trigBasisExp

Given a periodically varying variable such as time of day or day of year and the associated cycle length, this function performs a basis expansion to efficiently calculate a linear predictor of the form 
\( 
 \eta^{(t)} = \beta_0 + \sum_{k=1}^K \bigl( \beta_{1k} \sin(\frac{2 \pi k t}{L}) + \beta_{2k} \cos(\frac{2 \pi k t}{L}) \bigr). 
 \) 
 This is relevant for modeling e.g. diurnal variation and the flexibility can be increased by adding smaller frequencies (i.e. increasing \(K\)).

The class of latent Markov models, including hidden Markov models,
hidden semi-Markov models, state space models, and point processes, is a very popular and powerful framework for inference of time series driven by latent processes.
Furthermore, all these models can be fitted using direct numerical maximum likelihood estimation using the so-called forward algorithm as discussed in
Zucchini et al. (2016) <doi:10.1201/b20790>.
However, due to their great flexibility, researchers using these models in applied work often need to build highly customized models for which standard software implementation is lacking,
or the construction of such models in said software is as complicated as writing fully tailored 'R' code.
While providing greater flexibility and control, the latter suffers from slow estimation speeds that make custom solutions inconvenient.
We address the above issues in two ways. First, standard blocks of code, common to all these model classes, are implemented as simple-to-use functions that can be added like Lego blocks to an otherwise fully custom likelihood function, making writing custom code much easier.
Second, under the hood, these functions are written in 'C++', allowing for 10-20 times faster evaluation time, and thus drastically speeding up model estimation.
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 Koslik

LaMa

Fast Numerical Maximum Likelihood Estimation for Latent Markov
Models

trigBasisExp function

<dl><dt>tod</dt>
<dd>Time variable, describing the time point in a cycle. Could for example be time of day (between 0 and 24) or day of year.</dd>
<dt>L</dt>
<dd>Length of one cycle on the scale of the time variable. For time of day, this would be 24.</dd>
<dt>degree</dt>
<dd>Degree K of the trigonometric link above. Increasing K increases the flexibility.</dd></dl>

Arguments

Given a periodically varying variable such as time of day or day of year and the associated cycle length, this function performs a basis expansion to efficiently calculate a linear predictor of the form 
\( 
 \eta^{(t)} = \beta_0 + \sum_{k=1}^K \bigl( \beta_{1k} \sin(\frac{2 \pi k t}{L}) + \beta_{2k} \cos(\frac{2 \pi k t}{L}) \bigr). 
 \) 
 This is relevant for modeling e.g. diurnal variation and the flexibility can be increased by adding smaller frequencies (i.e. increasing \(K\)).

Trigonometric Basis Expansion — trigBasisExp

<dl>

<dt>tod</dt>
<dd>Time variable, describing the time point in a cycle. Could for example be time of day (between 0 and 24) or day of year.</dd>


<dt>L</dt>
<dd>Length of one cycle on the scale of the time variable. For time of day, this would be 24.</dd>


<dt>degree</dt>
<dd>Degree K of the trigonometric link above. Increasing K increases the flexibility.</dd>

</dl>

trigBasisExp: Trigonometric Basis Expansion

Description

Usage

Value

Arguments

Examples