plot.ssm_df: plot

In ianjonsen/foieGras: Fit Continuous-Time State-Space and Latent Variable Models for Quality Control of Argos Satellite (and Other) Telemetry Data and for Estimating Changes in Animal Movement

plot

Description

visualize fits from an ssm object

Usage

## S3 method for class 'ssm_df'
plot(
  x,
  what = c("fitted", "predicted", "rerouted"),
  type = 1,
  outlier = TRUE,
  alpha = 0.3,
  pages = 0,
  ncol = 1,
  ask = TRUE,
  pal = "default",
  normalise = TRUE,
  group = FALSE,
  ...
)

Arguments

x	a aniMotum ssm fit object with class ssm_df
what	specify which location estimates to display on time-series plots: fitted, predicted, or rerouted
type	of plot to generate: 1-d time series for lon and lat separately (type = 1, default); 2-d track plot (type = 2); 1-d time series of move persistence estimates (type = 3; if fitted model was mp); 2-d track plot with locations coloured by move persistence (type = 4; if fitted model was mp)
outlier	include outlier locations dropped by prefilter (outlier = TRUE, default)
alpha	opacity of standard errors. Lower opacity can ease visualization when multiple ellipses overlap one another
pages	each individual is plotted on a separate page by default (pages = 0), multiple individuals can be combined on a single page; pages = 1
ncol	number of columns to arrange plots when combining individuals on a single page (ignored if pages = 0)
ask	logical; if TRUE (default) user is asked for input before each plot is rendered. set to FALSE to return ggplot objects
pal	grDevices::hcl.colors palette to use (see grDevices::hcl.pals() for options)
normalise	logical; if plotting move persistence estimates from an mp model fit, should estimates be normalised to 0,1 (default = TRUE).
group	logical; should g be normalised among individuals as a group, a 'relative g', or to individuals separately to highlight regions of lowest and highest move persistence along single tracks (default = FALSE).
...	additional arguments to be ignored

Value

a ggplot object with either: (type = 1) 1-d time series of fits to data, separated into x and y components (units = km) with prediction uncertainty ribbons (2 x SE); or (type = 2) 2-d fits to data (units = km)

Examples

## generate a ssm fit object (call is for speed only)
xs <- fit_ssm(sese2, spdf=FALSE, model = "rw", time.step=72, control = ssm_control(verbose = 0))

# plot fitted locations as 1-D timeseries on 1 page
plot(xs, what = "f", pages = 1)

ianjonsen/foieGras documentation built on Jan. 17, 2025, 11:15 p.m.