mcse {bayestestR} | R Documentation |
This function returns the Monte Carlo Standard Error (MCSE).
mcse(model, ...) ## S3 method for class 'stanreg' mcse( model, effects = c("fixed", "random", "all"), component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), parameters = NULL, ... )
model |
A |
... |
Currently not used. |
effects |
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. |
component |
Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models. |
parameters |
Regular expression pattern that describes the parameters
that should be returned. Meta-parameters (like |
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”.
Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
## Not run: library(bayestestR) library(rstanarm) model <- stan_glm(mpg ~ wt + am, data = mtcars, chains = 1, refresh = 0) mcse(model) ## End(Not run)