Produces a plot of boxes whose widths correspond to the cumulative amount of \(^{39}\)Ar (or any other variable), and whose heights express the analytical uncertainties. Only propagates the analytical uncertainty associated with decay constants and J-factors after computing the plateau composition.
agespectrum(x, ...)# S3 method for default
agespectrum(x, alpha = 0.05, plateau = TRUE,
random.effects = TRUE, plateau.col = rgb(0, 1, 0, 0.5),
non.plateau.col = rgb(0, 1, 1, 0.5), sigdig = 2, line.col = "red",
lwd = 2, title = TRUE, show.ci = TRUE, xlab = "cumulative fraction",
ylab = "age [Ma]", ...)
# S3 method for ArAr
agespectrum(x, alpha = 0.05, plateau = TRUE,
random.effects = TRUE, plateau.col = rgb(0, 1, 0, 0.5),
non.plateau.col = rgb(0, 1, 1, 0.5), sigdig = 2, exterr = TRUE,
line.col = "red", lwd = 2, i2i = FALSE, ...)
a three-column matrix whose first column gives the amount of \(^{39}\)Ar in each aliquot, and whose second and third columns give the age and its uncertainty.
OR
an object of class ArAr
optional parameters to the generic plot function
the confidence level of the error bars/boxes and confidence intervals.
logical flag indicating whether a plateau age should
be calculated. If plateau=TRUE, the function will
compute the weighted mean of the largest succession of steps
that pass the Chi-square test for age homogeneity. If
TRUE, returns a list with plateau parameters.
if TRUE, computes the weighted mean
using a random effects model with two parameters: the mean and
the dispersion. This is akin to a `model-3' isochron regression.
if FALSE, attributes any excess dispersion to an
underestimation of the analytical uncertainties. This akin to a
`model-1' isochron regression.
the fill colour of the rectangles used to mark the steps belonging to the age plateau.
if plateau=TRUE, the steps that do
NOT belong to the plateau are given a different colour.
the number of significant digits of the numerical
values reported in the title of the graphical output (only used
if plateau=TRUE).
colour of the average age line
width of the average age line
add a title to the plot?
show a 100(1-\(\alpha\))% confidence interval for the plateau age as a grey band
x-axis label
y-axis label
propagate the external (decay constant and calibration factor) uncertainties?
`isochron to intercept':
calculates the initial (aka `inherited',
`excess', or `common') \(^{40}\)Ar/\(^{36}\)Ar ratio from
an isochron fit. Setting i2i to FALSE uses the
default values stored in settings('iratio',...)
If plateau=TRUE, returns a list with the following
items:
a 3-element vector with:
x: the plateau mean
s[x]: the estimated standard deviation of x
ci[x]: the width of a 100(1-\(\alpha\))% confidence interval of
t
a 3-element vector with:
w: the overdispersion, i.e. the standard deviation of the
Normal distribution that is assumed to describe the true ages.
ll: the width of the lower half of a 100(1-\(\alpha\))%
confidence interval for the overdispersion
ul: the width of the upper half of a 100(1-\(\alpha\))%
confidence interval for the overdispersion
the degrees of freedom for the weighted mean plateau fit
the mean square of the weighted deviates of the plateau
the p-value of a Chi-square test with \(df=n-2\) degrees of freedom, where \(n\) is the number of steps in the plateau and 2 degrees of freedom have been removed to estimate the mean and the dispersion.
the fraction of \(^{39}\)Ar contained in the plateau
plot parameters for the weighted mean (see
weightedmean), which are not used in the age
spectrum
indices of the steps that are retained for the plateau age calculation
IsoplotR defines the `plateau age' as the weighted mean age
of the longest sequence (in terms of cumulative \(^{39}\)Ar
content) of consecutive heating steps that pass the modified
Chauvenet criterion (see weightedmean). Note that
this definition is different (and simpler) than the one used by
Isoplot (Ludwig, 2003). However, it is important to mention
that all definitions of an age plateau are heuristic by nature and
should not be used for quantitative inference.
# NOT RUN {
data(examples)
agespectrum(examples$ArAr,ylim=c(0,80))
# }
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