plot_RadialPlot: Function to create a Radial Plot

Description

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

Usage

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

Arguments

data

data.frame or RLum.Results object (required): for data.frame two columns: De (data[,1]) and De error (data[,2]). To plot

na.exclude

logical (with default): excludes NA values from the data set prior to any further operations.

negatives

character (with default): rule for negative values. Default is "remove" (i.e. negative values are removed from the data set).

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

character (with default): measure of centrality, used for automatically centering the plot and drawing the central line. Can be one out of "mean", "median", "mean.weighte

mtext

character: additional text below the plot title.

summary

character (optional): adds numerical output to the plot. Can be one or more out of: "n" (number of samples), "mean" (mean De value), "mean.weighted" (error-weigh

summary.pos

numeric or character (with default): optional position coordinates or keyword (e.g. "topright") for the statistical summary. Alternativel

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. "topright") for the legend to be plotted.

stats

character: additional labels of statistically important values in the plot. One or more out of the following: "min", "max", "median".

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 parame

bar.col

character or numeric (with default): colour of the bar showing the 2-sigma range around the central value. To disable the bar, use "none".

y.ticks

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

grid.col

character or numeric (with default): colour of the grid lines (originating at [0,0] and stretching to the z-scale). To disable grid lines, use "

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 FALSE.

...

Further plot arguments to pass. xlab must be a vector of length 2, specifying the upper and lower x-axes labels.

Value

Returns a plot object.

Function version

0.5.2 (2014-06-05 13:42:41)

Details

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).

References

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.

Examples

Run this code

## load example data
data(ExampleData.DeValues, envir = environment())
ExampleData.DeValues <- 
  Second2Gray(values = ExampleData.DeValues, dose_rate = 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 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.ci"))

## 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.ci"),
                summary.pos = "sub",
                legend = c("Sample 1", "Sample 2"))

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