calc_MaxDose3(input.data, sigmab, log = TRUE, sample.id = "unknown sample",
gamma.xlb = 0.1, gamma.xub = 100, sigma.xlb = 0.001, sigma.xub = 5,
init.gamma = 10, init.sigma = 1.2, init.p0 = 0.01, ignore.NA = FALSE,
calc.ProfileLikelihoods = TRUE, console.ProfileLikelihoods = FALSE,
console.extendedOutput = FALSE, output.file = FALSE, output.filename = "default",
output.plot = FALSE, output.indices = 3)RLum.Results data.frame: two columns with De (input.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, if the sample is well-bleached (Cunningham & Walling 2012, plogical (with default): fit the (un-)logged three parameter
maximum dose model to De data. An un-logged version is currently not
supportedcharacter (with default): sample idnumeric (with default): lower boundary of gammanumeric (with default): upper boundary of gammanumeric (with default): lower boundary of sigmanumeric (with default): upper boundary of sigmanumeric (with default): starting value of gammanumeric (with default): starting value of sigmanumeric (with default): starting value of p0logical (with default): ignore NA values during log
likelihood calculations. See details.logical (with default): calculate profile log likelihood
functions for gamma, sigma, p0. See output.indices.logical (with default): print profile log likelihood
functions for gamma, sigma, p0 to console.logical (with default): extended terminal outputlogical (with default): save results to file. See
output.filename.character (with default): desired filename, else results
are saved to default-3R(-UL).reslogical (with default): plot output
(TRUE/FALSE)numeric (with default): requires
calc.ProfileLikelihoods = TRUE. Indices: 1 = gamma,
2 = gamma/sigma, 3 = gamma/sigma/p0.RLum.Results get_RLum.Resultsgamma: maximum dose on the log scale
sigma: spread in ages above the maximum
p0: proportion of grains at gamma
}
Data transformation
To estimate the maximum dose population and its standard error, the three
parameter minimum age model of Galbraith et al. (1999) is adapted. The
measured De values are transformed as follows:
1. convert De values to natural logs
2. multiply the logged data to creat a mirror image of the De distribution
3. shift De values along x-axis by the smallest x-value found to obtain
only positive values
4. apply the MAM to these data, after combining the square of the measurement
error associated with each De value with a relative error specified by
sigmab
When all calculations are done, results are then converted
as follows
1. subtract the x-offset
2. multiply the natural logs by -1
3. take the exponent to obtain the maximum dose estimate in Gy
(Un-)logged model
In the original version of the three-parameter maximum 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 maximum true dose in
the population. NOTE: This option is disabled for the maximum dose
model, as the data transformation requires logged De values!
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 (gamma.xlb
and gamma.xub) need to be specified. If the final estimate of gamma is
on the boundary, gamma.xlb and gamma.xub need to be adjusted
appropriately, so that gamma lies within the bounds. The same applies for
sigma boundaries (sigma.xlb and sigma.xub).
Initial values
The log likelihood calculations use the nlminb function. Accordingly,
initial values for the three parameters init.gamma, init.sigma
and init.p0 need to be specified.
Ignore NA values
In some cases during the calculation of the log likelihoods NA values
are produced instantly terminating the minimum age model. It is advised to
adjust some of the values provided for any argument. If the model still
produces NA values it is possible to omit these values by setting
ignore.NA = TRUE. While the model is then usually able to finish
all calculations the integrity of the final estimates cannot be ensured.
Use this argument at your own risk.nlminb,
calc_CentralDose,
calc_CommonDose, calc_FiniteMixture,
calc_FuchsLang2001, calc_MinDose4## load example data
data(ExampleData.DeValues, envir = environment())
## apply the logged maximum dose model
## NOTE THAT THE EXAMPLE DATA SET IS NOT SUITABLE FOR THE
## MAXIMUM DOSE MODEL.
calc_MaxDose3(ExampleData.DeValues,
sigmab = 0.3, gamma.xub = 4000)Run the code above in your browser using DataLab