View source: R/calc_OSLLxTxRatio.R
calc_OSLLxTxRatio | R Documentation |
Lx/Tx
ratio for CW-OSL curvesCalculate Lx/Tx
ratios from a given set of CW-OSL curves assuming late light
background subtraction.
calc_OSLLxTxRatio(
Lx.data,
Tx.data = NULL,
signal.integral,
signal.integral.Tx = NULL,
background.integral,
background.integral.Tx = NULL,
background.count.distribution = "non-poisson",
use_previousBG = FALSE,
sigmab = NULL,
sig0 = 0,
digits = NULL
)
Lx.data |
RLum.Data.Curve or data.frame (required): requires a CW-OSL shine down curve (x = time, y = counts) |
Tx.data |
RLum.Data.Curve or data.frame (optional):
requires a CW-OSL shine down curve (x = time, y = counts). If no
input is given the |
signal.integral |
numeric (required): vector with the limits for the signal integral.
Can be set to |
signal.integral.Tx |
numeric (optional):
vector with the limits for the signal integral for the |
background.integral |
numeric (required):
vector with the bounds for the background integral.
Can be set to |
background.integral.Tx |
numeric (optional):
vector with the limits for the background integral for the |
background.count.distribution |
character (with default):
sets the count distribution assumed for the error calculation.
Possible arguments |
use_previousBG |
logical (with default):
If set to |
sigmab |
numeric (optional):
option to set a manual value for the overdispersion (for |
sig0 |
numeric (with default):
allow adding an extra component of error to the final |
digits |
integer (with default):
round numbers to the specified digits.
If digits is set to |
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).
Please note: In cases where the calculation results in NaN
values (for
example due to zero-signal, and therefore a division of 0 by 0), these NaN
values are replaced by 0.
sigmab
The default value of sigmab
is calculated assuming the background is
constant and would not applicable when the background varies as,
e.g., as observed for the early light subtraction method.
sig0
This argument allows to add an extra component of error to the final Lx/Tx
error value. The input will be treated as factor that is multiplied with
the already calculated LxTx
and the result is add up by:
se(LxTx) = \sqrt(se(LxTx)^2 + (LxTx * sig0)^2)
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:
poisson
rse(\mu_{S}) \approx \sqrt(Y_{0} + Y_{1}/k^2)/Y_{0} - Y_{1}/k
non-poisson
rse(\mu_{S}) \approx \sqrt(Y_{0} + Y_{1}/k^2 + \sigma^2(1+1/k))/Y_{0} - Y_{1}/k
Please note that when using the early background subtraction method in
combination with the 'non-poisson' distribution argument, the corresponding Lx/Tx
error
may considerably increase due to a high sigmab
value.
Please check whether this is valid for your data set and if necessary
consider to provide an own sigmab
value using the corresponding argument sigmab
.
Returns an S4 object of type RLum.Results.
Slot data
contains a list with the following structure:
@data
$LxTx.table (data.frame) .. $ LnLx .. $ LnLx.BG .. $ TnTx .. $ TnTx.BG .. $ Net_LnLx .. $ Net_LnLx.Error .. $ Net_TnTx .. $ Net_TnTx.Error .. $ LxTx .. $ LxTx.Error $ calc.parameters (list) .. $ sigmab.LnTx .. $ sigmab.TnTx .. $ k
@info
$ call (original function call)
0.8.0
Kreutzer, S., 2024. calc_OSLLxTxRatio(): Calculate Lx/Tx ratio for CW-OSL curves. Function version 0.8.0. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.24. https://CRAN.R-project.org/package=Luminescence
The results of this function have been cross-checked with the Analyst (version 3.24b). Access to the results object via get_RLum.
Caution: If you are using early light subtraction (EBG), please either provide your
own sigmab
value or use background.count.distribution = "poisson"
.
Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) , RLum Developer Team
Duller, G., 2018. Analyst v4.57 - User Manual.
https://users.aber.ac.uk/ggd
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. Ancient TL, 31 (2), 1-3.
RLum.Data.Curve, Analyse_SAR.OSLdata, plot_GrowthCurve, analyse_SAR.CWOSL
##load data
data(ExampleData.LxTxOSLData, envir = environment())
##calculate Lx/Tx ratio
results <- calc_OSLLxTxRatio(
Lx.data = Lx.data,
Tx.data = Tx.data,
signal.integral = c(1:2),
background.integral = c(85:100))
##get results object
get_RLum(results)
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