Last data update: 2014.03.03

 R: Function to create a Radial Plot
plot_RadialPlotR Documentation

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.rm = TRUE, negatives = "remove", 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, ...)

Arguments

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.

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 or numeric (with default): measure of centrality, used for automatically centering the plot and drawing the central line. Can either be one out of "mean", "median", "mean.weighted" and "median.weighted" or a numeric value used for the standardisation.

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. "topright") for the statistical summary. Alternatively, the keyword "sub" may be specified to place the summary below the plot header. However, this latter option is only possible if mtext is not used.

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".

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

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

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 "none". Default is "grey".

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.

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).

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).

Value

Returns a plot object.

Function version

0.5.3 (2016-05-19 23:47:38)

Author(s)

Michael Dietze, GFZ Potsdam (Germany),
Sebastian Kreutzer, IRAMAT-CRP2A, Universite Bordeaux Montaigne (France)
Based on a rewritten S script of Rex Galbraith, 2010
R Luminescence Package Team

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.

See Also

plot, plot_KDE, plot_Histogram

Examples


## 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"))

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]
Rubber mallet to steel cylinder: 'Let's rock and roll.'
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Luminescence/plot_RadialPlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot_RadialPlot
> ### Title: Function to create a Radial Plot
> ### Aliases: plot_RadialPlot
> 
> ### ** Examples
> 
> 
> ## 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
$data
$data[[1]]
       De error      z    se z.central precision std.estimate std.estimate.plot
1  151.48 5.334 151.48 5.334  126.8469 0.1874766   4.61813168        4.61813168
2  152.08 5.144 152.08 5.144  126.8469 0.1944012   4.90534883        4.90534883
3  165.80 6.805 165.80 6.805  126.8469 0.1469508   5.72419021        5.72419021
4  136.15 4.608 136.15 4.608  126.8469 0.2170139   2.01890503        2.01890503
5  144.42 4.642 144.42 4.642  126.8469 0.2154244   3.78567738        3.78567738
6  123.44 4.471 123.44 4.471  126.8469 0.2236636  -0.76199634       -0.76199634
7  123.64 4.227 123.64 4.227  126.8469 0.2365744  -0.75866705       -0.75866705
8  127.07 4.396 127.07 4.396  126.8469 0.2274795   0.05075395        0.05075395
9  125.06 4.630 125.06 4.630  126.8469 0.2159827  -0.38593642       -0.38593642
10 124.45 4.256 124.45 4.256  126.8469 0.2349624  -0.56317801       -0.56317801
11 118.60 4.049 118.60 4.049  126.8469 0.2469746  -2.03677096       -2.03677096
12 128.08 4.408 128.08 4.408  126.8469 0.2268603   0.27974464        0.27974464
13 110.78 3.701 110.78 3.701  126.8469 0.2701972  -4.34122821       -4.34122821
14 121.02 4.187 121.02 4.187  126.8469 0.2388345  -1.39166124       -1.39166124
15 124.09 4.129 124.09 4.129  126.8469 0.2421894  -0.66768845       -0.66768845
16 124.70 4.043 124.70 4.043  126.8469 0.2473411  -0.53101302       -0.53101302
17 123.68 4.262 123.68 4.262  126.8469 0.2346316  -0.74305153       -0.74305153
18 126.34 4.228 126.34 4.228  126.8469 0.2365184  -0.11988780       -0.11988780
19 128.59 4.254 128.59 4.254  126.8469 0.2350729   0.40975890        0.40975890
20 131.46 4.448 131.46 4.448  126.8469 0.2248201   1.03712104        1.03712104
21 127.77 4.330 127.77 4.330  126.8469 0.2309469   0.21319039        0.21319039
22 131.05 5.023 131.05 5.023  126.8469 0.1990842   0.83677372        0.83677372
23 126.34 4.317 126.34 4.317  126.8469 0.2316423  -0.11741617       -0.11741617
24 115.49 3.479 115.49 3.479  126.8469 0.2874389  -3.26441093       -3.26441093
25 119.58 3.815 119.58 3.815  126.8469 0.2621232  -1.90481930       -1.90481930


$data.global
       De error      z    se z.central precision std.estimate std.estimate.plot
1  151.48 5.334 151.48 5.334  126.8469 0.1874766   4.61813168        4.61813168
2  152.08 5.144 152.08 5.144  126.8469 0.1944012   4.90534883        4.90534883
3  165.80 6.805 165.80 6.805  126.8469 0.1469508   5.72419021        5.72419021
4  136.15 4.608 136.15 4.608  126.8469 0.2170139   2.01890503        2.01890503
5  144.42 4.642 144.42 4.642  126.8469 0.2154244   3.78567738        3.78567738
6  123.44 4.471 123.44 4.471  126.8469 0.2236636  -0.76199634       -0.76199634
7  123.64 4.227 123.64 4.227  126.8469 0.2365744  -0.75866705       -0.75866705
8  127.07 4.396 127.07 4.396  126.8469 0.2274795   0.05075395        0.05075395
9  125.06 4.630 125.06 4.630  126.8469 0.2159827  -0.38593642       -0.38593642
10 124.45 4.256 124.45 4.256  126.8469 0.2349624  -0.56317801       -0.56317801
11 118.60 4.049 118.60 4.049  126.8469 0.2469746  -2.03677096       -2.03677096
12 128.08 4.408 128.08 4.408  126.8469 0.2268603   0.27974464        0.27974464
13 110.78 3.701 110.78 3.701  126.8469 0.2701972  -4.34122821       -4.34122821
14 121.02 4.187 121.02 4.187  126.8469 0.2388345  -1.39166124       -1.39166124
15 124.09 4.129 124.09 4.129  126.8469 0.2421894  -0.66768845       -0.66768845
16 124.70 4.043 124.70 4.043  126.8469 0.2473411  -0.53101302       -0.53101302
17 123.68 4.262 123.68 4.262  126.8469 0.2346316  -0.74305153       -0.74305153
18 126.34 4.228 126.34 4.228  126.8469 0.2365184  -0.11988780       -0.11988780
19 128.59 4.254 128.59 4.254  126.8469 0.2350729   0.40975890        0.40975890
20 131.46 4.448 131.46 4.448  126.8469 0.2248201   1.03712104        1.03712104
21 127.77 4.330 127.77 4.330  126.8469 0.2309469   0.21319039        0.21319039
22 131.05 5.023 131.05 5.023  126.8469 0.1990842   0.83677372        0.83677372
23 126.34 4.317 126.34 4.317  126.8469 0.2316423  -0.11741617       -0.11741617
24 115.49 3.479 115.49 3.479  126.8469 0.2874389  -3.26441093       -3.26441093
25 119.58 3.815 119.58 3.815  126.8469 0.2621232  -1.90481930       -1.90481930
   NA
1   1
2   1
3   1
4   1
5   1
6   1
7   1
8   1
9   1
10  1
11  1
12  1
13  1
14  1
15  1
16  1
17  1
18  1
19  1
20  1
21  1
22  1
23  1
24  1
25  1

$xlim
[1] 0.0000000 0.2874389

$ylim
[1] -16.08311  20.09820

$zlim
[1]  93.89862 191.06569

$r
[1] 0.2985416

$plot.ratio
[1] 0.8181818

$ticks.major
     tick.x1.major tick.x2.major tick.y1.major tick.y2.major
[1,]     0.2854285     0.2897099     -7.662866     -7.777809
[2,]     0.2976332     0.3020977     -2.037860     -2.068428
[3,]     0.2952298     0.2996582      3.883191      3.941439
[4,]     0.2792013     0.2833893      9.256392      9.395238
[5,]     0.2552062     0.2590343     13.565004     13.768479

$ticks.minor
      tick.x1.minor tick.x2.minor tick.y1.minor tick.y2.minor
 [1,]     0.2805631     0.2825271    -8.9350621    -8.9976075
 [2,]     0.2854285     0.2874265    -7.6628657    -7.7165057
 [3,]     0.2896629     0.2916905    -6.3282319    -6.3725295
 [4,]     0.2931655     0.2952176    -4.9389249    -4.9734974
 [5,]     0.2958465     0.2979174    -3.5048592    -3.5293932
 [6,]     0.2976332     0.2997166    -2.0378605    -2.0521255
 [7,]     0.2984753     0.3005646    -0.5512497    -0.5551084
 [8,]     0.2983483     0.3004367     0.9407262     0.9473113
 [9,]     0.2972560     0.2993368     2.4235620     2.4405269
[10,]     0.2952298     0.2972964     3.8831908     3.9103731
[11,]     0.2923263     0.2943726     5.3066331     5.3437795
[12,]     0.2886233     0.2906437     6.6825291     6.7293069
[13,]     0.2842141     0.2862035     8.0015107     8.0575213
[14,]     0.2792013     0.2811557     9.2563922     9.3211869
[15,]     0.2736917     0.2756075    10.4421902    10.5152856
[16,]     0.2677906     0.2696652    11.5560001    11.6368921
[17,]     0.2615982     0.2634294    12.5967683    12.6849456
[18,]     0.2552062     0.2569926    13.5650040    13.6599591
[19,]     0.2486964     0.2504372    14.4624680    14.5637053
[20,]     0.2421396     0.2438345    15.2918668    15.3989099

$labels
       label.x   label.y label.z.text
[1,] 0.2939913 -7.777809          100
[2,] 0.3065622 -2.068428          120
[3,] 0.3040867  3.941439          140
[4,] 0.2875773  9.395238          160
[5,] 0.2628624 13.768479          180

$polygons
     [,1] [,2]      [,3]      [,4] [,5] [,6] [,7] [,8]
[1,]    0    0 0.2874389 0.2874389   -2    2    2   -2

$ellipse.lims
           [,1]       [,2]
[1,]  0.2407419  0.2985415
[2,] -8.9350621 15.4601557

> plot1$zlim
[1]  93.89862 191.06569
> 
> ## 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"))
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO