Plot cross ranking by usefulness vs mid-parent index — hbr_plot_cross

Visualises how the ranking of recommended crosses changes when progeny variance is accounted for. Each point is one cross; the x-axis is the rank by mid-parent index alone, the y-axis is the rank by usefulness (Schnell and Utz, 1975). Points on the diagonal are crosses whose recommendation is identical under the two criteria; points off the diagonal are crosses whose rank depends on progeny variance. The most displaced cross is optionally highlighted and labelled.

Usage

hbr_plot_cross_ranking(
  x,
  top_n = 20L,
  highlight = c("auto"),
  label_size = 3,
  palette = "haplobreed",
  theme = "haplobreed",
  output_dir = NULL,
  file_name = "cross_ranking",
  width = 6,
  height = 6,
  formats = c("pdf", "png"),
  verbose = TRUE
)

Arguments

x: Either an `hgsi_advanced` object (from [hbr_advanced_decision()]) or a data.frame with columns `parent_a`, `parent_b`, `mid_index`, and `uc`.
top_n: Number of top crosses to display. Crosses are ordered by `uc`. Default `20L`.
highlight: A cross to annotate. Either a length-2 character vector `c(parent_a, parent_b)`, or `"auto"` to highlight the cross with the largest absolute rank displacement (default).
label_size: Text size for the highlight label.
palette: One of [hbr_palette_names()].
theme: One of [hbr_theme_names()].
output_dir: Directory for the figure. `NULL` returns the plot without writing anything.
file_name: Base name for the output file (without extension).
width, height: Figure dimensions in inches.
formats: Character vector of output formats. Any of `"pdf"`, `"png"`, `"tiff"`, `"svg"`.
verbose: Print progress.

Value

Invisibly, a list with `plot` (the ggplot object) and `files` (the paths written, or `character(0)` if `output_dir` is `NULL`). The `plot` element can be assigned and modified further with ggplot2.

Details

Use this to document that the genetic map is doing real work in the cross-design step, or to justify why a cross with a lower mid-parent index is nonetheless recommended.

Examples

toy <- system.file("extdata", "toy", package = "HaploBreedR")
crosses <- data.frame(
  parent_a  = sprintf("IRIS_%04d", 1:20),
  parent_b  = sprintf("IRIS_%04d", 21:40),
  mid_index = runif(20, 5, 12),
  uc        = runif(20, 5, 15))
hbr_plot_cross_ranking(crosses, top_n = 20)
#>   19 of 20 top crosses reordered (max |shift| = 17)