% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_NRt.R
\name{plot_NRt}
\alias{plot_NRt}
\title{Visualise natural/regenerated signal ratios}
\usage{
plot_NRt(
  data,
  log = FALSE,
  smooth = c("none", "spline", "rmean"),
  k = 3,
  legend = TRUE,
  legend.pos = "topright",
  ...
)
}
\arguments{
\item{data}{\link{list}, \link{data.frame}, \link{matrix} or \linkS4class{RLum.Analysis} (\strong{required}):
X,Y data of measured values (time and counts). See details on individual data structure.}

\item{log}{\link{character} (\emph{optional}):
logarithmic axes (\code{c("x", "y", "xy")}).}

\item{smooth}{\link{character} (\emph{with default}):
apply data smoothing. If \code{"none"} (default), no data smoothing is applied.
Use \code{"rmean"} to calculate the rolling mean, where \code{k} determines the
width of the rolling window (see \link[data.table:froll]{data.table::frollmean}). \code{"spline"}
applies a smoothing spline to each curve (see \link[stats:smooth.spline]{stats::smooth.spline})}

\item{k}{\link{integer} (\emph{with default}):
integer width of the rolling window.}

\item{legend}{\link{logical} (\emph{with default}):
enable/disable the plot legend.}

\item{legend.pos}{\link{character} (\emph{with default}):
keyword specifying the position of the legend (see \link{legend}).}

\item{...}{further parameters passed to \link{plot} (also see \link{par}).}
}
\value{
Returns a plot and \linkS4class{RLum.Analysis} object.
}
\description{
This function creates a Natural/Regenerated signal vs. time (NR(t)) plot
as shown in Steffen et al. 2009.

This function accepts the individual curve data in many different formats. If
\code{data} is a \code{list}, each element of the list must contain a two
column \code{data.frame} or \code{matrix} containing the \code{XY} data of the curves
(time and counts). Alternatively, the elements can be objects of class
\linkS4class{RLum.Data.Curve}.

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


} 

\section{How to cite}{
Burow, C., 2025. 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., Colombo, M., Steinbuch, L., Boer, A.d., 2025. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 1.1.2. https://r-lum.github.io/Luminescence/
}

\references{
Steffen, D., Preusser, F., Schlunegger, F., 2009. OSL quartz underestimation due to
unstable signal components. Quaternary Geochronology, 4, 353-362.
}
\seealso{
\link{plot}
}
\author{
Christoph Burow, University of Cologne (Germany)
, RLum Developer Team}
