calc_summary_stats {tracerer} | R Documentation |
Calculates the Effective Sample Sizes of one estimated variable's trace.
calc_summary_stats(traces, sample_interval)
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 |
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
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.
Richèl J.C. Bilderbeek
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).
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))