Compute effective sample size estimates for quantile estimates of a single variable.
ess_quantile(x, probs = c(0.05, 0.95), ...)# S3 method for default
ess_quantile(x, probs = c(0.05, 0.95), names = TRUE, ...)
# S3 method for rvar
ess_quantile(x, probs = c(0.05, 0.95), names = TRUE, ...)
ess_median(x, ...)
# S3 method for default
ess_mean(x, ...)
If the input is an array,
returns a numeric vector with one element per quantile. If any of the draws is
non-finite, that is, NA, NaN, Inf, or -Inf, the returned output will
be a vector of (numeric) NA values. Also, if all draws of a variable are
the same (constant), the returned output will be a vector of (numeric) NA
values as well. The reason for the latter is that, for constant draws, we cannot distinguish between variables that are supposed to be constant (e.g., a diagonal element of a correlation matrix is always 1) or variables that just happened to be constant because of a failure of convergence or other problems in the sampling process.
If the input is an rvar and length(probs) == 1, returns an array of the
same dimensions as the rvar, where each element is equal to the value
that would be returned by passing the draws array for that element of the
rvar to this function. If length(probs) > 1, the first dimension of the
result indexes the input probabilities; i.e. the result has dimension
c(length(probs), dim(x)).
(multiple options) One of:
A matrix of draws for a single variable (iterations x chains). See
extract_variable_matrix().
An rvar.
(numeric vector) Probabilities in [0, 1].
Arguments passed to individual methods (if applicable).
(logical) Should the result have a names attribute? The
default is TRUE, but use FALSE for improved speed if there are many
values in probs.
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner (2021). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC (with discussion). Bayesian Data Analysis. 16(2), 667-–718. doi:10.1214/20-BA1221
Other diagnostics:
ess_basic(),
ess_bulk(),
ess_sd(),
ess_tail(),
mcse_mean(),
mcse_quantile(),
mcse_sd(),
pareto_diags(),
pareto_khat(),
rhat(),
rhat_basic(),
rhat_nested(),
rstar()
mu <- extract_variable_matrix(example_draws(), "mu")
ess_quantile(mu, probs = c(0.1, 0.9))
d <- as_draws_rvars(example_draws("multi_normal"))
ess_quantile(d$mu, probs = c(0.1, 0.9))
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