Summary of prior distributions

Print a summary of prior distribution details.

Usage

# S3 method for nma_prior
summary(object, ..., probs = c(0.5, 0.95), digits = 2, trunc = NULL)

Arguments

object

Prior distribution as a nma_prior object

...

Additional arguments, not used

probs

Numeric vector of probabilities to calculate prior intervals

digits

Number of digits to display

trunc

Optional numeric vector of length 2, giving the truncation limits of the prior distribution. Useful if a real-valued prior is assigned to a positive-valued parameter, then trunc = c(0, Inf) will give the correct prior intervals. By default, truncation is not used.

Value

A data frame is returned invisibly, giving the prior intervals

Examples

summary(normal(location = 0, scale = 1))
#> A Normal prior distribution: location = 0, scale = 1.
#> 50% of the prior density lies between -0.67 and 0.67.
#> 95% of the prior density lies between -1.96 and 1.96.
summary(half_normal(scale = 1))
#> A half-Normal prior distribution: location = 0, scale = 1.
#> 50% of the prior density lies between 0 and 0.67.
#> 95% of the prior density lies between 0 and 1.96.
summary(log_normal(location = -3.93, scale = 1.51))
#> A log-Normal prior distribution: location = -3.93, scale = 1.51.
#> 50% of the prior density lies between 0.01 and 0.05.
#> 95% of the prior density lies between 0 and 0.38.

# Truncation limits may be set, for example to restrict a prior to positive values
summary(normal(location = 0.5, scale = 1), trunc = c(0, Inf))
#> A Normal prior distribution: location = 0.5, scale = 1.
#> 50% of the prior density lies between 0.45 and 1.44.
#> 95% of the prior density lies between 0.05 and 2.61.