calc_summary_stats {tracerer}

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

Description

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

Usage

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:

• mean: mean

• stderr_mean: standard error of the mean

• stdev: standard deviation

• variance: variance

• mode: mode

• geom_mean: geometric mean

• hpd_interval_low: lower bound of 95% highest posterior density

• hpd_interval_high: upper bound of 95% highest posterior density

• act: auto correlation time

• ess: effective sample size

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

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

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

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