Visualise natural/regenerated signal ratios

Description

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

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

Returns a plot and RLum.Analysis object.

Author(s)

Christoph Burow, University of Cologne (Germany)

References

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

See Also

plot

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))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(Luminescence)
Welcome to the R package Luminescence version 0.6.0 [Built: 2016-05-30 16:47:30 UTC]
A trapped electron to a yellow LED: 'Well, that's all?'
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Luminescence/plot_NRt.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot_NRt
> ### Title: Visualise natural/regenerated signal ratios
> ### Aliases: plot_NRt
> 
> ### ** 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))
> 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>