Luminescence (version 0.8.6)

calc_TLLxTxRatio: Calculate the Lx/Tx ratio for a given set of TL curves [beta version]

Description

Calculate Lx/Tx ratio for a given set of TL curves.

Usage

calc_TLLxTxRatio(Lx.data.signal, Lx.data.background = NULL,
  Tx.data.signal, Tx.data.background = NULL, signal.integral.min,
  signal.integral.max)

Arguments

Lx.data.signal

'>RLum.Data.Curve or data.frame (required): TL data (x = temperature, y = counts) (TL signal)

Lx.data.background

'>RLum.Data.Curve or data.frame (optional): TL data (x = temperature, y = counts). If no data are provided no background subtraction is performed.

Tx.data.signal

'>RLum.Data.Curve or data.frame (required): TL data (x = temperature, y = counts) (TL test signal)

Tx.data.background

'>RLum.Data.Curve or data.frame (optional): TL data (x = temperature, y = counts). If no data are provided no background subtraction is performed.

signal.integral.min

integer (required): channel number for the lower signal integral bound (e.g. signal.integral.min = 100)

signal.integral.max

integer (required): channel number for the upper signal integral bound (e.g. signal.integral.max = 200)

Value

Returns an S4 object of type '>RLum.Results. Slot data contains a list with the following structure:

$ LxTx.table  
.. $ LnLx  
.. $ LnLx.BG  
.. $ TnTx  
.. $ TnTx.BG
.. $ Net_LnLx  
.. $ Net_LnLx.Error

Function version

0.3.2 (2018-01-21 17:22:38)

How to cite

Kreutzer, S., Schmidt, C. (2018). calc_TLLxTxRatio(): Calculate the Lx/Tx ratio for a given set of TL curves [beta version]. Function version 0.3.2. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J. (2018). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.8.6. https://CRAN.R-project.org/package=Luminescence

Details

Uncertainty estimation

The standard errors are calculated using the following generalised equation:

$$SE_{signal} <- abs(Signal_{net} * BG_f /BG_{signal}$$

where \(BG_f\) is a term estimated by calculating the standard deviation of the sum of the \(L_x\) background counts and the sum of the \(T_x\) background counts. However, if both signals are similar the error becomes zero.

See Also

'>RLum.Results, analyse_SAR.TL

Examples

# NOT RUN {
##load package example data
data(ExampleData.BINfileData, envir = environment())

##convert Risoe.BINfileData into a curve object
temp <- Risoe.BINfileData2RLum.Analysis(TL.SAR.Data, pos = 3)


Lx.data.signal <- get_RLum(temp, record.id=1)
Lx.data.background <- get_RLum(temp, record.id=2)
Tx.data.signal <- get_RLum(temp, record.id=3)
Tx.data.background <- get_RLum(temp, record.id=4)
signal.integral.min <- 210
signal.integral.max <- 230

output <- calc_TLLxTxRatio(Lx.data.signal,
                           Lx.data.background,
                           Tx.data.signal, Tx.data.background,
                           signal.integral.min, signal.integral.max)
get_RLum(output)

# }