calc_gSGC(data, gSGC.type = "0-250", gSGC.parameters, n.MC = 100, verbose = TRUE, plot = TRUE, ...)
data.frame
(required): input data of providing the following
columns: 'LnTn', 'LnTn.error', Lr1Tr1', 'Lr1Tr1.error', 'Dr1'
Note: column names are not required. The function expect the input data in the given ordercharacter
(with default): define the function parameters that
should be used for the iteration procedure: Li et al., 2015 (Table 2)
presented function parameters for two dose ranges: "0-450"
and "0-250"
list
(optional): option to provide own function
parameters used for #' fitting as named list.
Nomenclature follows Li et al., 2015, i.e.
list(A,A.error,D0,D0.error,c,c.error,Y0,Y0.error,range)
, range requires a vector for
the range the function is considered as valid, e.g. range = c(0,250)
Using this option overwrites the default parameter list of the gSGC, meaning the argument
gSGC.type
will be without effectinteger
(with default): number of Monte Carlo simulation runs for
error estimation, s. details.logical
: enable or disable terminal outputlogical
: enable or disable graphical feedback as plotRLum.Results
.@data
$ De.value (data.frame)
.. $ De
.. $ De.error
.. $ Eta
$ De.MC (list) contains the matricies from the error estimation.
$ uniroot (list) contains the uniroot outputs of the De estimations@info
$ call (call) the original function call
uniroot
.
Large values for n.MC
will significantly increase the computation time.
RLum.Results
, get_RLum
, uniroot
results <- calc_gSGC(data = data.frame(
LnTn = 2.361, LnTn.error = 0.087,
Lr1Tr1 = 2.744, Lr1Tr1.error = 0.091,
Dr1 = 34.4))
get_RLum(results, data.object = "De")
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