miSum

Constructor of <code>miSum</code> objects

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

miSum function

<dl><dt>m</dt>
<dd>A list of attributes required for multiple imputation summaries: <code>data</code> (averaged across imputations), <code>Par</code> (the pooled estimates of
the parameters of the model), <code>conditions</code> (conditions used to fit the model), and <code>MIcombine</code> (as returned by <code>MIcombine</code> for
the working parameters).</dd></dl>

Arguments

Constructor of miSum objects — miSum

<dl>

<dt>m</dt>
<dd>A list of attributes required for multiple imputation summaries: <code>data</code> (averaged across imputations), <code>Par</code> (the pooled estimates of
the parameters of the model), <code>conditions</code> (conditions used to fit the model), and <code>MIcombine</code> (as returned by <code>MIcombine</code> for
the working parameters).</dd>

</dl>

miSum: Constructor of `miSum` objects

Description

Usage

Value

Arguments