`R/model-averaging-plots.R`

`plot_posterior.Rd`

Plot samples from the mixed posterior distributions

```
plot_posterior(
samples,
parameter,
plot_type = "base",
prior = FALSE,
n_points = 1000,
n_samples = 10000,
force_samples = FALSE,
transformation = NULL,
transformation_arguments = NULL,
transformation_settings = FALSE,
rescale_x = FALSE,
par_name = NULL,
dots_prior = list(),
...
)
```

- samples
samples from a posterior distribution for a parameter generated by mix_posteriors.

- parameter
parameter name to be plotted. Use

`"PETPEESE"`

for PET-PEESE plot with parameters`"PET"`

and`"PEESE"`

, and`"weightfunction"`

for plotting a weightfunction with parameters`"omega"`

.- plot_type
whether to use a base plot

`"base"`

or ggplot2`"ggplot"`

for plotting.- prior
whether prior distribution should be added to the figure

- 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

- 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
also known as Fisher's z transformation

- exp
exponential transformation

, or a list containing the transformation function

`fun`

, inverse transformation function`inv`

, and the Jacobian of the transformation`jac`

. See examples for details.- transformation_arguments
a list with named arguments for the

`transformation`

- transformation_settings
boolean indicating whether the settings the

`x_seq`

or`x_range`

was specified on the transformed support- rescale_x
allows to rescale x-axis in case a weightfunction is plotted.

- par_name
a type of parameter for which the prior is specified. Only relevant if the prior corresponds to a mu parameter that needs to be transformed.

- dots_prior
additional arguments for the prior distribution plot

- ...
additional arguments

`plot_posterior`

returns either `NULL`

or
an object of class 'ggplot' if plot_type is `plot_type = "ggplot"`

.