rstanarm (version 2.9.0-3)

stan_glmer: Bayesian generalized linear models with group-specific terms via Stan

Description

Bayesian inference for GLMs with group-specific coefficients that have unknown covariance matrices with flexible priors.

Usage

stan_glmer(formula, data = NULL, family = gaussian, subset, weights,
  na.action = getOption("na.action", "na.omit"), offset, contrasts = NULL,
  ..., prior = normal(), prior_intercept = normal(),
  prior_ops = prior_options(), prior_covariance = decov(),
  prior_PD = FALSE, algorithm = c("sampling", "meanfield", "fullrank"),
  adapt_delta = NULL, QR = FALSE)
stan_lmer(...)
stan_glmer.nb(..., link = "log")

Arguments

formula, data, family

Same as for glmer.

subset, weights, offset

Same as glm.

na.action, contrasts

Same as glm, but rarely specified.

...

For stan_glmer, further arguments passed to sampling (e.g. iter, chains, cores, etc.) or to vb (i

prior

Prior for coefficients. See priors for details. Set prior to NULL to omit a prior, i.e., use an (improper) uniform prior.

prior_intercept

Prior for intercept. See priors for details. Set prior_intercept to NULL to omit a prior, i.e., use an (improper) uniform prior. (Note: the prior distribution for

prior_ops

Additional options related to prior distributions. Can be NULL to omit a prior on the dispersion and see prior_options otherwise.

prior_covariance

Cannot be NULL; see decov for more information about the default arguments.

prior_PD

A logical scalar (defaulting to FALSE) indicating whether to draw from the prior predictive distribution instead of conditioning on the outcome.

algorithm

Character string (possibly abbreviated) indicating the estimation approach to use. Can be "sampling" for MCMC (the default), "optimizing" for optimization, "meanfield" for variational inference with independent norm

adapt_delta

Only relevant if algorithm="sampling". See adapt_delta for details.

QR

A logical scalar (defaulting to FALSE) but if TRUE applies a scaled qr decomposition to the design matrix, $X = Q^\ast R^\ast$, where $Q^\ast = Q \sqrt{n-1}$ and $R^\ast = \frac{1}{\s

link

For stan_glmer.nb only, the link function to use. See neg_binomial_2.

Value

• A stanreg object is returned for stan_glmer, stan_lmer, stan_glmer.nb.

Details

The stan_glmer function is similar in syntax to glmer but rather than performing (restricted) maximum likelihood estimation of generalized linear models, Bayesian estimation is performed via MCMC. The Bayesian model adds independent priors on the regression coefficients (in the same way as stan_glm) and priors on the terms of a decomposition of the covariance matrices of the group-specific parameters. See priors for more information about the priors. The stan_lmer function is equivalent to stan_glmer with family = gaussian(link = "identity"). The stan_glmer.nb function, which takes the extra argument link, is a simple wrapper for stan_glmer with family = neg_binomial_2(link).

References

Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge, UK. (Ch. 11-15)

See Also

stanreg-methods and glmer.

The vignette for stan_glmer and the Hierarchical Partial Pooling vignette.

Examples

# see help(example_model) for details on the model below
print(example_model, digits = 1)