Summaries and tidy accessors

summary() methods for fits, posteriors, and samples, plus as.data.frame() for posterior draws so results drop straight into data-frame workflows (dplyr, ggplot2, ...).

Usage

# S3 method for class 'nsbi_samples'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

# S3 method for class 'nsbi_samples'
summary(object, probs = c(0.025, 0.25, 0.5, 0.75, 0.975), ...)

# S3 method for class 'nsbi_posterior'
summary(object, n = 1000L, x = NULL, ...)

# S3 method for class 'nsbi_npe'
summary(object, ...)

# S3 method for class 'nsbi_nle'
summary(object, ...)

# S3 method for class 'nsbi_nre'
summary(object, ...)

Arguments

x

Observation to condition on (defaults to the posterior's x_obs); for as.data.frame(), the samples object.

row.names, optional

Standard as.data.frame() arguments.

...

Additional arguments passed to methods.

object

An nsbi_samples matrix from sample(), an nsbi_posterior, or an nsbi_npe, nsbi_nle or nsbi_nre fit.

probs

Quantiles to report.

n

Number of draws used to summarize a posterior.

Value

For samples and posteriors, a data frame with one row per parameter (mean, sd, and quantiles). For fits, an invisible list of training metadata. In that list dim_x counts the data dimension the estimator was trained on, which for an nsbi_nle or nsbi_nre fit is one observation rather than the whole data set.