This function returns the Monte Carlo Standard Error (MCSE).
mcse(model, ...)
# S3 method for class 'stanreg'
mcse(model, effects = "fixed", component = "location", parameters = NULL, ...)A stanreg, stanfit, brmsfit, blavaan, or MCMCglmm object.
Currently not used.
Should variables for fixed effects ("fixed"), random effects
("random") or both ("all") be returned? Only applies to mixed models. May
be abbreviated.
For models of from packages brms or rstanarm there are additional options:
"fixed" returns fixed effects.
"random_variance" return random effects parameters (variance and
correlation components, e.g. those parameters that start with sd_ or
cor_).
"grouplevel" returns random effects group level estimates, i.e. those
parameters that start with r_.
"random" returns both "random_variance" and "grouplevel".
"all" returns fixed effects and random effects variances.
"full" returns all parameters.
Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, etc. See details in section Model Components. May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):
component = "all" returns all possible parameters.
If component = "location", location parameters such as conditional,
zero_inflated, smooth_terms, or instruments are returned (everything
that are fixed or random effects - depending on the effects argument -
but no auxiliary parameters).
For component = "distributional" (or "auxiliary"), components like
sigma, dispersion, beta or precision (and other auxiliary
parameters) are returned.
Regular expression pattern that describes the parameters
that should be returned. Meta-parameters (like lp__ or prior_) are
filtered by default, so only parameters that typically appear in the
summary() are returned. Use parameters to select specific parameters
for the output.
Monte Carlo Standard Error (MCSE) is another measure of
accuracy of the chains. It is defined as standard deviation of the chains
divided by their effective sample size (the formula for mcse() is
from Kruschke 2015, p. 187). The MCSE “provides a quantitative
suggestion of how big the estimation noise is”.
Possible values for the component argument depend on the model class.
Following are valid options:
"all": returns all model components, applies to all models, but will only
have an effect for models with more than just the conditional model
component.
"conditional": only returns the conditional component, i.e. "fixed
effects" terms from the model. Will only have an effect for models with
more than just the conditional model component.
"smooth_terms": returns smooth terms, only applies to GAMs (or similar
models that may contain smooth terms).
"zero_inflated" (or "zi"): returns the zero-inflation component.
"location": returns location parameters such as conditional,
zero_inflated, or smooth_terms (everything that are fixed or random
effects - depending on the effects argument - but no auxiliary
parameters).
"distributional" (or "auxiliary"): components like sigma,
dispersion, beta or precision (and other auxiliary parameters) are
returned.
For models of class brmsfit (package brms), even more options are
possible for the component argument, which are not all documented in detail
here. See also ?insight::find_parameters.
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
# \donttest{
library(bayestestR)
model <- suppressWarnings(
rstanarm::stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
)
mcse(model)
#> Parameter MCSE
#> 1 (Intercept) 0.13752712
#> 2 wt 0.03455307
#> 3 am 0.07082405
# }