Visualise natural/regenerated signal ratios

This function creates a Natural/Regenerated signal vs. time (NR(t)) plot as shown in Steffen et al. 2009

plot_NRt(
  data,
  log = FALSE,
  smooth = c("none", "spline", "rmean"),
  k = 3,
  legend = TRUE,
  legend.pos = "topright",
  ...
)

Arguments

data: list, data.frame, matrix or RLum.Analysis (required): X,Y data of measured values (time and counts). See details on individual data structure.
log: character (optional): logarithmic axes (c("x", "y", "xy")).
smooth: character (optional): apply data smoothing. Use "rmean" to calculate the rolling where k determines the width of the rolling window (see rollmean). "spline" applies a smoothing spline to each curve (see smooth.spline)
k: integer (with default): integer width of the rolling window.
legend: logical (with default): show or hide the plot legend.
legend.pos: character (with default): keyword specifying the position of the legend (see legend).
...: further parameters passed to plot (also see par).

Value

Returns a plot and RLum.Analysis object.

Details

This function accepts the individual curve data in many different formats. If data is a list, each element of the list must contain a two column data.frame or matrix containing the XY data of the curves (time and counts). Alternatively, the elements can be objects of class RLum.Data.Curve.

Input values can also be provided as a data.frame or matrix where the first column contains the time values and each following column contains the counts of each curve.

How to cite

Burow, C., 2023. plot_NRt(): Visualise natural/regenerated signal ratios. 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., 2023. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.23. https://CRAN.R-project.org/package=Luminescence

References

Steffen, D., Preusser, F., Schlunegger, F., 2009. OSL quartz underestimation due to unstable signal components. Quaternary Geochronology, 4, 353-362.

Author

Christoph Burow, University of Cologne (Germany) , RLum Developer Team

Examples


## load example data
data("ExampleData.BINfileData", envir = environment())

## EXAMPLE 1

## convert Risoe.BINfileData object to RLum.Analysis object
data <- Risoe.BINfileData2RLum.Analysis(object = CWOSL.SAR.Data, pos = 8, ltype = "OSL")

## extract all OSL curves
allCurves <- get_RLum(data)

## keep only the natural and regenerated signal curves
pos <- seq(1, 9, 2)
curves <- allCurves[pos]

## plot a standard NR(t) plot
plot_NRt(curves)


## re-plot with rolling mean data smoothing
plot_NRt(curves, smooth = "rmean", k = 10)


## re-plot with a logarithmic x-axis
plot_NRt(curves, log = "x", smooth = "rmean", k = 5)


## re-plot with custom axes ranges
plot_NRt(curves, smooth = "rmean", k = 5,
         xlim = c(0.1, 5), ylim = c(0.4, 1.6),
         legend.pos = "bottomleft")


## re-plot with smoothing spline on log scale
plot_NRt(curves, smooth = "spline", log = "x",
         legend.pos = "top")


## EXAMPLE 2

# you may also use this function to check whether all
# TD curves follow the same shape (making it a TnTx(t) plot).
posTD <- seq(2, 14, 2)
curves <- allCurves[posTD]

plot_NRt(curves, main = "TnTx(t) Plot",
         smooth = "rmean", k = 20,
         ylab = "TD natural / TD regenerated",
         xlim = c(0, 20), legend = FALSE)


## EXAMPLE 3

# extract data from all positions
data <- lapply(1:24, FUN = function(pos) {
   Risoe.BINfileData2RLum.Analysis(CWOSL.SAR.Data, pos = pos, ltype = "OSL")
})

# get individual curve data from each aliquot
aliquot <- lapply(data, get_RLum)

# set graphical parameters
par(mfrow = c(2, 2))

# create NR(t) plots for all aliquots
for (i in 1:length(aliquot)) {
   plot_NRt(aliquot[[i]][pos],
            main = paste0("Aliquot #", i),
            smooth = "rmean", k = 20,
            xlim = c(0, 10),
            cex = 0.6, legend.pos = "bottomleft")
}







# reset graphical parameters
par(mfrow = c(1, 1))