cor_arma

cor_arma-class

A one sided formula of the form <code>~ t</code>, or <code>~ t | g</code>,
specifying a time covariate <code>t</code> and, optionally, a grouping factor
<code>g</code>. A covariate for this correlation structure must be integer
valued. When a grouping factor is present in <code>formula</code>, the
correlation structure is assumed to apply only to observations within the
same grouping level; observations with different grouping levels are
assumed to be uncorrelated. Defaults to <code>~ 1</code>, which corresponds to
using the order of the observations in the data as a covariate, and no
groups.

formula

A non-negative integer specifying the autoregressive (AR) 
order of the ARMA structure. Default is 0.

A non-negative integer specifying the moving average (MA) 
order of the ARMA structure. Default is 0.

A flag indicating whether ARMA effects should be estimated by
means of residual covariance matrices. This is currently only possible for
stationary ARMA effects of order 1. If the model family does not have
natural residuals, latent residuals are added automatically. If
<code>FALSE</code> (the default) a regression formulation is used that is
considerably faster and allows for ARMA effects of order higher than 1 but
is only available for <code>gaussian</code> models and some of its 
generalizations.

This functions is a constructor for the <code>cor_arma</code> class, representing 
an autoregression-moving average correlation structure of order (p, q).

Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include non-linear and
smooth terms, auto-correlation structures, censored data, meta-analytic
standard errors, and quite a few more. In addition, all parameters of the
response distribution can be predicted in order to perform distributional
regression. Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs.
Model fit can easily be assessed and compared with posterior predictive
checks and leave-one-out cross-validation. References: B<c3><bc>rkner (2017)
<doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

cor_arma: ARMA(p,q) correlation structure

Description

Usage

Arguments

Value

See Also

Examples