numOSL (version 1.2)

sgcED: Fitting a dose response curve and calculating a number of equivalent dose values using the standardised growth curves (SGC) method

Description

It is a function for fitting a dose response curve and calculating an equivalent dose values using the standardised growth curves (SGC) method after Roberts HM and Duller GAT (2004). It is based on the fact that using information collected as part of standard SAR measurements, it is possible to construct reproducible patterns of growth from multiple-grain aliquots of a large number of samples (Roberts HM and Duller GAT, 2004).

Usage

sgcED(Curvedata, Ltx, 
      model = c("line","exp","line+exp"), origin = FALSE,
      nstart = 100, upb = 1, ErrorMethod = c("mc","sp"),
      nsim = 1000, plot = TRUE, samplename = NULL, outfile = NULL)

Arguments

Curvedata

data.frame(required): three columns, see function calED

Ltx

data.frame(required): two columns (standardized signals and its standard errors)

model

character(with default): a fitting model("linear", "exponential" or "linear+exponential"), see function calED

origin

logical(optional): force the fitting to pass the origin (x=0,y=0) or not

nstart

numeric(with default): the maximum number of attempts used to initialize parameters when fitting a dose response curve, see function calED

upb

numeric(with default): the upper limit on b value, see function calED

ErrorMethod

character(with default): a method ("sp" or "mc") for estimating the standard errors of equivalent doses, see function calED

nsim

numeric(with default): the allowed maximum number of simulations if a Monte Carlo method is used for error assessing

plot

logical(with default): plot the results or not

samplename

character(optional): the name of the sample

outfile

character(optional): if specified, calculated equivalent doses will be written to a file named "outfile" in ".csv" format will and be saved to the current work directory

Value

• Return a visible list that contains following elements:
• LMpars
  the characteristic parameters and their standard errors of the fitted dose response curve
• residual
  the sum of square of residuals
• fit.value
  the fitted values
• ED
  the estimated equivalent doses and their standard errors

Details

see function calED() for details.

References

Roberts, H.M. and Duller, G.A.T., 2004. Standardised growth curves for optical dating of sediment using multiple-grain aliquots. Radiation Measurements 38(2), pp. 241-252.

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

Roberts, H.M., Durcan, J.A., Duller, G.A.T., 2009. Exploring procedures for the rapid assessment of optically stimulated luminescence range-finder ages. Radiation Measurements, 44(5-6), pp. 582-587.

Jorge More, Burton Garbow, Kenneth Hillstrom, User Guide for MINPACK-1, Technical Report ANL-80-74, Argonne National Laboratory, 1980.

Further reading

Long, H., Lai, Z.P., Fan, Q.S., Sun, Y.J., Liu, X.J., 2010. Applicability of a quartz OSL standardised growth curve for De determination up to 400 Gy for lacustrine sediments from the Qaidam Basin of the Qinghai-Tibetan Plateau. Quaternary Geochronology 5(2-3), pp. 212-217.

Yang, L.H., Lai, Z.P., Long, H., Zhang, J.R., 2011. Construction of a quartz OSL standardised growth curve (SGC) for aeolian samples from the Horqin dunefield in northeastern China. Geochronometria 38(4), pp. 391-396.

See Also

calED

Examples

ltx1<-c(0.032,1.61,2.55,3.21,3.87,0.031,1.55) # Lx/Tx for the first aliquot
 ltx2<-c(0.025,1.44,2.47,3.35,4.17,0.033,1.47) # Lx/Tx for the second aliquot
 ltx3<-c(0.027,1.51,2.68,3.52,4.41,0.021,1.39) # Lx/Tx for the third aliquot
 ltx4<-c(0.018,1.71,2.28,3.81,4.03,0.017,1.62) # Lx/Tx for the four aliquot
 ltx5<-c(0.026,1.49,1.99,3.43,4.17,0.015,2.01) # Lx/Tx for the five aliquot
 ltx<-cbind(ltx1,ltx2,ltx3,ltx4,ltx5)
 ltx<-cbind(apply(ltx,MARGIN=1,mean),
            apply(ltx,MARGIN=1,sd))  # means an standard deviations
 redose<-c(0,18,36,54,72,0,18)       # the same ReDose for the five aliquots
 Curvedata<-data.frame(redose,ltx)
 Ltx<-data.frame(c(0.5,1.0,1.8,2.3,2.8,3.1,3.6,4.0),
                 rep(0.1,8))         # Lx/Txs from which EDs are expected
 sgcED(Curvedata,Ltx,model="line+exp",origin=TRUE) # fitting y=a*(1-exp(-b*x))+c*x