plotStationary: Plot stationary state probabilities

In bmcclintock/momentuHMM: Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

Plot stationary state probabilities

Description

Plot stationary state probabilities

Usage

plotStationary(
  model,
  covs = NULL,
  col = NULL,
  plotCI = FALSE,
  alpha = 0.95,
  return = FALSE,
  ...
)

Arguments

model	momentuHMM, momentuHierHMM, miHMM, or miSum object
covs	Optional data frame consisting of a single row indicating the covariate values to be used in plots. If none are specified, the means of any covariates appearing in the model are used (unless covariate is a factor, in which case the first factor in the data is used).
col	Vector or colors for the states (one color per state).
plotCI	Logical indicating whether to include confidence intervals in plots (default: FALSE)
alpha	Significance level of the confidence intervals (if plotCI=TRUE). Default: 0.95 (i.e. 95% CIs).
return	Logical indicating whether to return a list containing estimates, SEs, CIs, and covariate values used to create the plots for each mixture and state. Ignored if plotCI=FALSE. Default: FALSE.
...	Additional arguments passed to graphics::plot. These can currently include cex.axis, cex.lab, cex.legend, cex.main, legend.pos, and lwd. See par. legend.pos can be a single keyword from the list “bottomright”, “bottom”, “bottomleft”, “left”, “topleft”, “top”, “topright”, “right”, and “center”.

Examples

# m is a momentuHMM object (as returned by fitHMM), automatically loaded with the package
m <- example$m

plotStationary(m)

bmcclintock/momentuHMM documentation built on Oct. 26, 2022, 1 a.m.