plot_steepness_regression: plot steepness regression

In EloSteepness: Bayesian Dominance Hierarchy Steepness via Elo Rating and David's Scores

plot steepness regression

Description

visually combine individual scores with group-level steepness

Usage

plot_steepness_regression(
  x,
  adjust = 3,
  color = TRUE,
  width_fac = 0.1,
  axis_extend = 0.1
)

Arguments

x	result from elo_steepness_from_matrix, elo_steepness_from_sequence or davids_steepness
adjust	numeric, parameter for smoothing posterior of individual scores
color	logical, default is TRUE where individuals get color- coded. If FALSE: a gray scale is used. It is also possible to hand over a vector with colors, which then must be correspond in length to the number of individuals.
width_fac	numeric, relative width of posterior distributions. This is actually affects the 'height' but since the posteriors are rotated it visually represents width.
axis_extend	numeric, an extension factor to extend the horizontal axis to leave space for the posteriors. When set to 0 the axis stops at n (the number of individuals, which represents the lowest rank).

Value

a plot

Examples

data("bonobos", package = "EloRating")
res <- davids_steepness(bonobos, refresh = 0, iter = 1000)
plot_steepness_regression(res, width_fac = 0.5)

EloSteepness documentation built on Sept. 21, 2023, 1:06 a.m.