A Galbraith's radial plot is produced on a logarithmic or a linear scale.

```
plot_RadialPlot(data, na.rm = TRUE, log.z = TRUE, central.value,
centrality = "mean.weighted", mtext, summary, summary.pos, legend,
legend.pos, stats, rug = FALSE, plot.ratio, bar.col, y.ticks = TRUE,
grid.col, line, line.col, line.label, output = FALSE, ...)
```

data

data.frame or '>RLum.Results object (**required**):
for `data.frame`

two columns: De (`data[,1]`

) and De error (`data[,2]`

).
To plot several data sets in one plot, the data sets must be provided as
`list`

, e.g. `list(data.1, data.2)`

.

na.rm

logical (*with default*):
excludes `NA`

values from the data set prior to any further operations.

log.z

logical (*with default*):
Option to display the z-axis in logarithmic scale. Default is `TRUE`

.

central.value

numeric: User-defined central value, primarily used for horizontal centering of the z-axis.

centrality

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

legend

character vector (*optional*):
legend content to be added to the plot.

legend.pos

stats

character: additional labels of statistically important values in the plot. One or more out of the following:

`"min"`

,`"max"`

,`"median"`

.

rug

logical: Option to add a rug to the z-scale, to indicate the location of individual values

plot.ratio

numeric:
User-defined plot area ratio (i.e. curvature of the z-axis). If omitted,
the default value (`4.5/5.5`

) is used and modified automatically to optimise
the z-axis curvature. The parameter should be decreased when data points
are plotted outside the z-axis or when the z-axis gets too elliptic.

bar.col

y.ticks

logical: Option to hide y-axis labels. Useful for data with small scatter.

grid.col

line

numeric: numeric values of the additional lines to be added.

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 `FALSE`

.

...

Further plot arguments to pass. `xlab`

must be a vector of
length 2, specifying the upper and lower x-axes labels.

Returns a plot object.

0.5.5 (2018-08-03 10:46:47)

Dietze, M., Kreutzer, S. (2018). 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. (2018). Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.8.6. https://CRAN.R-project.org/package=Luminescence

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 2-sigma-bar
drawn onto the z-axis. However, this might have caused misunderstanding in
that the 2-sigma range may also refer to the z-scale, which it does not!
Rather it applies only to the x-y-coordinate 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"`

(error-weighted mean),`"median"`

(median of the De values),`"sdrel"`

(relative standard deviation in percent),`"sdrel.weighted"`

(error-weighted relative standard deviation in percent),`"sdabs"`

(absolute standard deviation),`"sdabs.weighted"`

(error-weighted absolute standard deviation),`"serel"`

(relative standard error),`"serel.weighted"`

(error-weighted relative standard error),`"seabs"`

(absolute standard error),`"seabs.weighted"`

(error-weighted absolute standard error),`"in.2s"`

(percent of samples in 2-sigma range),`"kurtosis"`

(kurtosis) and`"skewness"`

(skewness).

Galbraith, R.F., 1988. Graphical Display of Estimates Having Differing Standard Errors. Technometrics, 30 (3), 271-281.

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), 207-214.

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) 197-206.

Galbraith, R.F. & Laslett, G.M., 1993. Statistical models for mixed fission track ages. Nuclear Tracks And Radiation Measurements, 21 (4), 459-470.

Galbraith, R.F., 1994. Some Applications of Radial Plots. Journal of the American Statistical Association, 89 (428), 1232-1242.

Galbraith, R.F., 2010. On plotting OSL equivalent doses. Ancient TL, 28 (1), 1-10.

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, 1-27.

# NOT RUN { ## load example data data(ExampleData.DeValues, envir = environment()) ExampleData.DeValues <- Second2Gray(ExampleData.DeValues$BT998, c(0.0438,0.0019)) ## plot the example data straightforward plot_RadialPlot(data = ExampleData.DeValues) ## now with linear z-scale plot_RadialPlot(data = ExampleData.DeValues, log.z = FALSE) ## now with output of the plot parameters plot1 <- plot_RadialPlot(data = ExampleData.DeValues, log.z = FALSE, output = TRUE) plot1 plot1$zlim ## now with adjusted z-scale 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 user-defined central value, in log-scale again plot_RadialPlot(data = ExampleData.DeValues, central.value = 150) ## now with a rug, indicating individual De values at the z-scale 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 2-sigma bar, y-axis, grid lines and central value line plot_RadialPlot(data = ExampleData.DeValues, bar.col = "none", grid.col = "none", y.ticks = FALSE, lwd = 0) ## now with user-defined 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 sub-groups, 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")) # }