dose_fit
builds a calibration curve for gamma dose rate estimation.
Usage
dose_fit(object, background, doses, ...)
dose_predict(object, spectrum, ...)
# S4 method for GammaSpectra,GammaSpectrum,matrix
dose_fit(
object,
background,
doses,
range_Ni,
range_NiEi,
details = list(authors = "", date = Sys.time())
)
# S4 method for GammaSpectra,GammaSpectrum,data.frame
dose_fit(
object,
background,
doses,
range_Ni,
range_NiEi,
details = list(authors = "", date = Sys.time())
)
# S4 method for CalibrationCurve,missing
dose_predict(object, sigma = 1, epsilon = 1.5)
# S4 method for CalibrationCurve,GammaSpectrum
dose_predict(object, spectrum, sigma = 1, epsilon = 1.5)
# S4 method for CalibrationCurve,GammaSpectra
dose_predict(object, spectrum, sigma = 1, epsilon = 1.5)
Arguments
- object
A GammaSpectra or CalibrationCurve object.
- background
A GammaSpectrum object of a length-two
numeric
vector giving the background noise integration value and error, respectively.- doses
A
matrix
ordata.frame
TODO.- ...
Currently not used.
- spectrum
An optional GammaSpectrum or GammaSpectra object in which to look for variables with which to predict. If omitted, the fitted values are used.
- range_Ni, range_NiEi
A length-two
numeric
vector giving the energy range to integrate within (in keV).- details
A
list
of length-one vector specifying additional informations about the instrument for which the curve is built.- sigma
A
numeric
value giving TODO.- epsilon
A
numeric
value giving an extra error term introduced by the calibration of the energy scale of the spectrum.
Value
dose_fit()
returns a CalibrationCurve object.dose_predict()
returns adata.frame
with the following columns:name
(
character
) the name of the spectra.*_signal
(
numeric
) the integrated signal value (according to the value ofthreshold
; seesignal_integrate()
).*_error
(
numeric
) the integrated signal error value (according to the value ofthreshold
; seesignal_integrate()
).gamma_signal
(
numeric
) the predicted gamma dose rate.gamma_error
(
numeric
) the predicted gamma dose rate error.
Details
dose_predict
predicts in situ gamma dose rate.
To estimate the gamma dose rate, one of the calibration curves distributed with this package can be used. These built-in curves are in use in several luminescence dating laboratories and can be used to replicate published results. As these curves are instrument specific, the user may have to build its own curve.
The construction of a calibration curve requires a set of reference spectra for which the gamma dose rate is known and a background noise measurement. First, each reference spectrum is integrated over a given interval, then normalized to active time and corrected for background noise. The dose rate is finally modelled by the integrated signal value used as a linear predictor (York et al., 2004).
See vignette(doserate)
for a reproducible example.
References
Mercier, N. & Falguères, C. (2007). Field Gamma Dose-Rate Measurement with a NaI(Tl) Detector: Re-Evaluation of the "Threshold" Technique. Ancient TL, 25(1), p. 1-4.
York, D., Evensen, N. M., Martínez, M. L. & De Basabe Delgado, J. (2004). Unified Equations for the Slope, Intercept, and Standard Errors of the Best Straight Line. American Journal of Physics, 72(3), p. 367-75. doi:10.1119/1.1632486 .
Examples
## Import CNF files
## Spectra
spc_dir <- system.file("extdata/BDX_LaBr_1/calibration", package = "gamma")
spc <- read(spc_dir)
## Background
bkg_dir <- system.file("extdata/BDX_LaBr_1/background", package = "gamma")
bkg <- read(bkg_dir)
## Get dose rate values
data("clermont")
(doses <- clermont[, c("gamma_dose", "gamma_error")])
#> gamma_dose gamma_error
#> BRIQUE 1986.4620 35.619679
#> C341 849.9668 21.317615
#> C347 1423.8589 25.249756
#> GOU 1575.2249 17.433789
#> LAS 1083.6737 9.570593
#> LMP 641.9004 17.560649
#> MAZ 1141.4033 11.665045
#> MPX 964.0196 13.274167
#> PEP 2538.2217 112.169131
## Build the calibration curve
calib_curve <- dose_fit(spc, bkg, doses,
range_Ni = c(300, 2800),
range_NiEi = c(165, 2800))
## Plot the curve
plot(calib_curve, threshold = "Ni")
## Estimate gamma dose rates
dose_predict(calib_curve, spc)
#> names dose_Ni error_Ni dose_NiEi error_NiEi
#> 1 BRIQUE 1955.8683 2934.1305 1946.9606 2920.7064
#> 2 C341 843.9938 1266.1669 843.0632 1264.7097
#> 3 C347 1420.1912 2130.5630 1402.5391 2103.9999
#> 4 GOU 1599.0175 2398.8191 1598.6341 2398.1692
#> 5 LMP 640.1782 960.4183 639.0612 958.6789
#> 6 MAZ 1140.4575 1710.9077 1144.1496 1716.3805
#> 7 PEP 2443.3517 3665.4815 2435.1440 3653.0481