calc_MinDose(data, sigmab, log = TRUE, par = 3, bootstrap = FALSE,
boundaries, init.values, plot = TRUE, ...)RLum.Results data.frame: two columns with De (data[,1]) and
De error (values[,2])numeric (required): spread in De values given as a
fraction (e.g. 0.2). This value represents the expected overdispersion in
the data should the sample be well-bleached (Cunningham & Walling 2012logical (with default): fit the (un-)logged
minimum dose model to De datanumeric (with default): apply the 3- or 4-parametric minimum age
model (par=3 or par=4). The MAM-3 is used by default.logical (with default): apply the recycled bootstrap approach of Cunningham & Wallinga (2012).list: a named list of boundary values for gamma, sigma, p0 and mu
to be used in the optimisation routine
(e.g. list(gamma=c(0.01,100), sigma=c(0.01,5), p0=c(0.01,0.99), mu=c(10, 100))).
If nonumeric: a named list with starting values for gamma, sigma, p0 and mu
(e.g. list(gamma=100 sigma=1.5, p0=0.1, mu=100)).
If no values are provided reasonable values are tried to be estimated logical (with default): plot output
(TRUE/FALSE)bs.M, bs.N, bs.h, sigmab.sd).
See details for their usage.gamma: minimum dose on the log scale
mu: mean of the non-truncated normal distribution
sigma: spread in ages above the minimum
p0: proportion of grains at gamma
}
If par=3 (default) the 3-parametric minimum age model is applied,
where gamma = mu. For par=4 the 4-parametric model is applied
instead.
(Un-)logged model
In the original version of the three-parameter minimum dose model, the
basic data are the natural logarithms of the De estimates and relative
standard errors of the De estimates. This model will be applied if
log = TRUE.
If log = FALSE, the modified un-logged model will be applied
instead. This has essentially the same form as the original version.
gamma and sigma are in Gy and gamma becomes the
minimum true dose in the population.
While the original (logged) version of the mimimum dose model may be
appropriate for most samples (i.e. De distributions), the modified
(un-logged) version is specially designed for modern-age and young
samples containing negative, zero or near-zero De estimates (Arnold
et al. 2009, p. 323).
Boundaries
Depending on the data, the upper and lower bounds for gamma, mu,
sigma and p0 need to be specified. If no values are provided
for boundaries the function tries to estimate reasonable boundaries.
However, these might not be appropriate in all cases and
if the final estimate of any of these parameters is on the boundary,
make sure to adjust the boundaries via boundaries.
Initial values
The log likelihood calculations use the optim function with the
L-BFGS-B method.
Accordingly, initial values for the four parameters need to be specified.
If no values are provided for init.values reasonable starting
values are estimated. If the final estimates of gamma, mu,
sigma and p0 are totally off target, consider providing custom
starting values via init.values.calc_CentralDose,
calc_CommonDose, calc_FiniteMixture,
calc_FuchsLang2001, calc_MaxDose## load example data
data(ExampleData.DeValues, envir = environment())
# apply the un-logged, 3-parametric minimum age model
calc_MinDose(data = ExampleData.DeValues, par = 3, sigmab = 0.2, log = FALSE)
# re-run the model, but save results to a variable
mam<- calc_MinDose(data = ExampleData.DeValues, par = 3, sigmab = 0.2, log = FALSE, plot = FALSE)
# show summary table
get_RLum.Results(mam, "summary")
# plot the log likelihood profiles retroactively
plot_RLum.Results(mam)Run the code above in your browser using DataLab