Re-scaling sensitivity-corrected natural-dose signals according to the "global standardised growth curve" (gSGC) method suggested by Li et al. (2015, 2016).
scaleSGCN(obj_analyseBIN, SGCpars, model, origin,
SAR.Cycle, Tn.above.3BG = TRUE,
TnBG.ratio.low = NULL, rseTn.up = NULL,
FR.low = NULL, use.se = TRUE, outfile = NULL)
Return an invisible list that contains the following elements:
scaled natural-dose signals and associated standard errors
aliquot (grain) ID of scaled natural-dose signals
list(required): an object of S3 class "analyseBIN" produced by
function analyseBINdata or as_analyseBIN
vector(required): optimized parameters of the SGC obtained using function fitGrowth or lsNORM
character(required): fitting model used for obtaining SGCpars
logical(required): logical value indicating if established SGC passes the origin
character(required): a two-element character vector containing SAR cycles used for
natural-dose signal re-scaling. Example: SAR.Cycle=c("N","R3")
logical(with default): logical value indicating if aliquot (grain) with Tn below 3 sigma BG should be rejected
numeric(optional): lower limit on ratio of initial Tn signal to BG
numeric(optional): upper limit on relative standard error of Tn in percent
numeric(optional): lower limit on fast ratio of Tn
logical(with default): logical value indicating if standard errors of values should be used during application of rejection criteria
character(optional): if specified, scaled SGC data related quantities will be written to a CSV file named "outfile"
and saved to the current work directory
Sensitivity-corrected natural-dose signals are re-scaled according to Eqn.(10) of Li et al. (2015).
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.
lsNORM; calSGCED
# Not run.
data(SARdata)
gSGCpars <- c(137.440874251, 0.007997863, 2.462035263, -0.321536177)
scaleSGCN(as_analyseBIN(SARdata), SGCpars=gSGCpars, model="gok",
origin=FALSE, SAR.Cycle=c("N","R3"))
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