2.4 Dose models

The Luminescence package provides alot of functions for calculating a “mean” equivalent dose. These include:

  • calc_AverageDose()
  • calc_CommonDose()
  • calc_CentralDose()
  • calc_MinDose()
  • calc_MaxDose()
  • calc_FiniteMixture()
  • calc_FuchsLang2001()
  • calc_IEU()

All of these functions more or less follow the same pattern with regards to input and output, so it suffices to apply just one the models here in this example.

The first argument is always named data and requires either an RLum.Results object, or a data.frame with two columns for the equivalent doses and their corresponding standard errors. In our example it is easiest to just pass the RLum.Analysis object to the dose model function.

cam <- calc_CentralDose(de, plot = FALSE, verbose = TRUE)
## 
##  [calc_CentralDose]
## 
## ----------- meta data ----------------
##  n:                       10
##  log:                     TRUE
## ----------- dose estimate ------------
##  central dose [Gy]:       474.49
##  SE [Gy]:                 30.28
##  rel. SE [%]:             6.38
## ----------- overdispersion -----------
##  OD [Gy]:                 94.00
##  SE [Gy]:                 21.78
##  OD [%]:                  19.81
##  SE [%]:                  4.59
## -------------------------------------

We do now, however, face the problem that the one aliquot that failed the rejection criteria was also considered when calculating the central dose. It is usually better to gather and filter the data first before applying the dose model. Here, we extract the table with the equivalent doses first, filter out only those aliquots that passed the rejection criteria and then select only the first two columns.

passed <- which(de_df$RC.Status == "OK")
de_df <- de_df[passed, 1:2]

cam <- calc_CentralDose(de_df, plot = FALSE, verbose = TRUE)
## 
##  [calc_CentralDose]
## 
## ----------- meta data ----------------
##  n:                       10
##  log:                     TRUE
## ----------- dose estimate ------------
##  central dose [Gy]:       474.49
##  SE [Gy]:                 30.28
##  rel. SE [%]:             6.38
## ----------- overdispersion -----------
##  OD [Gy]:                 94.00
##  SE [Gy]:                 21.78
##  OD [%]:                  19.81
##  SE [%]:                  4.59
## -------------------------------------

As usual, the dose model function returns an RLum.Results object, whose content can be accessed with the get_RLum() function. If we do not specify which slot of the object should be returned, we automatically receive the summary that also contains the mean equivalent dose.

get_RLum(cam)
##        de   de_err       OD   OD_err     Lmax
## 1 474.493 30.28434 19.81061 4.590771 10.94889