plot

visualize fits from an ssm object

# 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)