# stationary: Stationary state probabilities In momentuHMM: Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

## Description

Calculates the stationary probabilities of each state based on covariate values.

## Usage

 `1` ```stationary(model, covs, covIndex) ```

## Arguments

 `model` `momentuHMM`, `miHMM`, or `miSum` object `covs` Either a data frame or a design matrix of covariates. If `covs` is not provided, then the stationary probabilties are calculated based on the covariate data for each time step. `covIndex` Integer vector indicating specific rows of the data to be used in the calculations. This can be useful for reducing unnecessarily long computation times, e.g., when `formula` includes factor covariates (such as `ID`) but no temporal covariates. Ignored unless `covs` is missing.

## Value

A list of length `model\$conditions\$mixtures` where each element is a matrix of stationary state probabilities for each mixture. For each matrix, each row corresponds to a row of covs, and each column corresponds to a state.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# m is a momentuHMM object (as returned by fitHMM), automatically loaded with the package m <- example\$m # data frame of covariates stationary(m, covs = data.frame(cov1 = 0, cov2 = 0)) # design matrix (each column corresponds to row of m\$mle\$beta) stationary(m, covs = matrix(c(1,0,cos(0)),1,3)) # get stationary distribution for first 3 observations stationary(m, covIndex = c(1,2,3)) ```

momentuHMM documentation built on Sept. 5, 2021, 5:17 p.m.