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, 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_ops = prior_options(), 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_ops = prior_options(), prior_covariance = decov(), prior_PD = FALSE, algorithm = c("sampling", "meanfield", "fullrank"), adapt_delta = NULL, QR = FALSE)glmer.glm.glm, but rarely
specified.prior can be a call to normal, student_t,
cauchy, hs or hs_plus. See priors for
details. 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 the scaled argument
to prior_options is left at its default and recommended value
of TRUE, then the scale(s) of prior may be modified
internally based on the scales of the predictors, as in the arm
package. See priors for details on the rescaling and
prior_summary for a summary of the priors used for a
particular model.)prior_intercept can be a call to normal, student_t or
cauchy. See priors for details. To to omit a prior
---i.e., to use a flat (improper) uniform prior--- set
prior_intercept to NULL. (Note: if a dense
representation of the design matrix is utilized ---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.)NULL to omit a prior on the dispersion and see
prior_options otherwise.NULL; see decov for
more information about the default arguments.FALSE) indicating
whether to draw from the prior predictive distribution instead of
conditioning on the outcome."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 rstanarm-package for more details on the
estimation algorithms. NOTE: not all fitting functions support all four
algorithms.algorithm="sampling". See
adapt_delta for details.FALSE) but if TRUE
applies a scaled qr decomposition to the design matrix,
$X = Q* R*$, where
$Q* = Q (n-1)^0.5$ and
$R* = (n-1)^(-0.5) R$. The coefficients
relative to $Q*$ are obtained and then premultiplied by the
inverse of $R*$ to obtain coefficients relative to the
original predictors, $X$. These transformations do not change the
likelihood of the data but are recommended for computational reasons when
there are multiple predictors. However, because the coefficients relative
to $Q*$ are not very interpretable it is hard to specify an
informative prior. Setting QR=TRUE is therefore only recommended
if you do not have an informative prior for the regression coefficients.FALSE) indicating
whether to use a sparse representation of the design (X) matrix.
Setting this to TRUE will likely be twice as slow, even if the
design matrix has a considerable number of zeros, but it may allow the
model to be estimated when the computer has too little RAM to
utilize a dense design matrix. If TRUE, the the design matrix
is not centered (since that would destroy the sparsity) and it is
not possible to specify both QR = TRUE and sparse = TRUE.stan_glmer.nb only, the link function to use. See
neg_binomial_2.stan_glmer, stan_lmer, stan_glmer.nb.
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).
stanreg-methods and
glmer.The vignette for stan_glmer and the Hierarchical
Partial Pooling vignette.
# see help(example_model) for details on the model below
if (!exists("example_model")) example(example_model)
print(example_model, digits = 1)
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