marxss_to_marss

marss_to_marxss

An object of class <code><a href="/link/marssMLE?package=MARSS&version=3.8" rd-options="" data-mini-rdoc="MARSS::marssMLE">marssMLE</a></code>.

If the C and D matrices are all 0, then a marss model can be converted to marxss without further information besides the marss model.

C.and.D.are.zero

If only.par=TRUE then only the par element is changed and marss is used for the marss object.

only.par

These are utility functions for model objects in the package <code><a href="/link/MARSS-package?package=MARSS&version=3.8" rd-options="" data-mini-rdoc="MARSS::MARSS-package">MARSS-package</a></code>.  
Users would not normally work directly with these functions.

marss.conversion

The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data.  Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided.  MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model.  Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary residuals for detecting outliers and shocks.  The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.  Type RShowDoc("UserGuide", package="MARSS") at the R command line to open the MARSS user guide.  Online workshops (lectures and computer labs) at http://faculty.washington.edu/eeholmes/workshops.shtml  See the NEWS file for update information.

marss.conversion: Convert Model Objects between Forms

Description

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See Also