Plot a dataset as a Cumulative Age Distribution (CAD), also known as a `empirical cumulative distribution function'.
cad(x, ...)# S3 method for default
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
colmap = "heat.colors", col = "black", ...)
# S3 method for detritals
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
colmap = "heat.colors", ...)
# S3 method for UPb
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
col = "black", type = 4, cutoff.76 = 1100, cutoff.disc = c(-15, 5),
common.Pb = 0, ...)
# S3 method for PbPb
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
col = "black", common.Pb = 1, ...)
# S3 method for ArAr
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
col = "black", i2i = FALSE, ...)
# S3 method for ThU
cad(x, pch = NA, verticals = TRUE, xlab = "age [ka]",
col = "black", i2i = FALSE, ...)
# S3 method for ReOs
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
col = "black", i2i = TRUE, ...)
# S3 method for SmNd
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
col = "black", i2i = TRUE, ...)
# S3 method for RbSr
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
col = "black", i2i = TRUE, ...)
# S3 method for LuHf
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
col = "black", i2i = TRUE, ...)
# S3 method for UThHe
cad(x, pch = NA, verticals = TRUE, xlab = "age [Ma]",
col = "black", ...)
# S3 method for fissiontracks
cad(x, pch = NA, verticals = TRUE,
xlab = "age [Ma]", col = "black", ...)
a numerical vector OR an object of class UPb,
PbPb, ArAr, UThHe, fissiontracks,
ReOs, RbSr, SmNd, LuHf, ThU
or detritals
optional arguments to the generic plot function
plot character to mark the beginning of each CAD step
logical flag indicating if the horizontal lines of the CAD should be connected by vertical lines
x-axis label
an optional string with the name of one of R's
built-in colour palettes (e.g., heat.colors,
terrain.colors, topo.colors, cm.colors),
which are to be used for plotting data of class
detritals.
colour to give to single sample datasets (not applicable
if x has class detritals)
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-age 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',...) or zero (for the Pb-Pb
method). When applied to data of class ThU, setting
i2i to TRUE applies a detrital Th-correction.
Empirical cumulative distribution functions or cumulative age distributions CADs are the most straightforward way to visualise the probability distribution of multiple dates. Suppose that we have a set of \(n\) dates \(t_i\). The the CAD is a step function that sets out the rank order of the dates against their numerical value:
\(CAD(t) = \sum_i 1(t<t_i)/n\)
where 1(\(\ast\)) = 1 if \(\ast\) is true and 1(\(\ast\)) = 0 if \(\ast\) is false. CADs have two desirable properties (Vermeesch, 2007). First, they do not require any pre-treatment or smoothing of the data. This is not the case for histograms or kernel density estimates. Second, it is easy to superimpose several CADs on the same plot. This facilitates the intercomparison of multiple samples. The interpretation of CADs is straightforward but not very intuitive. The prominence of individual age components is proportional to the steepness of the CAD. This is different from probability density estimates such as histograms, in which such components stand out as peaks.
Vermeesch, P., 2007. Quantitative geomorphology of the White Mountains (California) using detrital apatite fission track thermochronology. Journal of Geophysical Research: Earth Surface, 112(F3).
# NOT RUN {
data(examples)
cad(examples$DZ,verticals=FALSE,pch=20)
# }
Run the code above in your browser using DataLab