ggplot2 visualizations for hsquared results — hsquared-autoplot • hsquared

Usage

# S3 method for class 'hsquared_fit'
autoplot(
  object,
  type = c("variance", "breeding_values", "g_matrix", "g_geometry", "reaction_norm",
    "rr_eigenfunctions", "rr_surface"),
  ...
)

# S3 method for class 'hs_gwas'
autoplot(object, type = c("manhattan", "qq"), ...)

Arguments

object: An hsquared_fit or hs_gwas object.
type: Which figure to draw (see Details).
...: Figure-specific options passed through: low_h2 (the genetic-correlation heatmap flags off-diagonal cells involving a trait with h^2 < low_h2 as imprecise; default 0.1), at/n (the reaction-norm covariate evaluation points), and correlation (rr_surface: draw the genetic-correlation surface instead of the covariance surface).

Value

A ggplot object.

Details

autoplot() methods render the quantitative-genetic results an hsquared_fit (or a gwas() scan) carries as ggplot2 objects, in the style of the brms/bayesplot ecosystem and consistent with the sister packages drmTMB/gllvmTMB. They are uncertainty-first: where the fit carries the experimental standard errors / reliabilities, the figures show them (clearly labelled experimental and asymptotic).

Available types for autoplot.hsquared_fit():

"variance" (default) – a horizontal forest of the variance components and per-trait h^2, each with approximate 95% intervals (+/- 1.96 * SE) when the fit carries the experimental standard errors.
"breeding_values" – a sorted caterpillar of the estimated breeding values, with +/- 1.96 * sqrt(PEV) bands when prediction error variances are available, faceted by trait for multivariate fits.
"g_matrix" – a rotation-invariant genetic-correlation heatmap of the estimated G for multivariate fits (correlations are invariant to the factor rotation; raw loadings are never plotted – the ratified cross-lane convention). Off-diagonal cells involving a low-h^2 trait are flagged as imprecise (threshold low_h2, default 0.1).
"g_geometry" – a scree of the rotation-invariant genetic eigenstructure (eigenvalues = variance per genetic axis, with percent variance explained) for multivariate fits. Axis directions / loadings are never drawn (rotation-arbitrary; span-ambiguous under repeated eigenvalues).
"reaction_norm" – for random-regression fits, the genetic-variance and heritability trajectories across the covariate (faceted). The heritability trajectory carries the same caveat as rr_heritability(): with a homogeneous residual and no permanent-environment term it can overstate h^2(t) for repeated-records designs.
"rr_eigenfunctions" – for random-regression fits, the rotation-invariant eigenfunctions psi_j(t) of K_g as covariate functions (faceted by axis, labelled by percent genetic variance). Signs are arbitrary and the curves are span-ambiguous under repeated eigenvalues (do not over-read).
"rr_surface" – for random-regression fits, the genetic covariance surface S(s, t) = phi(s)' K_g phi(t) over the covariate grid as a heatmap (pass correlation = TRUE for the genetic-correlation surface). Supplied-K_g descriptive.

autoplot() on a gwas() scan (hs_gwas) draws type = "manhattan" (default) or type = "qq" (observed vs expected -log10(p) with a y = x null and the genomic-inflation lambda_GC as a diagnostic). Both carry the EXPERIMENTAL, NOT-genome-wide-calibrated caveat. The Julia lane has a fixed-panel calibration smoke harness, but R significance thresholds are not activated.

The figure helpers are deliberately modular (each takes a tidy data frame and returns a ggplot) so they can be factored into a shared visualization package later.