R: Fast-component equivalent dose calculation

fastED {numOSL}

R Documentation

Fast-component equivalent dose calculation

Description

Estimating a fast-, medium-, or slow-component equivalent dose using decay curves obtained from the single aliquot regenerative-dose (SAR) method.

Usage

fastED(Sigdata, Redose, delay.off = c(0,0), ncomp = 2, 
       constant = TRUE, compIDX = 1, control.args = list(), 
       typ = "cw", model = "gok", origin = FALSE, errMethod = "sp", 
       nsim = 500, weight.decomp = FALSE, weight.fitGrowth = TRUE, 
       trial = TRUE, nofit.rgd = NULL, outpdf = NULL, log = "x", 
       lwd = 2, test.dose = NULL, agID = NULL)

Arguments

`Sigdata`	matrix(required): a series of decay curves stored in a matrix column by column, the first column denotes stimulation time values, see details. Data structure of this kind can be obtained using function pickBINdata by setting argument `force.matrix=TRUE`, see examples
`Redose`	vector(required): regenerative dose values. Example: `Redose=c(1,2,3,4,0,1)`
`delay.off`	vector(with default): a two-elment vector indicating the "Delay" and "Off" values of the decay curves, i.e., `delay.off[1]=Delay`,`delay.off[2]=Off`
`ncomp`	integer(with default): number of components to be decomposed
`constant`	logical(with default): logical value indicating if a constant background should be subtracted from the decay curve, see function decomp for details
`compIDX`	integer(with default): index of the component to be extracted. For example, `compIDX=1` and `compIDX=2` indicate respectively that the fast- and medium-component signals will be used to calculate the equivalent dose. The index should not exceed the number of components to be decomposed
`control.args`	list(with default): arguments used in the differential evolution algorithm, see function decomp for details
`typ`	character(with default): type of an OSL decay curve, only CW-OSL decay curve can be analyzed currently
`model`	character(with default): model used for growth curve fitting, see function fitGrowth for available models
`origin`	logical(with default): logical value indicating if the growth curve should be forced to pass the origin
`errMethod`	character(with default): method used for equivalent dose error assessment. See function calED for details
`nsim`	integer(with default): desired number of randomly simulated equivalent dose obtained by Monte Carlo simulation
`weight.decomp`	character(with default): logical value indicating if the decay curve should be fitted using a weighted procedure, see function decomp for details
`weight.fitGrowth`	character(with default): logical value indicating if the growth curve should be fitted using a weighted procedure, see function fitGrowth for details
`trial`	logical(with default): logical value indicating if the growth curve should be fitted using other models if the given model fails, see function fitGrowth for details
`nofit.rgd`	integer(optional): regenerative doses that will not be used during the fitting. For example, if `nofit.rgd=1` then the first regenerative dose will not be used during fast-, medium-, or slow-component growth curve fitting
`outpdf`	character(optional): if specified, results of fast-, medium-, or slow-component equivalent dose calculation will be written to a PDF file named `"outpdf"` and saved to the current work directory
`log`	character(with default): a character string which contains "x" if the x axis is to be logarithmic, "y" if the y axis is to be logarithmic and "xy" or "yx" if both axes are to be logarithmic
`lwd`	numeric(with default): width of curves (lines)
`test.dose`	numeric(optional): test dose of decay curves
`agID`	vector(optional): a three-elemenet vector indicating aliquot (grain) ID, i.e., `agID[1]=NO`, `agID[2]=Position`, `agID[3]=Grain`

Details

Function fastED is used to estimate a fast-, medium-, or slow-component equivalent dose using data sets obtained from the SAR protocol (Murray and Wintle, 2000). The routine trys to decompose a series of decay curves to a specified number of components, then the numbers of trapped electrons from the fast-, medium-, or slow-component will be used to construct the growth curve to estimate a fast-, medium-, or slow-component equivalent dose. See function decomp, fitGrowth, and calED for more details concerning decay curve decomposition, growth curve fitting, and equivalent dose calculation, respectively.

Argument Sigdata is a column-matrix made up with stimulation time values and a number of decay curves:

Column.no	Description
`I`	Stimulation time values
`II`	Natural-dose signal values
`III`	Test-dose signal values for the natural-dose
`IV`	The 1th Regenerative-dose signal values
`V`	Test-dose signal values for the 1th regenerative-dose
`VI`	The 2th regenerative-dose signal values
`VII`	Test-dose signal values for the 2th regenerative-dose
`...`	...

Value

Return an invisible list containing the following elements:

`decomp.pars`	a list containing optimized parameters of successfully fitted decay curves
`Curvedata`	data sets used for building the fast-, medium-, or slow-component growth curve
`Ltx`	sensitivity-corrected natural-dose fast-, medium-, or slow-component signal and its standard error
`LMpars`	optimizaed parameters for the fast-, medium-, or slow-component growth curve
`value`	minimized objective for the fast-, medium-, or slow-component growth curve
`avg.error`	average fit error for the fast-, medium-, or slow-component growth curve
`RCS`	reduced chi-square value for the fast-, medium-, or slow-component growth curve
`FOM`	figure of merit value for the fast-, medium-, or slow-component growth curve in percent
`calED.method`	method used for calculating the fast-, medium-, or slow-component equivalent dose, i.e., `"Interpolation"` or `"Extrapolation"`
`mcED`	randomly simulated fast-, medium-, or slow-component equivalent doses
`ED`	fast-, medium-, or slow-component equivalent dose and its standard error
`ConfInt`	68 percent and 95 percent confidence interval of the fast-, medium-, or slow-component equivalent dose
`RecyclingRatio1`	the first fast-, medium-, or slow-component recycling ratio and its standard error
`RecyclingRatio2`	the second fast-, medium-, or slow-component recycling ratio and its standard error
`RecyclingRatio3`	the third fast-, medium-, or slow-component recycling ratio and its standard error
`Recuperation1`	the first fast-, medium-, or slow-component recuperation (i.e., ratio of the sensitivity-corrected zero-dose signal to natural-dose signal) and its standard error in percent
`Recuperation2`	the second fast-, medium-, or slow-component recuperation (i.e., ratio of the sensitivity-corrected zero-dose signal to the maximum regenerative-dose signal) and its standard error in percent

Note

Argument test.dose and agID have nothing to do with fast-, medium-, or slow-component equivalent dose calculation. They are used only for plotting purpose.

The number of trapped electrons that corresponds to the largest, the second largest, and the third largest decay rates will be regarded as the fast-, medium-, and slow-component signals, respectively, which cannot always ensure that pure fast-, medium-, or slow-component signals be extracted if an ultra-fast decaying component appears.

The authors thank Professor Sheng-Hua Li and Professor Geoff Duller for their helpful discussions concerning fast-component equivalent dose calculation.

References

Li SH, Li B, 2006. Dose measurement using the fast component of LM-OSL signals from quartz. Radiation Measurements, 41(5): 534-541.

Murray AS, Wintle AG, 2000. Luminescence dating of quartz using improved single-aliquot regenerative-dose protocol. Radiation Measurements, 32(1): 57-73.

Examples

 ### Example 1 (not run):
 # data(Signaldata)
 # fastED(Signaldata$cw,Redose=c(80,160,240,320,0, 80)*0.13,
 #        ncomp=3, constant=FALSE, compIDX=1, outpdf="fastED1")

 # fastED(Signaldata$cw,Redose=c(80,160,240,320,0, 80)*0.13,
 #        ncomp=3, constant=FALSE, compIDX=2, outpdf="mediumED1")

 # fastED(Signaldata$cw,Redose=c(80,160,240,320,0, 80)*0.13,
 #        ncomp=3, constant=FALSE, compIDX=3, outpdf="slowED1")

 ### Example 2 (not run):
 # data(BIN)
 # obj_pickBIN <- pickBINdata(BIN, Position=6, Grain=0, 
 #                            LType="OSL", force.matrix=TRUE)
 # fastED(obj_pickBIN$BINdata[[1]], ncomp=2, constant=TRUE,
 #        Redose=c(100,200,300,400,0,100)*0.13, outpdf="fastED2")

[Package numOSL version 2.8 Index]