R/stan_glmer.R
stan_glmer.Rd
Bayesian inference for GLMs with group-specific coefficients that have unknown covariance matrices with flexible priors.
stan_glmer( formula, data = NULL, family = gaussian, subset, weights, na.action = getOption("na.action", "na.omit"), offset, contrasts = NULL, ..., prior = default_prior_coef(family), prior_intercept = default_prior_intercept(family), prior_aux = exponential(autoscale = TRUE), prior_covariance = decov(), prior_PD = FALSE, algorithm = c("sampling", "meanfield", "fullrank"), adapt_delta = NULL, QR = FALSE, sparse = FALSE ) stan_lmer( formula, data = NULL, subset, weights, na.action = getOption("na.action", "na.omit"), offset, contrasts = NULL, ..., prior = default_prior_coef(family), prior_intercept = default_prior_intercept(family), prior_aux = exponential(autoscale = TRUE), prior_covariance = decov(), prior_PD = FALSE, algorithm = c("sampling", "meanfield", "fullrank"), adapt_delta = NULL, QR = FALSE ) stan_glmer.nb( formula, data = NULL, subset, weights, na.action = getOption("na.action", "na.omit"), offset, contrasts = NULL, link = "log", ..., prior = default_prior_coef(family), prior_intercept = default_prior_intercept(family), prior_aux = exponential(autoscale = TRUE), prior_covariance = decov(), prior_PD = FALSE, algorithm = c("sampling", "meanfield", "fullrank"), adapt_delta = NULL, QR = FALSE )
formula, data | Same as for |
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family | Same as for |
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subset, weights, offset | Same as |
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na.action, contrasts | Same as |
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... | For |
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prior | The prior distribution for the (non-hierarchical) regression coefficients. The default priors are described in the vignette
Prior
Distributions for rstanarm Models.
If not using the default,
See the priors help page for details on the families and
how to specify the arguments for all of the functions in the table above.
To omit a prior ---i.e., to use a flat (improper) uniform prior---
Note: Unless |
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prior_intercept | The prior distribution for the intercept (after centering all predictors, see note below). The default prior is described in the vignette
Prior
Distributions for rstanarm Models.
If not using the default, Note: If using a dense representation of the design matrix
---i.e., if the |
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prior_aux | The prior distribution for the "auxiliary" parameter (if
applicable). The "auxiliary" parameter refers to a different parameter
depending on the The default prior is described in the vignette
Prior
Distributions for rstanarm Models.
If not using the default, |
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prior_covariance | Cannot be |
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prior_PD | A logical scalar (defaulting to |
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algorithm | A string (possibly abbreviated) indicating the
estimation approach to use. Can be |
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adapt_delta | Only relevant if |
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QR | A logical scalar defaulting to |
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sparse | A logical scalar (defaulting to |
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link | For |
A stanreg object is returned
for stan_glmer, stan_lmer, stan_glmer.nb
.
A list with classes stanreg
, glm
, lm
,
and lmerMod
. The conventions for the parameter names are the
same as in the lme4 package with the addition that the standard
deviation of the errors is called sigma
and the variance-covariance
matrix of the group-specific deviations from the common parameters is
called Sigma
, even if this variance-covariance matrix only has
one row and one column (in which case it is just the group-level variance).
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 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 wrapper for stan_glmer
with family =
neg_binomial_2(link)
.
Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge, UK. (Ch. 11-15)
Muth, C., Oravecz, Z., and Gabry, J. (2018) User-friendly Bayesian regression modeling: A tutorial with rstanarm and shinystan. The Quantitative Methods for Psychology. 14(2), 99--119. https://www.tqmp.org/RegularArticles/vol14-2/p099/p099.pdf
stanreg-methods
and
glmer
.
The vignette for stan_glmer
and the Hierarchical
Partial Pooling vignette. http://mc-stan.org/rstanarm/articles/
# see help(example_model) for details on the model below if (!exists("example_model")) example(example_model) print(example_model, digits = 1)#> stan_glmer #> family: binomial [logit] #> formula: cbind(incidence, size - incidence) ~ size + period + (1 | herd) #> observations: 56 #> ------ #> Median MAD_SD #> (Intercept) -1.5 0.6 #> size 0.0 0.0 #> period2 -1.0 0.3 #> period3 -1.1 0.3 #> period4 -1.5 0.5 #> #> Error terms: #> Groups Name Std.Dev. #> herd (Intercept) 0.77 #> Num. levels: herd 15 #> #> ------ #> * For help interpreting the printed output see ?print.stanreg #> * For info on the priors used see ?prior_summary.stanreg