calSGCED: SGC Equivalent dose calculation and selection

In numOSL: Numeric Routines for Optically Stimulated Luminescence Dating

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")

numOSL documentation built on Sept. 18, 2023, 9:07 a.m.