calc_summary_stats: Calculates the Effective Sample Sizes of one estimated...

Description Usage Arguments Value Note Author(s) See Also Examples

Description

Calculates the Effective Sample Sizes of one estimated variable's trace.

Usage

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calc_summary_stats(traces, sample_interval)

Arguments

traces

one or more traces, supplies as either, (1) a numeric vector or, (2) a data frame of numeric values.

sample_interval

the interval (the number of state transitions between samples) of the MCMC run that produced the trace. Using a different sample_interval than the actually used sampling interval will result in bogus return values.

Value

the summary statistics of the traces. If one numeric vector is supplied, a list is returned with the elements listed below. If the traces are supplied as a data frame, a data frame is returned with the elements listed below as column names.
The elements are:

Note

This function assumes the burn-in is removed. Use remove_burn_in (on a vector) or remove_burn_ins (on a data frame) to remove the burn-in.

Author(s)

Richèl J.C. Bilderbeek

See Also

Use calc_summary_stats_trace to calculate the summary statistics of one trace (stored as a numeric vector). Use calc_summary_stats_traces to calculate the summary statistics of more traces (stored as a data frame).

Examples

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  estimates_all <- parse_beast_log(get_tracerer_path("beast2_example_output.log"))
  estimates <- remove_burn_ins(estimates_all, burn_in_fraction = 0.1)

  # From a single variable's trace
  sum_stats_posterior <- calc_summary_stats(
    estimates$posterior,
    sample_interval = 1000
  )

  testit::assert("mean" %in% names(sum_stats_posterior))

  # From all variables' traces
  sum_stats <- calc_summary_stats(
    estimates,
    sample_interval = 1000
  )

  testit::assert("mean" %in% colnames(sum_stats))

ropensci/tracerer documentation built on May 14, 2019, 8:55 p.m.