numOSL (version 2.0)

calED: Equivalent dose calculation and error assessment

Description

Calculating an equivalent dose and assessing its standard error.

Usage

calED(Curvedata, Ltx, model = "exp", origin = FALSE,  ErrorMethod = "mc", nsim = 1000, weight = TRUE,  plot = TRUE)

Arguments

Curvedata

matrix(required): a three-column matrix (i.e., regenerative doses, standardised regenerative dose signals, and measurement errors of standardised regenerative dose signals)

Ltx

vector(required): a two-element vector consisting of standardised natural dose signal and its measurement error

model

character(with default): model used for growth curve fitting, see fitGrowth for available models

origin

logical(with default): force the fitted growth curve to pass the origin or not

ErrorMethod

character(with default): method used for assessing the standard error of an equivalent dose, see details

nsim

integer(with default): number of Monte Carlo simulations used for equivalent dose error estimation if ErrorMethod="mc"

weight

logical(with default): perform growth curve fitting using a weighted or an un-weighted procedure, see function fitGrowth for details

plot

logical(with default): plot output or not

Value

Return a list that contains the following elements:

mcED

simulated random equivalent doses if ErrorMethod="mc"

LMpars

optimized parameters for the fitted growth curve

value

minimized objective for the fitted growth curve

fit.value

observed standardised regenerative dose signals and fitted counterparts

ED

calculated equivalent dose and standard error

Details

Function calED is used for calculating an equivalent dose and assessing its standard error. The standard errors of an equivalent dose can be assessd using two methods outlined by Duller (2007): (1) "sp": a simple transformation of the standard error of the standardised natural dose signal to the standard error of an equivalent dose; (2) "mc": a Monte Carlo method (also called a "parametric bootstrap" protocol). Interpolation is performed using a combination of golden section search and successive parabolic interpolation (R function optimize in package stats) (freely available Fortran 77 source code at http://www.netlib.org/fmm/fmin.f). See function fitGrowth for more details on growth curve fitting.

References

Duller GAT, 2007. Assessing the error on equivalent dose estimates derived from single aliquot regenerative dose measurements. Ancient TL, 25(1): 15-24.

Further reading

Galbraith RF, Roberts RG, 2012. Statistical aspects of equivalent dose and error calculation and display in OSL dating: an overview and some recommendations. Quaternary Geochronology, 11: 1-27.

See Also

analyst; calSARED; calSGCED; fastED; fitGrowth

Examples

  Curvedata<-cbind(c(0, 18, 36, 54, 72, 0, 18),               
                   c(0.026, 1.55, 2.39, 3.46, 4.13, 0.023, 1.61),  
                   c(0.005, 0.11, 0.27, 0.22, 0.20, 0.008, 0.24))                         
  Ltx<-c(3.1,0.31)
  calED(Curvedata, Ltx, model="exp", origin=FALSE)