Description Usage Arguments Details Value Function version How to cite Author(s) References See Also Examples
View source: R/plot_RadialPlot.R
A Galbraith's radial plot is produced on a logarithmic or a linear scale.
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data 
data.frame or RLum.Results object (required):
for 
na.rm 
logical (with default):
excludes 
log.z 
logical (with default):
Option to display the zaxis in logarithmic scale. Default is 
central.value 
numeric: Userdefined central value, primarily used for horizontal centering of the zaxis. 
centrality 
character or numeric (with default): measure of centrality, used for automatically centering the plot and drawing the central line. Can either be one out of

mtext 
character: additional text below the plot title. 
summary 
character (optional): add statistic measures of centrality and dispersion to the plot. Can be one or more of several keywords. See details for available keywords. 
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. 
stats 
character: additional labels of statistically important values in the plot. One or more out of the following:

rug 
logical: Option to add a rug to the zscale, to indicate the location of individual values 
plot.ratio 
numeric:
Userdefined plot area ratio (i.e. curvature of the zaxis). If omitted,
the default value ( 
bar.col 
character or numeric (with default):
colour of the bar showing the 2sigma range around the central
value. To disable the bar, use 
y.ticks 
logical: Option to hide yaxis labels. Useful for data with small scatter. 
grid.col 
character or numeric (with default):
colour of the grid lines (originating at 
line 
numeric: numeric values of the additional lines to be added. 
line.col 
character or numeric: colour of the additional lines. 
line.label 
character: labels for the additional lines. 
output 
logical:
Optional output of numerical plot parameters. These can be useful to
reproduce similar plots. Default is 
... 
Further plot arguments to pass. 
Details and the theoretical background of the radial plot are given in the
cited literature. This function is based on an S script of Rex Galbraith. To
reduce the manual adjustments, the function has been rewritten. Thanks to
Rex Galbraith for useful comments on this function.
Plotting can be disabled by adding the argument plot = "FALSE"
, e.g.
to return only numeric plot output.
Earlier versions of the Radial Plot in this package had the 2sigmabar
drawn onto the zaxis. However, this might have caused misunderstanding in
that the 2sigma range may also refer to the zscale, which it does not!
Rather it applies only to the xycoordinate system (standardised error vs.
precision). A spread in doses or ages must be drawn as lines originating at
zero precision (x0) and zero standardised estimate (y0). Such a range may be
drawn by adding lines to the radial plot ( line
, line.col
,
line.label
, cf. examples).
A statistic summary, i.e. a collection of statistic measures of centrality and dispersion (and further measures) can be added by specifying one or more of the following keywords:
"n"
(number of samples),
"mean"
(mean De value),
"mean.weighted"
(errorweighted mean),
"median"
(median of the De values),
"sdrel"
(relative standard deviation in percent),
"sdrel.weighted"
(errorweighted relative standard deviation in percent),
"sdabs"
(absolute standard deviation),
"sdabs.weighted"
(errorweighted absolute standard deviation),
"serel"
(relative standard error),
"serel.weighted"
(errorweighted relative standard error),
"seabs"
(absolute standard error),
"seabs.weighted"
(errorweighted absolute standard error),
"in.2s"
(percent of samples in 2sigma range),
"kurtosis"
(kurtosis) and
"skewness"
(skewness).
Returns a plot object.
0.5.5
Dietze, M., Kreutzer, S., 2020. plot_RadialPlot(): Function to create a Radial Plot. Function version 0.5.5. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., 2020. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.7. https://CRAN.Rproject.org/package=Luminescence
Michael Dietze, GFZ Potsdam (Germany)
Sebastian Kreutzer, IRAMATCRP2A, Universite Bordeaux Montaigne (France)
Based on a rewritten S script of Rex Galbraith, 2010
, RLum Developer Team
Galbraith, R.F., 1988. Graphical Display of Estimates Having Differing Standard Errors. Technometrics, 30 (3), 271281.
Galbraith, R.F., 1990. The radial plot: Graphical assessment of spread in ages. International Journal of Radiation Applications and Instrumentation. Part D. Nuclear Tracks and Radiation Measurements, 17 (3), 207214.
Galbraith, R. & Green, P., 1990. Estimating the component ages in a finite mixture. International Journal of Radiation Applications and Instrumentation. Part D. Nuclear Tracks and Radiation Measurements, 17 (3) 197206.
Galbraith, R.F. & Laslett, G.M., 1993. Statistical models for mixed fission track ages. Nuclear Tracks And Radiation Measurements, 21 (4), 459470.
Galbraith, R.F., 1994. Some Applications of Radial Plots. Journal of the American Statistical Association, 89 (428), 12321242.
Galbraith, R.F., 2010. On plotting OSL equivalent doses. Ancient TL, 28 (1), 110.
Galbraith, R.F. & Roberts, R.G., 2012. Statistical aspects of equivalent dose and error calculation and display in OSL dating: An overview and some recommendations. Quaternary Geochronology, 11, 127.
plot, plot_KDE, plot_Histogram
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88  ## load example data
data(ExampleData.DeValues, envir = environment())
ExampleData.DeValues < Second2Gray(ExampleData.DeValues$BT998, c(0.0438,0.0019))
## plot the example data straightforward
plot_RadialPlot(data = ExampleData.DeValues)
## now with linear zscale
plot_RadialPlot(data = ExampleData.DeValues,
log.z = FALSE)
## now with output of the plot parameters
plot1 < plot_RadialPlot(data = ExampleData.DeValues,
log.z = FALSE,
output = TRUE)
plot1
plot1$zlim
## now with adjusted zscale limits
plot_RadialPlot(data = ExampleData.DeValues,
log.z = FALSE,
zlim = c(100, 200))
## now the two plots with serious but seasonally changing fun
#plot_RadialPlot(data = data.3, fun = TRUE)
## now with userdefined central value, in logscale again
plot_RadialPlot(data = ExampleData.DeValues,
central.value = 150)
## now with a rug, indicating individual De values at the zscale
plot_RadialPlot(data = ExampleData.DeValues,
rug = TRUE)
## now with legend, colour, different points and smaller scale
plot_RadialPlot(data = ExampleData.DeValues,
legend.text = "Sample 1",
col = "tomato4",
bar.col = "peachpuff",
pch = "R",
cex = 0.8)
## now without 2sigma bar, yaxis, grid lines and central value line
plot_RadialPlot(data = ExampleData.DeValues,
bar.col = "none",
grid.col = "none",
y.ticks = FALSE,
lwd = 0)
## now with userdefined axes labels
plot_RadialPlot(data = ExampleData.DeValues,
xlab = c("Data error (%)",
"Data precision"),
ylab = "Scatter",
zlab = "Equivalent dose [Gy]")
## now with minimum, maximum and median value indicated
plot_RadialPlot(data = ExampleData.DeValues,
central.value = 150,
stats = c("min", "max", "median"))
## now with a brief statistical summary
plot_RadialPlot(data = ExampleData.DeValues,
summary = c("n", "in.2s"))
## now with another statistical summary as subheader
plot_RadialPlot(data = ExampleData.DeValues,
summary = c("mean.weighted", "median"),
summary.pos = "sub")
## now the data set is split into subgroups, one is manipulated
data.1 < ExampleData.DeValues[1:15,]
data.2 < ExampleData.DeValues[16:25,] * 1.3
## now a common dataset is created from the two subgroups
data.3 < list(data.1, data.2)
## now the two data sets are plotted in one plot
plot_RadialPlot(data = data.3)
## now with some graphical modification
plot_RadialPlot(data = data.3,
col = c("darkblue", "darkgreen"),
bar.col = c("lightblue", "lightgreen"),
pch = c(2, 6),
summary = c("n", "in.2s"),
summary.pos = "sub",
legend = c("Sample 1", "Sample 2"))

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