stan_gamm4(formula, random = NULL, family = gaussian(), data = list(), weights = NULL, subset = NULL, na.action, knots = NULL, drop.unused.levels = TRUE, ..., 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)gamm4.glm,
but rarely specified.prior can be a call to normal, student_t,
cauchy, hs or hs_plus. See priors for
details. To to omit a prior ---i.e., to use a flat (improper) uniform
prior--- set prior to NULL.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_gamm4.
stan_gamm4 function is similar in syntax to
gamm4, which accepts a syntax that is similar to (but
not quite as extensive as) that for gamm and converts
it internally into the syntax accepted by glmer. But
rather than performing (restricted) maximum likelihood estimation, the
stan_gamm4 function utilizes MCMC to perform Bayesian estimation.
The Bayesian model adds independent priors on the common 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, including the smooths. Estimating these models via MCMC avoids
the optimization issues that often crop up with GAMMs and provides better
estimates for the uncertainty in the parameter estimates.
See gamm4 for more information about the model
specicification and priors for more information about the
priors.
stanreg-methods and
gamm4.
# see example(gamm4, package = "gamm4") but prefix gamm4() calls with stan_
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