mcse_quantile {posterior} | R Documentation |
Monte Carlo standard error for quantiles
Description
Compute Monte Carlo standard errors for quantile estimates of a single variable.
Usage
mcse_quantile(x, probs = c(0.05, 0.95), ...)
## Default S3 method:
mcse_quantile(x, probs = c(0.05, 0.95), names = TRUE, ...)
## S3 method for class 'rvar'
mcse_quantile(x, probs = c(0.05, 0.95), names = TRUE, ...)
mcse_median(x, ...)
Arguments
x |
(multiple options) One of:
|
probs |
(numeric vector) Probabilities in |
... |
Arguments passed to individual methods (if applicable). |
names |
(logical) Should the result have a |
Value
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))
.
References
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
See Also
Other diagnostics:
ess_basic()
,
ess_bulk()
,
ess_quantile()
,
ess_sd()
,
ess_tail()
,
mcse_mean()
,
mcse_sd()
,
pareto_diags()
,
pareto_khat()
,
rhat()
,
rhat_basic()
,
rhat_nested()
,
rstar()
Examples
mu <- extract_variable_matrix(example_draws(), "mu")
mcse_quantile(mu, probs = c(0.1, 0.9))
d <- as_draws_rvars(example_draws("multi_normal"))
mcse_quantile(d$mu)