calc_CommonDose(input.data, sigmab = 0, log = TRUE, sample.id = "unknown sample")
RLum.Results
or data.frame (required):
for data.frame
: two columns with De (input.data[,1])
and
De error (values[,2])
numeric
(with default): spread in De values given as a
fraction (e.g. 0.2). This value represents the expected overdispersion in
the data, if the sample is well-bleached (Cunningham & Walling 2012,
p.logical
(with default): fit the (un-)logged common age
model to De datacharacter
(with default): sample idRLum.Results
object is
returned containing the following element:get_RLum.Results
log = TRUE
this function calculates the weighted mean of
logarithmic De values. Each of the estimates is weighted by the inverse
square of its relative standard error. The weighted mean is then
transformed back to the dose scale (Galbraith & Roberts 2012, p. 14).
The log transformation is not applicable if the De estimates are close to
zero or negative. In this case the un-logged model can be applied instead
(log = FALSE
). The weighted mean is then calculated using the
un-logged estimates of De and their absolute standard error
(Galbraith & Roberts 2012, p. 14).calc_CentralDose
,
calc_FiniteMixture
, calc_FuchsLang2001
,
calc_MinDose3
, calc_MinDose4
## load example data
data(ExampleData.DeValues, envir = environment())
## apply the common dose model
calc_CommonDose(ExampleData.DeValues)
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