getDM_rcpp

Loop for creating full design matrix (X) from pseudo-design matrix (DM). Written in C++. Used in <code>getDM</code>.

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Brett T McClintock

momentuHMM

Maximum Likelihood Analysis of Animal Movement Behavior Using
Multivariate Hidden Markov Models

getDM_rcpp function

<dl><dt>X</dt>
<dd>full design matrix</dd>
<dt>covs</dt>
<dd>matrix of covariates</dd>
<dt>DM</dt>
<dd>pseudo design matrix</dd>
<dt>nr</dt>
<dd>number of rows in design matrix</dd>
<dt>nc</dt>
<dd>number of column in design matrix</dd>
<dt>cov</dt>
<dd>covariate names</dd>
<dt>nbObs</dt>
<dd>number of observations</dd></dl>

Arguments

Get design matrix — getDM_rcpp

<dl>

<dt>X</dt>
<dd>full design matrix</dd>


<dt>covs</dt>
<dd>matrix of covariates</dd>


<dt>DM</dt>
<dd>pseudo design matrix</dd>


<dt>nr</dt>
<dd>number of rows in design matrix</dd>


<dt>nc</dt>
<dd>number of column in design matrix</dd>


<dt>cov</dt>
<dd>covariate names</dd>


<dt>nbObs</dt>
<dd>number of observations</dd>

</dl>

getDM_rcpp: Get design matrix

Description

Usage

Value

Arguments