TnBG.ratio.low = NULL, 
         rseTn.up = NULL, FR.low = NULL, rseED.up = NULL, 
         use.se = TRUE, outpdf = NULL, outfile = NULL)

Arguments

obj_analyseBIN

list(required): an object of S3 class "analyseBIN" produced by
function analyseBINdata or as_analyseBIN

SGCpars

vector(required): optimized parameters of the SGC obtained using function lsNORM (or fitGrowth)

model

character(required): fitting model used for obtaining SGCpars

origin

logical(required): logical value indicating if established SGC passes the origin

avgDev

numeric(required): average deviation (i.e., average fit error) of the SGC obtained using function fitGrowth or lsNORM. This quantity stands for the uncertainty of established SGC when assessing the equivalent dose error using the simple transformaion method

method

character(with default): method used for equivalent dose calculation, i.e.,
method="SGC" (for the original SGC method) or method="gSGC" (for the improved SGC method)

SAR.Cycle

character(with default): SAR cycles used for SGC equivalent dose calculation.
Example: SAR.Cycle=c("N","R3")

errMethod

character(with default): method used for equivalent dose error assessment

Tn.above.3BG

logical(with default): logical value indicating if aliquot (grain) with Tn below 3 sigma BG should be rejected

TnBG.ratio.low

numeric(optional): lower limit on ratio of initial Tn signal to BG

rseTn.up

numeric(optional): upper limit on relative standard error of Tn in percent

FR.low

numeric(optional): lower limit on fast ratio of Tn

rseED.up

numeric(optional): upper limit on the relative standard error of equivalent dose in percent

use.se

logical(with default): logical value indicating if standard errors of values should be used during application of rejection criteria

outpdf

character(optional): if specified, results of SGC equivalent dose calculation will be written to a PDF file named "outpdf" and saved to the current work directory

outfile

character(optional): if specified, SGC equivalent doses related quantities will be written to a CSV file named "outfile" and saved to the current work directory

Value

Return an invisible list that contains the following elements:

scale.Ltx

scaled standardised natural-dose signals and associated standard errors used for SGC equivalent dose calculation. Note that standardised natural-dose signals will remain un-scaled if method="SGC"

sgcED

calculated SGC equivalent doses

ConfInt

68 percent (one sigma) and 95 percent (two sigma) confidence intervals of SGC equivalent doses

agID

aliquot (grain) ID of calculated (selected) SGC equivalent doses

summary.info

a summary of the SGC equivalent dose calculation

References

Li B, Roberts RG, Jacobs Z, Li SH, 2015. Potential of establishing a "global standardised growth curve" (gSGC) for optical dating of quartz from sediments. Quaternary Geochronology, 27: 94-104.

Li B, Jacobs Z, Roberts RG, 2016. Investigation of the applicability of standardised growth curves for OSL dating of quartz from Haua Fteah cave, Libya. Quaternary Geochronology, 35: 1-15.

Roberts HM, Duller GAT, 2004. Standardised growth curves for optical dating of sediment using multiple-grain aliquots. Radiation Measurements, 38(2): 241-252.

See Also

fitGrowth; lsNORM; SARdata; scaleSGCN; calED; calSARED

Examples

 data(SARdata)
 ### (1) gSGC ED calculation:
 ### gSGCpars were obtained using function "lsNORM".
 gSGCpars <- c(137.440874251, 0.007997863, 2.462035263, -0.321536177)
 avg.error2 <- 0.1111623
 res <- calSGCED(as_analyseBIN(SARdata), gSGCpars, method="gSGC", 
                 model="gok", origin=FALSE, avgDev=avg.error2,
                 SAR.Cycle=c("N","R3"))
 print(res$sgcED)

 ### (2) SGC ED calculation (not run): 
 ### SGCpars were obtained using function "fitGrowth".
 # SGCpars <- c(183.474322547,  0.007038048,  4.254287622, -0.337734151)
 # avg.error <- 0.3156259
 # calSGCED(as_analyseBIN(SARdata), SGCpars, method="SGC", model="gok", 
 #          origin=FALSE, avgDev=avg.error, SAR.Cycle="N", outpdf="SGCED")

 ### (3) gSGC ED calculation and signal-related 
 ###     rejection criteria application (not run):
 # data(BIN)
 # res_pickBIN <-pickBINdata(BIN, LType="OSL")
 # res_analyseBIN <- analyseBINdata(res_pickBIN, nfchn=4, nlchn=30)
 # res_lsNORM <- lsNORM(res_analyseBIN$SARdata, model="gok", origin=FALSE)

 # calSGCED(res_analyseBIN, SGCpars=res_lsNORM$LMpars2[,1], 
 #         model="gok", origin=FALSE, avgDev=res_lsNORM$avg.error2,
 #         method="gSGC", SAR.Cycle=c("N","R3"), Tn.above.3BG=TRUE, 
 #         TnBG.ratio.low=4, rseTn.up=10, outpdf="foo", outfile="foo")