Plot a fitted Gaussian-mixture proxy

An autoplot() method for gmm_fit objects, rendering the fitted mixture with ggplot2. The displayed coordinates are reduced to the requested one or two dimensions through the closed-form marginal gmm_marginalise(), so the method works for a proxy of any ambient dimension p.

A one-dimensional request draws the marginal mixture density, optionally with the per-component densities underneath and a rug of the target's samples. A two-dimensional request draws the marginal density as a viridis raster with white contour lines, optionally overlaying each component's mean and a probability-contour ellipse.

Arguments

object

A gmm_fit, typically from fit_proxymix().

dims

Integer vector of length one or two giving the coordinate(s) to display, in 1:p. Defaults to the first two coordinates (or the only coordinate when p == 1).

n_grid

Integer scalar — the number of grid points per axis at which the density is evaluated. A two-dimensional plot evaluates n_grid^2 points.

n_sd

Numeric scalar — how many component standard deviations beyond the extreme component means the plotting window extends.

level

Numeric scalar in (0, 1) — the probability level of the per-component ellipse drawn on a two-dimensional plot.

show_components

Logical scalar — whether to overlay the per-component densities (one dimension) or mean-and-ellipse glyphs (two dimensions).

show_data

Logical scalar — whether to overlay the target's samples, when the fitted target carries any.

...

Currently ignored, present for generic compatibility.

Value

A ggplot object.

Details

The method is registered against the ggplot2::autoplot() generic only when ggplot2 is installed; call it as ggplot2::autoplot(fit) or load ggplot2 first. It returns the ggplot object, so the usual + layering applies for further customisation.

See also

Other classes: glance.gmm_fit, gmm(), gmm_counterfactual_law(), gmm_dim(), gmm_fit(), gmm_n_components(), gmm_target(), gmm_weights(), is_proposal(), tidy.gmm

Examples

samples <- matrix(stats::rnorm(200), ncol = 2)
tgt <- gmm_target_from_samples(samples)
fit <- fit_proxymix(tgt, N = 2L, regime = "sample", max_iter = 25L)
ggplot2::autoplot(fit)

ggplot2::autoplot(fit, dims = 1L)