Calculates the Effective Sample Sizes of one estimated variable's trace.
one or more traces, supplies as either, (1) a numeric vector or, (2) a data frame of numeric values.
the interval (the number of state
transitions between samples) of the MCMC run that produced the trace.
Using a different
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:
stderr_mean: standard error of the mean
stdev: standard deviation
geom_mean: geometric mean
lower bound of 95% highest posterior density
upper bound of 95% highest posterior density
act: auto correlation time
ess: effective sample size
This function assumes the burn-in is removed.
remove_burn_in (on a vector) or
remove_burn_ins (on a data frame) to remove
Richèl J.C. Bilderbeek
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).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
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 )
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.