Plot Defective Densities — plot

Plots panels that contain a set of densities for each level of the specified defective factor in the data. These densities are defective; their areas are relative to the respective proportions of the defective factor levels. Across all levels, the area sums to 1. Optionally, posterior/prior predictive densities can be overlaid.

Usage

plot_density(
  input,
  post_predict = NULL,
  prior_predict = NULL,
  subject = NULL,
  quants = c(0.025, 0.975),
  functions = NULL,
  factors = NULL,
  defective_factor = "R",
  n_cores = 1,
  n_post = 50,
  layout = NA,
  to_plot = c("data", "posterior", "prior")[1:2],
  use_lim = c("data", "posterior", "prior")[1:2],
  legendpos = c("topright", "top"),
  posterior_args = list(),
  prior_args = list(),
  ...
)

Arguments

input: Either an emc object or a data frame, or a list of such objects.
post_predict: Optional posterior predictive data (matching columns) or list thereof.
prior_predict: Optional prior predictive data (matching columns) or list thereof.
subject: Subset the data to a single subject (by index or name).
quants: Numeric vector of credible interval bounds (e.g. c(0.025, 0.975)).
functions: A function (or list of functions) that create new columns in the datasets or predictives
factors: Character vector of factor names to aggregate over; defaults to plotting full data set ungrouped by factors if NULL.
defective_factor: Name of the factor used for the defective CDF (default "R").
n_cores: Number of CPU cores to use if generating predictives from an emc object.
n_post: Number of posterior draws to simulate if needed for predictives.
layout: Numeric vector used in par(mfrow=...); use NA for auto-layout.
to_plot: Character vector: any of "data", "posterior", "prior".
use_lim: Character vector controlling which source(s) define xlim.
legendpos: Character vector controlling the positions of the legends
posterior_args: Optional list of graphical parameters for posterior lines/ribbons.
prior_args: Optional list of graphical parameters for prior lines/ribbons.
...: Other graphical parameters for the real data lines.

Examples

# Plot defective densities for each subject and the factor combination in the design:
plot_density(forstmann)

# or for one subject:
plot_density(forstmann, subject = 1)

# Now collapsing across subjects and using a different defective factor:
plot_density(forstmann, factors = "S", defective_factor = "E")

# Or plot posterior predictives
plot_density(samples_LNR, n_post = 10)