Implementation of a graphical device developed by Rex Galbraith to display several estimates of the same quantity that have different standard errors.
radialplot(x, ...)# S3 method for default
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", sigdig = 2, show.numbers = FALSE, pch = 21,
levels = NA, clabel = "", bg = c("white", "red"), title = TRUE,
k = 0, markers = NULL, alpha = 0.05, ...)
# S3 method for fissiontracks
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "arcsin", sigdig = 2, show.numbers = FALSE, pch = 21,
levels = NA, clabel = "", bg = c("white", "red"), title = TRUE,
markers = NULL, k = 0, exterr = TRUE, alpha = 0.05, ...)
# S3 method for UPb
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", type = 4, cutoff.76 = 1100,
cutoff.disc = c(-15, 5), show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
exterr = TRUE, common.Pb = 0, alpha = 0.05, ...)
# S3 method for PbPb
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
exterr = TRUE, common.Pb = 1, alpha = 0.05, ...)
# S3 method for ArAr
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
exterr = TRUE, i2i = FALSE, alpha = 0.05, ...)
# S3 method for UThHe
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
alpha = 0.05, ...)
# S3 method for ReOs
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
exterr = TRUE, i2i = TRUE, alpha = 0.05, ...)
# S3 method for SmNd
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
exterr = TRUE, i2i = TRUE, alpha = 0.05, ...)
# S3 method for RbSr
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
exterr = TRUE, i2i = TRUE, alpha = 0.05, ...)
# S3 method for LuHf
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
exterr = TRUE, i2i = TRUE, alpha = 0.05, ...)
# S3 method for ThU
radialplot(x, from = NA, to = NA, t0 = NA,
transformation = "log", show.numbers = FALSE, pch = 21, levels = NA,
clabel = "", bg = c("white", "red"), markers = NULL, k = 0,
i2i = TRUE, alpha = 0.05, ...)
Either an [n x 2] matix of (transformed) values z
and their standard errors s
OR
and object of class fissiontracks, UThHe,
ArAr, ReOs, SmNd, RbSr, LuHf,
ThU, PbPb or UPb
additional arguments to the generic points
function
minimum age limit of the radial scale
maximum age limit of the radial scale
central value
one of either log, linear or
(if x has class fissiontracks), arcsin.
the number of significant digits of the numerical values reported in the title of the graphical output.
boolean flag (TRUE to show grain
numbers)
plot character (default is a filled circle)
a vector with additional values to be displayed as different background colours of the plot symbols.
label of the colour legend
a vector of two background colours for the plot symbols.
If levels=NA, then only the first colour is used. If
levels is a vector of numbers, then bg is used to
construct a colour ramp.
add a title to the plot?
number of peaks to fit using the finite mixture models of
Galbraith and Laslett (1993). Setting k='auto'
automatically selects an optimal number of components based on
the Bayes Information Criterion (BIC). Setting k='min'
estimates the minimum value using a three parameter model
consisting of a Normal distribution truncated by a discrete
component.
vector of ages of radial marker lines to add to the plot.
cutoff value for confidence intervals
propagate the external sources of uncertainty into the mixture model errors?
scalar indicating whether to plot the
\(^{207}\)Pb/\(^{235}\)U age (type=1), the
\(^{206}\)Pb/\(^{238}\)U age (type=2), the
\(^{207}\)Pb/\(^{206}\)Pb age (type=3), the
\(^{207}\)Pb/\(^{206}\)Pb-\(^{206}\)Pb/\(^{238}\)U age
(type=4), or the (Wetherill) concordia age
(type=5)
the age (in Ma) below which the
\(^{206}\)Pb/\(^{238}\)U and above which the
\(^{207}\)Pb/\(^{206}\)Pb age is used. This parameter is
only used if type=4.
two element vector with the maximum and minimum
percentage discordance allowed between the
\(^{207}\)Pb/\(^{235}\)U and \(^{206}\)Pb/\(^{238}\)U
age (if \(^{206}\)Pb/\(^{238}\)U < cutoff.76) or
between the \(^{206}\)Pb/\(^{238}\)U and
\(^{207}\)Pb/\(^{206}\)Pb age (if
\(^{206}\)Pb/\(^{238}\)U > cutoff.76). Set
cutoff.disc=NA if you do not want to use this filter.
apply a common lead correction using one of three methods:
1: use the isochron intercept as the initial Pb-composition
2: use the Stacey-Kramer two-stage model to infer the initial
Pb-composition
3: use the Pb-composition stored in
settings('iratio','Pb206Pb204') and
settings('iratio','Pb207Pb204')
`isochron to intercept': calculates the initial (aka
`inherited', `excess', or `common')
\(^{40}\)Ar/\(^{36}\)Ar, \(^{207}\)Pb/\(^{204}\)Pb,
\(^{87}\)Sr/\(^{86}\)Sr, \(^{143}\)Nd/\(^{144}\)Nd,
\(^{187}\)Os/\(^{188}\)Os or \(^{176}\)Hf/\(^{177}\)Hf
ratio from an isochron fit. Setting i2i to FALSE
uses the default values stored in
settings('iratio',...). When applied to data of class
ThU, setting i2i to TRUE applies a
detrital Th-correction.
The radial plot (Galbraith, 1988, 1990) is a graphical device that was specifically designed to display heteroscedastic data, and is constructed as follows. Consider a set of dates \(\{t_1,...,t_i,...,t_n\}\) and uncertainties \(\{s[t_1],...,s[t_i],...,s[t_n]\}\). Define \(z_i = z[t_i]\) to be a transformation of \(t_i\) (e.g., \(z_i = log[t_i]\)), and let \(s[z_i]\) be its propagated analytical uncertainty (i.e., \(s[z_i] = s[t_i]/t_i\) in the case of a logarithmic transformation). Create a scatterplot of \((x_i,y_i)\) values, where \(x_i = 1/s[z_i]\) and \(y_i = (z_i-z_\circ)/s[z_i]\), where \(z_\circ\) is some reference value such as the mean. The slope of a line connecting the origin of this scatterplot with any of the \((x_i,y_i)\)s is proportional to \(z_i\) and, hence, the date \(t_i\). These dates can be more easily visualised by drawing a radial scale at some convenient distance from the origin and annotating it with labelled ticks at the appropriate angles. While the angular position of each data point represents the date, its horizontal distance from the origin is proportional to the precision. Imprecise measurements plot on the left hand side of the radial plot, whereas precise age determinations are found further towards the right. Thus, radial plots allow the observer to assess both the magnitude and the precision of quantitative data in one glance.
Galbraith, R.F., 1988. Graphical display of estimates having differing standard errors. Technometrics, 30(3), pp.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), pp.207-214.
Galbraith, R.F. and Laslett, G.M., 1993. Statistical models for mixed fission track ages. Nuclear Tracks and Radiation Measurements, 21(4), pp.459-470.
# NOT RUN {
data(examples)
radialplot(examples$FT1)
dev.new()
radialplot(examples$LudwigMixture,k='min')
# }
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