coevfit objectR/coev_plot_selection_gradient.R
coev_plot_selection_gradient.RdPlot a heatmap of the selection gradient for two variables from a fitted
coevfit object.
coev_plot_selection_gradient(
object,
var1,
var2,
contour = FALSE,
limits = c(-2.5, 2.5)
)An object of class coevfit
A character string equal to one of the coevolving variables in the model
A character string equal to one of the coevolving variables in the model
Logical (defaults to FALSE); whether to show white contour lines to indicate where selection is stronger than drift
A numeric vector of length 2 (defaults to c(-2.5, 2.5));
specifying the lower limit and the upper limit of the x and y axes.
A ggplot object
The selection gradient is operationalised as the ratio of the change in a trait due to deterministic selection \(\Delta\alpha\) to the change in a trait due to stochastic drift \(\sigma\). Values between -1 and 1 indicate parameter space where the change due to drift is greater than change due to selection on the trait. Conversely, values greater than 1 (or less than -1) indicate parameter space where positive (or negative) selection is stronger than drift.
If three or more traits were included in the model, other traits are held at their median values during the computations. Note that selection gradient plots can potentially produce misleading pictures of coevolutionary dynamics when other traits are held constant in models with three or more traits.
If the plot does not look right, the user might try zooming out from the default parameter space by setting wider limits. For some variables (e.g., continuous and count variables), the default limits may not be suitable.
Ringen, E., Martin, J. S., & Jaeggi, A. (2021). Novel phylogenetic methods
reveal that resource-use intensification drives the evolution of "complex"
societies. EcoEvoRXiv. doi:10.32942/osf.io/wfp95
Sheehan, O., Watts, J., Gray, R. D., Bulbulia, J., Claessens, S., Ringen,
E. J., & Atkinson, Q. D. (2023). Coevolution of religious and political
authority in Austronesian societies. Nature Human Behaviour,
7(1), 38-45. 10.1038/s41562-022-01471-y
if (FALSE) { # \dontrun{
# fit dynamic coevolutionary model
fit <- coev_fit(
data = authority$data,
variables = list(
political_authority = "ordered_logistic",
religious_authority = "ordered_logistic"
),
id = "language",
tree = authority$phylogeny,
# additional arguments for cmdstanr::sample()
chains = 4,
parallel_chains = 4,
seed = 1
)
# plot selection gradient
coev_plot_selection_gradient(
object = fit,
var1 = "political_authority",
var2 = "religious_authority"
)
} # }