summary.marssMLE

A brief summary of the fit: number of state and observation time series and the estimates. See also <code>glance()</code> and <code>tidy()</code> for other summary like output.

The MARSS package provides maximum-likelihood parameter
estimation for constrained and unconstrained linear multivariate autoregressive
state-space (MARSS) models, including partially deterministic models. MARSS models are a class
of dynamic linear model (DLM) and vector autoregressive model (VAR)
model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB'
(using the 'marssTMB' companion package). Functions are provided for parametric and
innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including
bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all
conditional residual types. See the user guide for examples of dynamic factor analysis,
dynamic linear models, outlier and shock detection, and multivariate AR-p models.
Online workshops (lectures, eBook, and computer labs)
at <https://atsa-es.github.io/>.

Elizabeth Holmes  NOAA Federal

MARSS

Multivariate Autoregressive State-Space Modeling

Elizabeth Eli Holmes

Eric J. Ward

Mark D. Scheuerell

Kellie Wills

summary.marssMLE function

<dl> <dt>object</dt>
<dd>A <code>marssMLE</code> object.</dd> <dt>digits</dt>
<dd>Number of digits for printing.</dd> <dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Author

Summary methods for marssMLE objects — summary.marssMLE

<dl>

 <dt>object</dt>
<dd>A <code>marssMLE</code> object.</dd>

 <dt>digits</dt>
<dd>Number of digits for printing.</dd>

 <dt>...</dt>
<dd>Not used.</dd>

</dl>

summary.marssMLE: Summary methods for marssMLE objects

Description

Usage

Value

Arguments

Author

See Also

Examples