cor_ma

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 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
(currently only possible for stationary ARMA effects of order 1). 
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 cannot handle user defined standard errors.

This function is a constructor for the <code>cor_arma</code> class, 
allowing for moving average terms only.

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.

cor_ma: MA(q) correlation structure

Description

Usage

Arguments

Value

See Also

Examples