View source: R/plot_DRTResults.R
plot_DRTResults | R Documentation |
The function provides a standardised plot output for dose recovery test measurements.
plot_DRTResults(
values,
given.dose = NULL,
error.range = 10,
preheat,
boxplot = FALSE,
mtext,
summary,
summary.pos,
legend,
legend.pos,
par.local = TRUE,
na.rm = FALSE,
...
)
values |
RLum.Results or data.frame (required):
input values containing at least De and De error. To plot
more than one data set in one figure, a |
given.dose |
numeric (optional):
given dose used for the dose recovery test to normalise data.
If only one given dose is provided this given dose is valid for all input
data sets (i.e., |
error.range |
numeric:
symmetric error range in percent will be shown as dashed lines in the plot.
Set |
preheat |
numeric: optional vector of preheat temperatures to be used for grouping the De values. If specified, the temperatures are assigned to the x-axis. |
boxplot |
logical:
optionally plot values, that are grouped by preheat temperature as boxplots.
Only possible when |
mtext |
character: additional text below the plot title. |
summary |
character (optional): adds numerical output to the plot. Can be one or more out of:
and all other measures returned by the function calc_Statistics. |
summary.pos |
numeric or character (with default):
optional position coordinates or keyword (e.g. |
legend |
character vector (optional): legend content to be added to the plot. |
legend.pos |
numeric or character (with default):
optional position coordinates or keyword (e.g. |
par.local |
logical (with default):
use local graphical parameters for plotting, e.g. the plot is shown in one
column and one row. If |
na.rm |
logical: indicating whether |
... |
further arguments and graphical parameters passed to plot, supported are:
|
Procedure to test the accuracy of a measurement protocol to reliably
determine the dose of a specific sample. Here, the natural signal is erased
and a known laboratory dose administered which is treated as unknown. Then
the De measurement is carried out and the degree of congruence between
administered and recovered dose is a measure of the protocol's accuracy for
this sample.
In the plot the normalised De is shown on the y-axis, i.e. obtained De/Given Dose.
A plot is returned.
0.1.14
Kreutzer, S., Dietze, M., 2024. plot_DRTResults(): Visualise dose recovery test results. Function version 0.1.14. 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., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.24. https://CRAN.R-project.org/package=Luminescence
Further data and plot arguments can be added by using the appropriate R commands.
Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)
Michael Dietze, GFZ Potsdam (Germany)
, RLum Developer Team
Wintle, A.G., Murray, A.S., 2006. A review of quartz optically stimulated luminescence characteristics and their relevance in single-aliquot regeneration dating protocols. Radiation Measurements, 41, 369-391.
plot
## read example data set and misapply them for this plot type
data(ExampleData.DeValues, envir = environment())
## plot values
plot_DRTResults(
values = ExampleData.DeValues$BT998[7:11,],
given.dose = 2800,
mtext = "Example data")
## plot values with legend
plot_DRTResults(
values = ExampleData.DeValues$BT998[7:11,],
given.dose = 2800,
legend = "Test data set")
## create and plot two subsets with randomised values
x.1 <- ExampleData.DeValues$BT998[7:11,]
x.2 <- ExampleData.DeValues$BT998[7:11,] * c(runif(5, 0.9, 1.1), 1)
plot_DRTResults(
values = list(x.1, x.2),
given.dose = 2800)
## some more user-defined plot parameters
plot_DRTResults(
values = list(x.1, x.2),
given.dose = 2800,
pch = c(2, 5),
col = c("orange", "blue"),
xlim = c(0, 8),
ylim = c(0.85, 1.15),
xlab = "Sample aliquot")
## plot the data with user-defined statistical measures as legend
plot_DRTResults(
values = list(x.1, x.2),
given.dose = 2800,
summary = c("n", "weighted$mean", "sd.abs"))
## plot the data with user-defined statistical measures as sub-header
plot_DRTResults(
values = list(x.1, x.2),
given.dose = 2800,
summary = c("n", "weighted$mean", "sd.abs"),
summary.pos = "sub")
## plot the data grouped by preheat temperatures
plot_DRTResults(
values = ExampleData.DeValues$BT998[7:11,],
given.dose = 2800,
preheat = c(200, 200, 200, 240, 240))
## read example data set and misapply them for this plot type
data(ExampleData.DeValues, envir = environment())
## plot values
plot_DRTResults(
values = ExampleData.DeValues$BT998[7:11,],
given.dose = 2800,
mtext = "Example data")
## plot two data sets grouped by preheat temperatures
plot_DRTResults(
values = list(x.1, x.2),
given.dose = 2800,
preheat = c(200, 200, 200, 240, 240))
## plot the data grouped by preheat temperatures as boxplots
plot_DRTResults(
values = ExampleData.DeValues$BT998[7:11,],
given.dose = 2800,
preheat = c(200, 200, 200, 240, 240),
boxplot = TRUE)
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