print.KFS

Print Ouput of Kalman Filter and Smoother

State space modelling is an efficient and flexible framework for
statistical inference of a broad class of time series and other data. KFAS
includes computationally efficient functions for Kalman filtering, smoothing,
forecasting, and simulation of multivariate exponential family state space models,
with observations from Gaussian, Poisson, binomial, negative binomial, and gamma
distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.

Jouni Helske

KFAS

Kalman Filter and Smoother for Exponential Family State Space
Models

print.KFS function

<dl><dt>x</dt>
<dd>output object from function KFS.</dd>
<dt>type</dt>
<dd>What to print. Possible values are <code>"state"</code> (default),
<code>"signal"</code>, and <code>"mean"</code>. Multiple choices are allowed.</dd>
<dt>digits</dt>
<dd>minimum number of digits to be printed.</dd>
<dt>...</dt>
<dd>Ignored.</dd></dl>

Arguments

Print Ouput of Kalman Filter and Smoother — print.KFS

<dl>

<dt>x</dt>
<dd>output object from function KFS.</dd>


<dt>type</dt>
<dd>What to print. Possible values are <code>"state"</code> (default),
<code>"signal"</code>, and <code>"mean"</code>. Multiple choices are allowed.</dd>


<dt>digits</dt>
<dd>minimum number of digits to be printed.</dd>


<dt>...</dt>
<dd>Ignored.</dd>

</dl>

print.KFS: Print Ouput of Kalman Filter and Smoother

Description

Usage

Arguments