mcse {bayestestR} | R Documentation |
Monte-Carlo Standard Error (MCSE)
Description
This function returns the Monte Carlo Standard Error (MCSE).
Usage
mcse(model, ...)
## S3 method for class 'stanreg'
mcse(model, effects = "fixed", component = "location", parameters = NULL, ...)
Arguments
model |
A |
... |
Currently not used. |
effects |
Should variables for fixed effects ( For models of from packages brms or rstanarm there are additional options:
|
component |
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):
|
parameters |
Regular expression pattern that describes the parameters
that should be returned. Meta-parameters (like |
Details
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”.
Model components
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 asconditional
,zero_inflated
, orsmooth_terms
(everything that are fixed or random effects - depending on theeffects
argument - but no auxiliary parameters). -
"distributional"
(or"auxiliary"
): components likesigma
,dispersion
,beta
orprecision
(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
.
References
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
Examples
library(bayestestR)
model <- suppressWarnings(
rstanarm::stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0)
)
mcse(model)