aKDE: Adaptive kernel density estimates

In tobiste/laftr: Calculates the ages from LA-ICP-MS based fission track dating

Adaptive kernel density estimates

Description

Modification of ggplot2::stat_density() for kernel density estimates using a combination of the Botev (2010) bandwidth selector and the Abramson (1982) adaptive kernel bandwidth modifier.

Usage

stat_aKDE(
  mapping = NULL,
  data = NULL,
  stat = "DensityAdaptive",
  geom = "area",
  position = "stack",
  ...,
  from = NA,
  to = NA,
  bw = NA,
  adjust = 1,
  kernel = "gaussian",
  n = 512,
  adaptive = TRUE,
  na.rm = FALSE,
  bounds = c(-Inf, Inf),
  show.legend = NA,
  orientation = NA,
  inherit.aes = TRUE
)

geom_aKDE(
  mapping = NULL,
  data = NULL,
  stat = "DensityAdaptive",
  position = "identity",
  ...,
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE,
  outline.type = "upper"
)

Arguments

mapping	Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
data	The data to be displayed in this layer. There are three options: If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot(). A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created. A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).
geom, stat	Use to override the default connection between geom_density() and stat_density(). For more information about overriding these connections, see how the stat and geom arguments work.
position	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following: The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as "jitter". For more information and other ways to specify the position, see the layer position documentation.
...	Other arguments passed on to layer()'s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through .... Unknown arguments that are not part of the 4 categories below are ignored. Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data. When constructing a layer using a ⁠stat_*()⁠ function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept. Inversely, when constructing a layer using a ⁠geom_*()⁠ function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept. The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.
from, to	the left and right-most points of the grid at which the density is to be estimated
bw	the bandwidth of the KDE. If NULL, bw will be calculated automatically using the algorithm by Botev et al. (2010).
adjust	A multiplicate bandwidth adjustment. This makes it possible to adjust the bandwidth while still using the a bandwidth estimator. For example, adjust = 1/2 means use half of the default bandwidth.
kernel	Kernel. See list of available kernels in density().
n	number of equally spaced points at which the density is to be estimated, should be a power of two, see density() for details
adaptive	logical flag controlling if the adaptive KDE modifier of Abramson (1982) is used
na.rm	If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.
bounds	Known lower and upper bounds for estimated data. Default c(-Inf, Inf) means that there are no (finite) bounds. If any bound is finite, boundary effect of default density estimation will be corrected by reflecting tails outside bounds around their closest edge. Data points outside of bounds are removed with a warning.
show.legend	logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.
orientation	The orientation of the layer. The default (NA) automatically determines the orientation from the aesthetic mapping. In the rare event that this fails it can be given explicitly by setting orientation to either "x" or "y". See the Orientation section for more detail.
inherit.aes	If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().
outline.type	Type of the outline of the area; "both" draws both the upper and lower lines, "upper"/"lower" draws the respective lines only. "full" draws a closed polygon around the area.
weight	numeric vector of non-negative observation weights, hence of same length as x. The default NULL is equivalent to weights = rep(1/nx, nx) where nx is the length of (the finite entries of) x[]. If na.rm = TRUE and there are NA's in x, they and the corresponding weights are removed before computations. In that case, when the original weights have summed to one, they are re-scaled to keep doing so. Ignored if adaptive is TRUE.

Source

Algorithm for adaptive kernel is modified from IsoplotR. The algorithm for the optimal kernel bandwidth is from provenance::botev().

Examples

data("sample")
example <- age_ICP(sample, zeta = c(0.1188, 0.0119))
# IsoplotR::kde(example$ages$t)
ggplot2::ggplot(data = example$ages, mapping = ggplot2::aes(x = t)) +
  stat_aKDE(adaptive = TRUE) +
  stat_aKDE(adaptive = FALSE, color = "red", fill = NA)

ggplot2::ggplot(
  data = example$ages,
  mapping = ggplot2::aes(x = t, weight = t / st)
) +
  geom_aKDE(
    ggplot2::aes(y = ggplot2::after_stat(scaled)),
    kernel = "epanechnikov", fill = "steelblue", alpha = .75
  ) +
  ggplot2::geom_histogram(
    ggplot2::aes(y = ggplot2::after_stat(ncount)),
    color = "grey", fill = "grey", alpha = .5
  ) +
  ggplot2::geom_rug(alpha = 0.5)

tobiste/laftr documentation built on Feb. 10, 2025, 11:10 p.m.