plot.momentuHMM {momentuHMM} | R Documentation |
Plot momentuHMM
Description
Plot the fitted step and angle densities over histograms of the data, transition probabilities as functions of the covariates, and maps of the animals' tracks colored by the decoded states.
Usage
## S3 method for class 'momentuHMM'
plot(
x,
animals = NULL,
covs = NULL,
ask = TRUE,
breaks = "Sturges",
hist.ylim = NULL,
sepAnimals = FALSE,
sepStates = FALSE,
col = NULL,
cumul = TRUE,
plotTracks = TRUE,
plotCI = FALSE,
alpha = 0.95,
plotStationary = FALSE,
...
)
Arguments
x |
Object |
animals |
Vector of indices or IDs of animals for which information will be plotted.
Default: |
covs |
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). |
ask |
If |
breaks |
Histogram parameter. See |
hist.ylim |
An optional named list of vectors specifying |
sepAnimals |
If |
sepStates |
If |
col |
Vector or colors for the states (one color per state). |
cumul |
If TRUE, the sum of weighted densities is plotted (default). |
plotTracks |
If TRUE, the Viterbi-decoded tracks are plotted (default). |
plotCI |
Logical indicating whether to include confidence intervals in natural parameter plots (default: FALSE) |
alpha |
Significance level of the confidence intervals (if |
plotStationary |
Logical indicating whether to plot the stationary state probabilities as a function of any covariates (default: FALSE). Ignored unless covariate are included in |
... |
Additional arguments passed to |
Details
The state-dependent densities are weighted by the frequency of each state in the most
probable state sequence (decoded with the function viterbi
). For example, if the
most probable state sequence indicates that one third of observations correspond to the first
state, and two thirds to the second state, the plots of the densities in the first state are
weighted by a factor 1/3, and in the second state by a factor 2/3.
Confidence intervals for natural parameters are calculated from the working parameter point and covariance estimates
using finite-difference approximations of the first derivative for the transformation (see grad
).
For example, if dN
is the numerical approximation of the first derivative of the transformation N = exp(x_1 * B_1 + x_2 * B_2)
for covariates (x_1, x_2) and working parameters (B_1, B_2), then
var(N)=dN %*% Sigma %*% dN
, where Sigma=cov(B_1,B_2)
, and normal confidence intervals can be
constructed as N +/- qnorm(1-(1-alpha)/2) * se(N)
.
Examples
# m is a momentuHMM object (as returned by fitHMM), automatically loaded with the package
m <- example$m
plot(m,ask=TRUE,animals=1,breaks=20,plotCI=TRUE)