Add prior object to a plot

Source: R/priors-plot-layers.R

Add prior object to a plot

# S3 method for class 'prior'
lines(
  x,
  xlim = NULL,
  x_seq = NULL,
  x_range_quant = NULL,
  n_points = 1000,
  n_samples = 10000,
  force_samples = FALSE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  show_parameter = if (individual) 1 else NULL,
  individual = FALSE,
  rescale_x = FALSE,
  scale_y2 = NULL,
  ...
)

Arguments

x

a prior

xlim

plotting range of the prior

x_seq

sequence of x coordinates

x_range_quant

quantile used for automatically obtaining x_range if both x_range and x_seq are unspecified. A number in the open interval (0, 0.5); other values are refused. Defaults to 0.005 for all but Cauchy, Student-t, Gamma, inverse-gamma, moment, and inverse-moment distributions that use 0.010.

n_points

number of equally spaced points in the x_range if x_seq is unspecified

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible. For supported ordered mixed measures, exact analytic curves and atoms are retained and the requested samples are attached to each component. Supported scalar ordered literal point totals and mixtures whose total components are all literal points do not sample, store draws, or advance the RNG when force_samples = FALSE. With force_samples = TRUE, their original seeded draws are attached without replacing the analytic measure. Other ordered families retain their existing sampling fallback.

transformation

transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:

lin

linear transformation in form of a + b*x

tanh

hyperbolic tangent transformation

exp

exponential transformation

, or a list containing the transformation function fun, inverse transformation function inv, and derivative of the transformation jac, evaluated on the original support. A custom transformation can also declare output_support = c(lower, upper). Display grids on the transformed scale are evaluated only within these bounds; a wider plotting range is retained without extending the density. Infinite inverse values at support boundaries are omitted from continuous density grids. Point masses retain their transformed locations.

transformation_arguments

a list with named arguments for the transformation

transformation_settings

boolean indicating whether the explicitly supplied x_seq or x_range is on the transformed scale. Automatically selected prior-coordinate ranges are always obtained on the source scale before the density family's existing transformation handling.

show_parameter

which parameter should be returned in case of multiple parameters per prior. Useful when priors for the omega parameter are plotted and individual = TRUE. For weight functions, k selects the publication weight omega of the k-th p-value interval in ascending order, as show_figures in plot.prior() and the order of the summary tables; 1 is the reference weight fixed at 1.

individual

should individual densities be returned (e.g., in case of weightfunction)

rescale_x

allows to rescale x-axis in case a weightfunction is plotted.

scale_y2

scaling factor for a secondary axis. The default NULL reuses the probability mapping from the active mixed plot when one is available.

...

additional arguments

Value

lines.prior returns NULL.

Details

For ordered and Dirichlet simplex priors, show_parameter is a single figure between 1 and the number of figures; other values stop with an error. The default draws the default selection of plot.prior().