Bayesian inference for GLMs with groupspecific 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 = normal(), prior_intercept = normal(),
prior_aux = exponential(), 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 = normal(), prior_intercept = normal(),
prior_aux = exponential(), 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 = normal(), prior_intercept = normal(),
prior_aux = exponential(), prior_covariance = decov(), prior_PD = FALSE,
algorithm = c("sampling", "meanfield", "fullrank"), adapt_delta = NULL,
QR = FALSE)
Arguments
formula, data 
Same as for glmer . We
strongly advise against omitting the data argument. Unless
data is specified (and is a data frame) many postestimation
functions (including update , loo , kfold ) are not
guaranteed to work properly. 
family 
Same as for glmer except it is also
possible to use family=mgcv::betar to estimate a Beta regression
with stan_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 (if algorithm is
"meanfield" or "fullrank" ). For stan_lmer and
stan_glmer.nb , ... should also contain all relevant arguments
to pass to stan_glmer (except family ). 
prior 
The prior distribution for the regression coefficients.
prior should be a call to one of the various functions provided by
rstanarm for specifying priors. The subset of these functions that
can be used for the prior on the coefficients can be grouped into several
"families":
Family  Functions 
Student t family  normal , student_t , cauchy 
Hierarchical shrinkage family  hs , hs_plus 
Laplace family  laplace , lasso 
Product normal family  product_normal 
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
prior can be set to NULL , although this is rarely a good
idea.
Note: Unless QR=TRUE , if prior is from the Student t
family or Laplace family, and if the autoscale argument to the
function used to specify the prior (e.g. normal ) is left at
its default and recommended value of TRUE , then the default or
userspecified prior scale(s) may be adjusted internally based on the
scales of the predictors. See the priors help page and the
Prior Distributions vignette for details on the rescaling and the
prior_summary function for a summary of the priors used for a
particular model. 
prior_intercept 
The prior distribution for the intercept.
prior_intercept can be a call to normal , student_t or
cauchy . See the priors help page for details on
these functions. To omit a prior on the intercept i.e., to use a flat
(improper) uniform prior prior_intercept can be set to
NULL .
Note: If using a dense representation of the design matrix
i.e., if the sparse argument is left at its default value of
FALSE  then the prior distribution for the intercept is set so it
applies to the value when all predictors are centered. If you prefer
to specify a prior on the intercept without the predictors being
autocentered, then you have to omit the intercept from the
formula and include a column of ones as a predictor,
in which case some element of prior specifies the prior on it,
rather than prior_intercept . Regardless of how
prior_intercept is specified, the reported estimates of the
intercept always correspond to a parameterization without centered
predictors (i.e., same as in glm ). 
prior_aux 
The prior distribution for the "auxiliary" parameter (if
applicable). The "auxiliary" parameter refers to a different parameter
depending on the family . For Gaussian models prior_aux
controls "sigma" , the error
standard deviation. For negative binomial models prior_aux controls
"reciprocal_dispersion" , which is similar to the
"size" parameter of rnbinom :
smaller values of "reciprocal_dispersion" correspond to
greater dispersion. For gamma models prior_aux sets the prior on
to the "shape" parameter (see e.g.,
rgamma ), and for inverseGaussian models it is the
socalled "lambda" parameter (which is essentially the reciprocal of
a scale parameter). Binomial and Poisson models do not have auxiliary
parameters.
prior_aux can be a call to exponential to
use an exponential distribution, or normal , student_t or
cauchy , which results in a halfnormal, halft, or halfCauchy
prior. See priors for details on these functions. To omit a
prior i.e., to use a flat (improper) uniform prior set
prior_aux to NULL .

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 
A 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 normal distributions, or
"fullrank" for variational inference with a multivariate normal
distribution. See rstanarmpackage for more details on the
estimation algorithms. NOTE: not all fitting functions support all four
algorithms. 
adapt_delta 
Only relevant if algorithm="sampling" . See
the adapt_delta help page for details. 
QR 
A logical scalar defaulting to FALSE , but if TRUE
applies a scaled qr decomposition to the design matrix. The
transformation does not change the likelihood of the data but is
recommended for computational reasons when there are multiple predictors.
See the QRargument documentation page for details on how
rstanarm does the transformation and important information about how
to interpret the prior distributions of the model parameters when using
QR=TRUE . 
sparse 
A logical scalar (defaulting to FALSE ) indicating
whether to use a sparse representation of the design (X) matrix.
If TRUE , the the design matrix is not centered (since that would
destroy the sparsity) and likewise it is not possible to specify both
QR = TRUE and sparse = TRUE . Depending on how many zeros
there are in the design matrix, setting sparse = TRUE may make
the code run faster and can consume much less RAM. 
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 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
groupspecific 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)
.
References
Gelman, A. and Hill, J. (2007). Data Analysis Using
Regression and Multilevel/Hierarchical Models. Cambridge University Press,
Cambridge, UK. (Ch. 1115)
Muth, C., Oravecz, Z., and Gabry, J. (2018)
Userfriendly Bayesian regression modeling: A tutorial with rstanarm and shinystan.
The Quantitative Methods for Psychology. 14(2), 99119.
https://www.tqmp.org/RegularArticles/vol142/p099/p099.pdf
See also
Examples
# see help(example_model) for details on the model below
if (!exists("example_model")) example(example_model)
#> Warning: no help found for ‘example_model’
print(example_model, digits = 1)
#> Error in print(example_model, digits = 1): object 'example_model' not found