summary.qremlModel

print.summary.qremlModel

Summarises a model from <code>qreml</code>: the summary of <code>summary.LaMaModel</code>,
plus the effective degrees of freedom and smoothing parameters of the smooth terms and diagnostics of the smoothness selection.

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models,
state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm.
Applied researchers often need custom models that standard software does not easily support.
Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds.
We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm.
These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers.
To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Jan-Ole Fischer

LaMa

Fast Numerical Maximum Likelihood Estimation for Latent Markov
Models

summary.qremlModel function

<dl><dt>object</dt>
<dd>model object of class <code>"qremlModel"</code></dd>
<dt>...</dt>
<dd>additional arguments (not used)</dd>
<dt>x</dt>
<dd>object of class <code>"summary.qremlModel"</code></dd>
<dt>digits</dt>
<dd>number of significant digits to print</dd></dl>

Arguments

Summary of a qremlModel object — summary.qremlModel

<dl>

<dt>object</dt>
<dd>model object of class <code>"qremlModel"</code></dd>


<dt>...</dt>
<dd>additional arguments (not used)</dd>


<dt>x</dt>
<dd>object of class <code>"summary.qremlModel"</code></dd>


<dt>digits</dt>
<dd>number of significant digits to print</dd>

</dl>

summary.qremlModel: Summary of a `qremlModel` object

Description

Usage

Value

Arguments