View source: R/calc_CentralDose.R
calc_CentralDose | R Documentation |
This function calculates the central dose and dispersion of the De distribution, their standard errors and the profile log likelihood function for sigma.
calc_CentralDose(data, sigmab, log = TRUE, na.rm = FALSE, plot = TRUE, ...)
data |
RLum.Results or data.frame (required):
for data.frame: two columns with De |
sigmab |
numeric (with default):
additional spread in De values.
This value represents the expected overdispersion in the data should the sample be
well-bleached (Cunningham & Walling 2012, p. 100).
NOTE: For the logged model ( |
log |
logical (with default): fit the (un-)logged central age model to De data |
na.rm |
logical (with default): strip |
plot |
logical (with default): plot output |
... |
further arguments ( |
This function uses the equations of Galbraith & Roberts (2012). The
parameters delta
and sigma
are estimated by numerically solving
eq. 15 and 16. Their standard errors are approximated using eq. 17.
In addition, the profile log-likelihood function for sigma
is
calculated using eq. 18 and presented as a plot. Numerical values of the
maximum likelihood approach are only presented in the plot and not
in the console. A detailed explanation on maximum likelihood estimation can
be found in the appendix of Galbraith & Laslett (1993, 468-470) and
Galbraith & Roberts (2012, 15)
Returns a plot (optional) and terminal output. In addition an RLum.Results object is returned containing the following elements:
.$summary |
data.frame summary of all relevant model results. |
.$data |
data.frame original input data |
.$args |
list used arguments |
.$call |
call the function call |
.$profile |
data.frame the log likelihood profile for sigma |
The output should be accessed using the function get_RLum
1.4.1
Burow, C., 2024. calc_CentralDose(): Apply the central age model (CAM) after Galbraith et al. (1999) to a given De distribution. Function version 1.4.1. 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
Christoph Burow, University of Cologne (Germany)
Based on a rewritten S script of Rex Galbraith, 2010
, RLum Developer Team
Galbraith, R.F. & Laslett, G.M., 1993. Statistical models for mixed fission track ages. Nuclear Tracks Radiation Measurements 4, 459-470.
Galbraith, R.F., Roberts, R.G., Laslett, G.M., Yoshida, H. & Olley, J.M., 1999. Optical dating of single grains of quartz from Jinmium rock shelter, northern Australia. Part I: experimental design and statistical models. Archaeometry 41, 339-364.
Galbraith, R.F. & Roberts, R.G., 2012. Statistical aspects of equivalent dose and error calculation and display in OSL dating: An overview and some recommendations. Quaternary Geochronology 11, 1-27.
Further reading
Arnold, L.J. & Roberts, R.G., 2009. Stochastic modelling of multi-grain equivalent dose (De) distributions: Implications for OSL dating of sediment mixtures. Quaternary Geochronology 4, 204-230.
Bailey, R.M. & Arnold, L.J., 2006. Statistical modelling of single grain quartz De distributions and an assessment of procedures for estimating burial dose. Quaternary Science Reviews 25, 2475-2502.
Cunningham, A.C. & Wallinga, J., 2012. Realizing the potential of fluvial archives using robust OSL chronologies. Quaternary Geochronology 12, 98-106.
Rodnight, H., Duller, G.A.T., Wintle, A.G. & Tooth, S., 2006. Assessing the reproducibility and accuracy of optical dating of fluvial deposits. Quaternary Geochronology, 1 109-120.
Rodnight, H., 2008. How many equivalent dose values are needed to obtain a reproducible distribution?. Ancient TL 26, 3-10.
plot, calc_CommonDose, calc_FiniteMixture, calc_FuchsLang2001, calc_MinDose
##load example data
data(ExampleData.DeValues, envir = environment())
##apply the central dose model
calc_CentralDose(ExampleData.DeValues$CA1)
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