# plot_NRt

##### Visualise natural/regenerated signal ratios

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

##### Usage

```
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).- ...

##### 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.

##### Value

##### How to cite

Burow, C. (2018). plot_NRt(): Visualise natural/regenerated signal ratios. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J. (2018). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.8.6. 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.

##### See Also

##### Examples

```
# NOT RUN {
## 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))
# }
```

*Documentation reproduced from package Luminescence, version 0.8.6, License: GPL-3*