R: Summarises the output created with 'simmr

summary.simmr_output {simmr}

R Documentation

Summarises the output created with `simmr_mcmc` or `simmr_ffvb`

Description

Produces textual summaries and convergence diagnostics for an object created with simmr_mcmc or simmr_ffvb. The different options are: 'diagnostics' which produces Brooks-Gelman-Rubin diagnostics to assess MCMC convergence, 'quantiles' which produces credible intervals for the parameters, 'statistics' which produces means and standard deviations, and 'correlations' which produces correlations between the parameters.

Usage

## S3 method for class 'simmr_output'
summary(
  object,
  type = c("diagnostics", "quantiles", "statistics", "correlations"),
  group = 1,
  ...
)

Arguments

`object`	An object of class `simmr_output` produced by the function `simmr_mcmc` or `simmr_ffvb`
`type`	The type of output required. At least none of 'diagnostics', 'quantiles', 'statistics', or 'correlations'.
`group`	Which group or groups the output is required for.
`...`	Not used

Details

The quantile output allows easy calculation of 95 per cent credible intervals of the posterior dietary proportions. The correlations, along with the matrix plot in plot.simmr_output allow the user to judge which sources are non-identifiable. The Gelman diagnostic values should be close to 1 to ensure satisfactory convergence.

When multiple groups are included, the output automatically includes the results for all groups.

Value

A list containing the following components:

`gelman`	The convergence diagnostics
`quantiles`	The quantiles of each parameter from the posterior distribution
`statistics`	The means and standard deviations of each parameter
`correlations`	The posterior correlations between the parameters

Note that this object is reported silently so will be discarded unless the function is called with an object as in the example below.

Author(s)

Andrew Parnell <andrew.parnell@mu.ie>, Emma Govan

Examples


# A simple example with 10 observations, 2 tracers and 4 sources

# The data
data(geese_data_day1)
simmr_1 <- with(
  geese_data_day1,
  simmr_load(
    mixtures = mixtures,
    source_names = source_names,
    source_means = source_means,
    source_sds = source_sds,
    correction_means = correction_means,
    correction_sds = correction_sds,
    concentration_means = concentration_means
  )
)

# Plot
plot(simmr_1)


# MCMC run
simmr_1_out <- simmr_mcmc(simmr_1)

# Summarise
summary(simmr_1_out) # This outputs all the summaries
summary(simmr_1_out, type = "diagnostics") # Just the diagnostics
# Store the output in an
ans <- summary(simmr_1_out,
  type = c("quantiles", "statistics")
)

[Package simmr version 0.5.1.216 Index]

Summarises the output created with simmr_mcmc or simmr_ffvb