Luminescence (version 0.3.4)

calc_OSLLxTxRatio: Calculate Lx/Tx ratio for CW-OSL curves.

Description

Calculate Lx/Tx ratios from a given set of CW-OSL curves.

Usage

calc_OSLLxTxRatio(Lx.data, Tx.data, signal.integral, background.integral, 
    background.count.distribution = "non-poisson", sigmab)

Arguments

Lx.data

data.frame (required): requires a CW-OSL shine down curve (x = time, y = counts)

Tx.data

data.frame (optional): requires a CW-OSL shine down curve (x = time, y = counts). If no input is given the Tx.data will be treated as NA and no Lx/Tx ratio is calculated.

signal.integral

vector (required): vector with the limits for the signal integral.

background.integral

vector (required): vector with the bounds for the background integral.

background.count.distribution

character (with default): Sets the count distribution assumed for the error calculation. Possible arguments poisson or non-poisson. See details for further information

sigmab

numeric (optional): Option to set a manual value for the overdispersion, used for the Lx/Tx error calculation. The value should be provided as absolute squared count values.

Value

• Returns an S4 object of type RLum.Results. Slot data contains a list with the following structure: $ LnLx $ LnLx.BG $ TnTx $ TnTx.BG $ Net_LnLx $ Net_LnLx.Error $ Net_TnTx.Error $ LxTx $ LxTx.Error

Function version

0.4.1 (2014-04-13 14:28:43)

Details

The integrity of the chosen values for the signal and background integral is checked by the function; the signal integral limits have to be lower than the background integral limits. If a vector is given as input instead of a data.frame, an artificial data.frame is produced. The error calculation is done according to Galbraith (2002). background.count.distribution This argument allows selecting the distribution assumption that is used for the error calculation. According to Galbraith (2002, 2014) the background counts may be overdispersed (i.e. do not follow a poisson distribution, which is assumed for the photomultiplier counts). In that case (might be the normal case) it has to be accounted for the overdispersion by estimating $\sigma^2$ (i.e. the overdispersion value). Therefore the relative standard error is calculated as: (a) poisson $$rse(\mu_{S}) \approx \sqrt(Y_{0} + Y_{1}/k^2)/Y_{0} - Y_{1}/k$$ (b) non-poisson $$rse(\mu_{S}) \approx \sqrt(Y_{0} + Y_{1}/k^2 + \sigma^2(1+1/k))/Y_{0} - Y_{1}/k$$

References

Duller, G., 2007. Analyst. http://www.nutech.dtu.dk/english/~/media/Andre_Universitetsenheder/Nutech/Produkter%20og%20services/Dosimetri/radiation_measurement_instruments/tl_osl_reader/Manuals/analyst_manual_v3_22b.ashx Galbraith, R.F., 2002. A note on the variance of a background-corrected OSL count. Ancient TL, 20 (2), 49-51. Galbraith, R.F., 2014. A further note on the variance of a background-corrected OSL count. Submitted to Ancient TL.

See Also

Analyse_SAR.OSLdata, plot_GrowthCurve, analyse_SAR.CWOSL

Examples

##load data
data(ExampleData.LxTxOSLData, envir = environment())

##calculate Lx/Tx ratio
results <- calc_OSLLxTxRatio(Lx.data, Tx.data, signal.integral = c(1:2), 
                             background.integral = c(85:100))

##get results object
get_RLum.Results(results)