plot

visualize fits from an mpm object

# S3 method for class 'mpm_df'
plot(
  x,
  y = NULL,
  se = FALSE,
  pages = 0,
  ncol = 1,
  ask = TRUE,
  pal = "Plasma",
  rev = FALSE,
  ...
)

Arguments

x

a aniMotum mpm fit object with class mpm_df

y

optional ssm fit object with class ssm_df corresponding to x. If absent, 1-d plots of gamma_t time series are rendered otherwise, 2-d track plots with locations coloured by `gamma_t“ are rendered.

se

logical (default = FALSE); should points be scaled by gamma_t uncertainty (ignored if y is not supplied)

pages

plots of all individuals on a single page (pages = 1; default) or each individual on a separate page (pages = 0)

ncol

number of columns to use for faceting. Default is ncol = 1 but this may be increased for multi-individual objects. 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 (default: "Plasma"; see grDevices::hcl.pals for options)

rev

reverse colour palette (logical)

...

additional arguments to be ignored

Value

a ggplot object with either: 1-d time series of gamma_t estimates (if y not provided), with estimation uncertainty ribbons (95 % CI's); or 2-d track plots (if y provided) coloured by gamma_t, with smaller points having greater uncertainty (size is proportional to SE^-2, if se = TRUE). Plots can be rendered all on a single page (pages = 1) or on separate pages.

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))
#> 
#> 

# fit mpm to ssm fits
xm <- fit_mpm(xs, model = "jmpm")
#> fitting jmpm...
#> 
 pars:   0 0 0      
 pars:   -0.63018 -0.77433 0.05731      
 pars:   -0.21925 -0.90513 0.95955      
 pars:   -0.08786 -0.20745 0.25528      
 pars:   -0.18067 -0.7003 0.75278      
 pars:   -0.42894 -0.54449 0.73596      
 pars:   -0.45937 -0.82886 0.66962      
 pars:   -0.39279 -0.70181 0.63844      
 pars:   -0.2662 -0.72981 0.37502      
 pars:   -0.36963 -0.70693 0.59025      
 pars:   -0.38254 -0.70407 0.61711      
 pars:   -0.40271 -0.6999 0.60524      
 pars:   -0.41907 -0.69089 0.56152      
 pars:   -0.41907 -0.69089 0.56152      

# plot 1-D mp timeseries on 1 page
plot(xm, pages = 1)