Luminescence (version 0.9.25)

plot_GrowthCurve: Fit and plot a dose-response curve for luminescence data (Lx/Tx against dose)

Description

A dose-response curve is produced for luminescence measurements using a regenerative or additive protocol. The function supports interpolation and extrapolation to calculate the equivalent dose.

Usage

plot_GrowthCurve(
  sample,
  mode = "interpolation",
  fit.method = "EXP",
  fit.force_through_origin = FALSE,
  fit.weights = TRUE,
  fit.includingRepeatedRegPoints = TRUE,
  fit.NumberRegPoints = NULL,
  fit.NumberRegPointsReal = NULL,
  fit.bounds = TRUE,
  NumberIterations.MC = 100,
  output.plot = TRUE,
  output.plotExtended = TRUE,
  output.plotExtended.single = FALSE,
  cex.global = 1,
  txtProgressBar = TRUE,
  verbose = TRUE,
  ...
)

Value

Along with a plot (so far wanted) an RLum.Results object is returned containing, the slot data contains the following elements:

DATA.OBJECT	TYPE	DESCRIPTION
..$De :	data.frame	Table with De values
..$De.MC :	numeric	Table with De values from MC runs
..$Fit :	nls or lm	object from the fitting for EXP, EXP+LIN and EXP+EXP. In case of a resulting linear fit when using LIN, QDR or EXP OR LIN
..$Formula :	expression	Fitting formula as R expression
..$call :	call	The original function call

Arguments

sample

data.frame (required): data frame with three columns for x = Dose,y = LxTx,z = LxTx.Error, y1 = TnTx. The column for the test dose response is optional, but requires 'TnTx' as column name if used. For exponential fits at least three dose points (including the natural) should be provided.

mode

character (with default): selects calculation mode of the function.

• "interpolation" (default) calculates the De by interpolation,

• "extrapolation" calculates the equivalent dose by extrapolation (useful for MAAD measurements) and

• "alternate" calculates no equivalent dose and just fits the data points.

Please note that for option "regenerative" the first point is considered as natural dose

fit.method

character (with default): function used for fitting. Possible options are:

• LIN,

• QDR,

• EXP,

• EXP OR LIN,

• EXP+LIN,

• EXP+EXP,

• GOK,

• LambertW

See details.

fit.force_through_origin

logical (with default) allow to force the fitted function through the origin. For method = "EXP+EXP" the function will be fixed through the origin in either case, so this option will have no effect.

fit.weights

logical (with default): option whether the fitting is done with or without weights. See details.

fit.includingRepeatedRegPoints

logical (with default): includes repeated points for fitting (TRUE/FALSE).

fit.NumberRegPoints

integer (optional): set number of regeneration points manually. By default the number of all (!) regeneration points is used automatically.

fit.NumberRegPointsReal

integer (optional): if the number of regeneration points is provided manually, the value of the real, regeneration points = all points (repeated points) including reg 0, has to be inserted.

fit.bounds

logical (with default): set lower fit bounds for all fitting parameters to 0. Limited for the use with the fit methods EXP, EXP+LIN, EXP OR LIN, GOK, LambertW Argument to be inserted for experimental application only!

NumberIterations.MC

integer (with default): number of Monte Carlo simulations for error estimation. See details.

output.plot

logical (with default): plot output (TRUE/FALSE).

output.plotExtended

logical (with default): If' TRUE, 3 plots on one plot area are provided:

1. growth curve,

2. histogram from Monte Carlo error simulation and

3. a test dose response plot.

If FALSE, just the growth curve will be plotted. Requires: output.plot = TRUE.

output.plotExtended.single

logical (with default): single plot output (TRUE/FALSE) to allow for plotting the results in single plot windows. Requires output.plot = TRUE and output.plotExtended = TRUE.

cex.global

numeric (with default): global scaling factor.

txtProgressBar

logical (with default): enables or disables txtProgressBar. If verbose = FALSE also no txtProgressBar is shown.

verbose

logical (with default): enables or disables terminal feedback.

...

Further arguments and graphical parameters to be passed. Note: Standard arguments will only be passed to the growth curve plot. Supported: xlim, ylim, main, xlab, ylab

Function version

1.11.13

How to cite

Kreutzer, S., Dietze, M., 2024. plot_GrowthCurve(): Fit and plot a dose-response curve for luminescence data (Lx/Tx against dose). Function version 1.11.13. 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., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.25. https://r-lum.github.io/Luminescence/

Author

Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)
Michael Dietze, GFZ Potsdam (Germany) , RLum Developer Team

Details

Fitting methods

For all options (except for the LIN, QDR and the EXP OR LIN), the minpack.lm::nlsLM function with the LM (Levenberg-Marquardt algorithm) algorithm is used. Note: For historical reasons for the Monte Carlo simulations partly the function nls using the port algorithm.

The solution is found by transforming the function or using uniroot.

LIN: fits a linear function to the data using lm: $$y = mx + n$$

QDR: fits a linear function to the data using lm: $$y = a + bx + cx^2$$

EXP: tries to fit a function of the form $$y = a(1 - exp(-\frac{(x+c)}{b}))$$ Parameters b and c are approximated by a linear fit using lm. Note: b = D0

EXP OR LIN: works for some cases where an EXP fit fails. If the EXP fit fails, a LIN fit is done instead.

EXP+LIN: tries to fit an exponential plus linear function of the form: $$y = a(1-exp(-\frac{x+c}{b}) + (gx))$$ The \(D_e\) is calculated by iteration.

Note: In the context of luminescence dating, this function has no physical meaning. Therefore, no D0 value is returned.

EXP+EXP: tries to fit a double exponential function of the form $$y = (a_1 (1-exp(-\frac{x}{b_1}))) + (a_2 (1 - exp(-\frac{x}{b_2})))$$ This fitting procedure is not robust against wrong start parameters and should be further improved.

GOK: tries to fit the general-order kinetics function after Guralnik et al. (2015) of the form of

$$y = a (d - (1 + (\frac{1}{b}) x c)^{(-1/c)})$$

where c > 0 is a kinetic order modifier (not to be confused with c in EXP or EXP+LIN!).

LambertW: tries to fit a dose-response curve based on the Lambert W function according to Pagonis et al. (2020). The function has the form

$$y ~ (1 + (W((R - 1) * exp(R - 1 - ((x + D_{int}) / D_{c}))) / (1 - R))) * N$$

with \(W\) the Lambert W function, calculated using the package lamW::lambertW0, \(R\) the dimensionless retrapping ratio, \(N\) the total concentration of trappings states in cm^-3 and \(D_{c} = N/R\) a constant. \(D_{int}\) is the offset on the x-axis. Please not that finding the root in mode = "extrapolation" is a non-easy task due to the shape of the function and the results might be unexpected.

Fit weighting

If the option fit.weights = TRUE is chosen, weights are calculated using provided signal errors (Lx/Tx error): $$fit.weights = \frac{\frac{1}{error}}{\Sigma{\frac{1}{error}}}$$

Error estimation using Monte Carlo simulation

Error estimation is done using a parametric bootstrapping approach. A set of Lx/Tx values is constructed by randomly drawing curve data sampled from normal distributions. The normal distribution is defined by the input values (mean = value, sd = value.error). Then, a dose-response curve fit is attempted for each dataset resulting in a new distribution of single De values. The standard deviation of this distribution is becomes then the error of the De. With increasing iterations, the error value becomes more stable. However, naturally the error will not decrease with more MC runs.

Alternatively, the function returns highest probability density interval estimates as output, users may find more useful under certain circumstances.

Note: It may take some calculation time with increasing MC runs, especially for the composed functions (EXP+LIN and EXP+EXP).
Each error estimation is done with the function of the chosen fitting method.

Subtitle information

To avoid plotting the subtitle information, provide an empty user mtext mtext = "". To plot any other subtitle text, use mtext.

References

Berger, G.W., Huntley, D.J., 1989. Test data for exponential fits. Ancient TL 7, 43-46.

Guralnik, B., Li, B., Jain, M., Chen, R., Paris, R.B., Murray, A.S., Li, S.-H., Pagonis, P., Herman, F., 2015. Radiation-induced growth and isothermal decay of infrared-stimulated luminescence from feldspar. Radiation Measurements 81, 224-231.

Pagonis, V., Kitis, G., Chen, R., 2020. A new analytical equation for the dose response of dosimetric materials, based on the Lambert W function. Journal of Luminescence 225, 117333. tools:::Rd_expr_doi("10.1016/j.jlumin.2020.117333")

See Also

nls, RLum.Results, get_RLum, minpack.lm::nlsLM, lm, uniroot, lamW::lambertW0

Examples

##(1) plot growth curve for a dummy data.set and show De value
data(ExampleData.LxTxData, envir = environment())
temp <- plot_GrowthCurve(LxTxData)
get_RLum(temp)

##(1b) horizontal plot arrangement
layout(mat = matrix(c(1,1,2,3), ncol = 2))
plot_GrowthCurve(LxTxData, output.plotExtended.single = TRUE)

##(1c) to access the fitting value try
get_RLum(temp, data.object = "Fit")

##(2) plot the growth curve only - uncomment to use
##pdf(file = "~/Desktop/Growth_Curve_Dummy.pdf", paper = "special")
plot_GrowthCurve(LxTxData)
##dev.off()

##(3) plot growth curve with pdf output - uncomment to use, single output
##pdf(file = "~/Desktop/Growth_Curve_Dummy.pdf", paper = "special")
plot_GrowthCurve(LxTxData, output.plotExtended.single = TRUE)
##dev.off()

##(4) plot resulting function for given interval x
x <- seq(1,10000, by = 100)
plot(
 x = x,
 y = eval(temp$Formula),
 type = "l"
)

##(5) plot using the 'extrapolation' mode
LxTxData[1,2:3] <- c(0.5, 0.001)
print(plot_GrowthCurve(LxTxData,mode = "extrapolation"))

##(6) plot using the 'alternate' mode
LxTxData[1,2:3] <- c(0.5, 0.001)
print(plot_GrowthCurve(LxTxData,mode = "alternate"))

##(7) import and fit test data set by Berger & Huntley 1989
QNL84_2_unbleached <-
read.table(system.file("extdata/QNL84_2_unbleached.txt", package = "Luminescence"))

results <- plot_GrowthCurve(
 QNL84_2_unbleached,
 mode = "extrapolation",
 plot = FALSE,
 verbose = FALSE)

#calculate confidence interval for the parameters
#as alternative error estimation
confint(results$Fit, level = 0.68)


if (FALSE) {
QNL84_2_bleached <-
read.table(system.file("extdata/QNL84_2_bleached.txt", package = "Luminescence"))
STRB87_1_unbleached <-
read.table(system.file("extdata/STRB87_1_unbleached.txt", package = "Luminescence"))
STRB87_1_bleached <-
read.table(system.file("extdata/STRB87_1_bleached.txt", package = "Luminescence"))

print(
 plot_GrowthCurve(
 QNL84_2_bleached,
 mode = "alternate",
 plot = FALSE,
 verbose = FALSE)$Fit)

print(
 plot_GrowthCurve(
 STRB87_1_unbleached,
 mode = "alternate",
 plot = FALSE,
 verbose = FALSE)$Fit)

print(
 plot_GrowthCurve(
 STRB87_1_bleached,
 mode = "alternate",
 plot = FALSE,
 verbose = FALSE)$Fit)
 }