Plotting an ordination plot for a GLLVM model

This function takes a GLLVM model fit and plots an ordination plot with a random (or specified) selection of draws

Usage

ordiplot(
  object,
  choices = c(1, 2),
  type = "species",
  summary_stat = "median",
  ndraws = 0,
  draw_ids = NULL,
  size = c(2, 1),
  alpha = c(1, 0.5),
  shape = c(18, 16),
  geom = "point",
  errorbar_range = 0.75,
  errorbar_linewidth = 1
)

Arguments

object: The jsdmStanFit model object
choices: Which latent variables to plot as dimensions, by default c(1,2)
type: Whether to plot sites or species, default "species".
summary_stat: The summary statistic used to plot overall averages of the posterior sample. By default this is "median", and NULL will result in no summary being included
ndraws: How many individual draws to include in plot, by default 0. Setting this to 0 will result in no individual draws being included
draw_ids: Which draws to include in plot (overrides ndraws)
size: The size of the points in the graph, specified as a two-element vector with the first being used for the summary points and the second the individual draws, default c(2,1)
alpha: The transparency/alpha of the points in the graph, specified as a two-element vector with the first being used for the summary points and the second the individual draws, default c(1,0.5)
shape: The shape of the points in the graph, specified as a two-element vector with the first being used for the summary points and the second the individual draws, default c(18,16)
geom: Which geom from ggplot2 is used for the summary statistic by default "point", or alternatively can be "text"
errorbar_range: If specified, the central range of the data that is covered by the errorbar. Needs to be given as either NULL (i.e. no errorbar), or a number between 0 and 1, default 0.75.
errorbar_linewidth: The linewidth of the error bar, by default 1.

Value

A ggplot object that can be customised using the ggplot2 package

Examples

if (FALSE) { # \dontrun{
# First simulate data and fit the model:
gllvm_data <- jsdm_sim_data(
  method = "gllvm", N = 100, S = 6, D = 3,
  family = "bernoulli"
)
gllvm_fit <- stan_jsdm(
  dat_list = gllvm_data, method = "gllvm",
  family = "bernoulli"
)

ordiplot(gllvm_fit)
# now plot the 1st and 3rd latent variables against each other for the sites:
ordiplot(gllvm_fit, choices = c(1, 3), type = "sites")
} # }