This is the heavily requested geometry for interpolating between ternary values, results being rendered using contours on a ternary mesh.
geom_interpolate_tern(
mapping = NULL,
data = NULL,
stat = "InterpolateTern",
position = "identity",
...,
method = "auto",
formula = value ~ poly(x, y, degree = 1),
lineend = "butt",
linejoin = "round",
linemitre = 1,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)stat_interpolate_tern(
mapping = NULL,
data = NULL,
geom = "interpolate_tern",
position = "identity",
...,
method = "auto",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
n = 80,
formula = value ~ poly(x, y, degree = 1),
base = "ilr"
)
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.
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)).
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.
Smoothing method (function) to use, accepts either
NULL or a character vector, e.g. "lm", "glm", "gam", "loess"
or a function, e.g. MASS::rlm or mgcv::gam, stats::lm, or stats::loess.
"auto" is also accepted for backwards compatibility. It is equivalent to
NULL.
For method = NULL the smoothing method is chosen based on the
size of the largest group (across all panels). stats::loess() is
used for less than 1,000 observations; otherwise mgcv::gam() is
used with formula = y ~ s(x, bs = "cs") with method = "REML". Somewhat anecdotally,
loess gives a better appearance, but is \(O(N^{2})\) in memory,
so does not work for larger datasets.
If you have fewer than 1,000 observations but want to use the same gam()
model that method = NULL would use, then set
method = "gam", formula = y ~ s(x, bs = "cs").
Formula to use in smoothing function, eg. y ~ x,
y ~ poly(x, 2), y ~ log(x). NULL by default, in which case
method = NULL implies formula = y ~ x when there are fewer than 1,000
observations and formula = y ~ s(x, bs = "cs") otherwise.
Line end style (round, butt, square).
Line join style (round, mitre, bevel).
Line mitre limit (number greater than 1).
If FALSE, the default, missing values are removed with
a warning. If TRUE, missing values are silently removed.
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. To include legend keys for all levels, even
when no data exists, use TRUE. If NA, all levels are shown in legend,
but unobserved levels are omitted.
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. annotation_borders().
Use to override the default connection between
geom_smooth() and stat_smooth(). For more information about overriding
these connections, see how the stat and geom
arguments work.
number of grid points in each direction
the base transformation of the data, options include 'identity' (ie direct on the cartesian space), or 'ilr' which means to use the isometric log ratio transformation.
ggtern:::rd_aesthetics("geom", "InterpolateTern")
Nicholas Hamilton
data(Feldspar)
ggtern(Feldspar,aes(Ab,An,Or,value=T.C)) +
stat_interpolate_tern(geom="polygon",
formula=value~x+y,
method=lm,n=100,
breaks=seq(0,1000,by=100),
aes(fill=..level..),expand=1) +
geom_point()
Run the code above in your browser using DataLab