format data by mapping supplied variable names to those expected by
fit_ssm(), and ensuring variables are put into the expected order. Can be
run manually by user as a data pre-processing step prior to calling fit_ssm()
or can be called automatically by fit_ssm(). In the latter case, any custom
variable names must be declared as arguments to fit_ssm(); see examples, below.
input data
the name (as a quoted character string) of id variable: a unique identifier for individual (animal) track data sets.
the name (as a quoted character string)of the date/time variable: date and time (as YYYY-MM-DD HH:MM:SS) of each observation.
the name (as a quoted character string) of the location quality class variable: Argos location quality class (values in the set: 3,2,1,0,"A","B","Z"). Can also include "G" for GPS data and/or "GL" for light-level geolocation (GLS) and other data types.
the names (as quoted character strings) of the location coordinate
variables: defaults are c("lon","lat"), but could also be c("x","y") for planar
coordinates; or if input data is an sf object then "geometry". If input
data is an sf object then coord is set to "geometry" by default.
the names (as quoted character strings) of the Argos error ellipse parameters: defaults are "smaj" (ellipse semi-major axis), "smin" (ellipse semi-minor axis), and "eor" (ellipse orientation). Ignored if these variables are missing from the input data.
the names (as quoted character strings) of provided standard
errors for lon,lat or x,y: default names are x.sd, y.sd. Typically,
these are only provided for generic location data such as processed light-level
geolocations, or high-resolution acoustic detections. The argument is ignored
if these variables are missing from the input data.
the timezone the applies to the data/time variable if they are not
in tz = 'UTC'. A list of valid timezone names can be viewed via OlsonNames()
a data.frame or sf-tibble of input data in expected aniMotum format.
Additional columns required by fit_ssm(), if missing, will be added to the
formatted tibble: smaj, smin, eor, x.sd, and y.sd.
## as a data pre-processing step
data(sese2_n)
head(sese2_n, 5)
#> # A tibble: 5 × 5
#> longitude latitude time lc id
#> <dbl> <dbl> <chr> <fct> <chr>
#> 1 72.5 -50.2 2009-02-01 17:50:46 A ct36-E-09
#> 2 73.0 -50.4 2009-02-02 03:30:26 A ct36-E-09
#> 3 73.8 -50.8 2009-02-02 17:50:48 B ct36-E-09
#> 4 74.6 -51.2 2009-02-03 07:39:08 A ct36-E-09
#> 5 74.9 -51.4 2009-02-03 15:29:59 A ct36-E-09
d <- format_data(sese2_n, date = "time", coord = c("longitude","latitude"),
tz = "America/Halifax")
fit <- fit_ssm(d, model = "crw", time.step = 24)
#> fitting crw SSM to 2 tracks...
#>
pars: 1 1 0 0 -1.83815 0
pars: 0.58822 0.47049 -0.39448 -0.58757 -2.05502 0.04656
pars: 0.24142 -0.08857 0.17073 -0.22123 -2.16295 0.36573
pars: -0.25485 -0.61181 -0.39281 -0.55381 -2.30537 0.5431
pars: -0.95996 -1.05417 0.05433 -0.3315 -2.16562 0.73866
pars: -1.52441 -1.28179 -0.24338 -0.09273 -1.48444 0.87986
pars: -1.15677 -1.14532 -0.15766 -0.32371 -1.9606 0.78088
pars: -1.2591 -1.23906 -0.00914 -0.54176 -1.74795 0.73881
pars: -1.28632 -1.23923 -0.06489 -0.37148 -1.72418 0.71526
pars: -1.37476 -1.3354 -0.03139 -0.43287 -1.62519 0.66829
pars: -1.43518 -1.36913 -0.07069 -0.36322 -1.49214 0.59704
pars: -1.50537 -1.39061 -0.03715 -0.40209 -1.33302 0.57215
pars: -1.55804 -1.48577 -0.11287 -0.42946 -1.21264 0.53812
pars: -1.53464 -1.42687 -0.0852 -0.38865 -1.29386 0.558
pars: -1.54606 -1.47357 -0.04788 -0.37299 -1.26878 0.5154
pars: -1.54606 -1.47357 -0.04788 -0.37299 -1.26878 0.5154
#>
pars: 1 1 0 0 -1.35869 0
pars: 0.26969 0.48413 0.13062 0.37138 -1.50855 0.15196
pars: -1.92124 -1.06346 0.52247 1.48551 -1.95813 0.60783
pars: -3.94772 -2.40713 -0.47652 -1.45121 -2.46454 1.06517
pars: -2.05317 -1.14577 0.37728 1.06076 -1.99643 0.6397
pars: -2.24177 -1.28178 0.31364 0.65504 -2.04218 0.69747
pars: -2.61685 -1.51707 0.42349 0.63143 -2.07707 0.83261
pars: -3.26866 -1.92596 0.61438 0.59039 -2.13769 1.06746
pars: -4.1194 -2.03377 -0.24849 1.01778 -1.97888 1.21674
pars: -3.31866 -1.88614 0.45421 0.68271 -2.10153 1.06158
pars: -3.44366 -1.81523 0.38836 0.67524 -1.98086 1.06261
pars: -3.53173 -1.85375 0.53088 0.65662 -1.88523 1.03885
pars: -3.63928 -1.97209 0.44911 0.66534 -1.80171 1.02159
pars: -3.67057 -1.9039 0.47921 0.72471 -1.6359 0.97625
pars: -3.68464 -1.93999 0.44261 0.64383 -1.6141 0.983
pars: -3.68464 -1.93999 0.44261 0.64383 -1.6141 0.983
## called automatically within fit_ssm()
fit <- fit_ssm(sese2_n, date = "time", coord = c("longitude", "latitude"),
tz = "America/Halifax", model = "crw", time.step = 24)
#> fitting crw SSM to 2 tracks...
#>
pars: 1 1 0 0 -1.83815 0
pars: 0.58822 0.47049 -0.39448 -0.58757 -2.05502 0.04656
pars: 0.24142 -0.08857 0.17073 -0.22123 -2.16295 0.36573
pars: -0.25485 -0.61181 -0.39281 -0.55381 -2.30537 0.5431
pars: -0.95996 -1.05417 0.05433 -0.3315 -2.16562 0.73866
pars: -1.52441 -1.28179 -0.24338 -0.09273 -1.48444 0.87986
pars: -1.15677 -1.14532 -0.15766 -0.32371 -1.9606 0.78088
pars: -1.2591 -1.23906 -0.00914 -0.54176 -1.74795 0.73881
pars: -1.28632 -1.23923 -0.06489 -0.37148 -1.72418 0.71526
pars: -1.37476 -1.3354 -0.03139 -0.43287 -1.62519 0.66829
pars: -1.43518 -1.36913 -0.07069 -0.36322 -1.49214 0.59704
pars: -1.50537 -1.39061 -0.03715 -0.40209 -1.33302 0.57215
pars: -1.55804 -1.48577 -0.11287 -0.42946 -1.21264 0.53812
pars: -1.53464 -1.42687 -0.0852 -0.38865 -1.29386 0.558
pars: -1.54606 -1.47357 -0.04788 -0.37299 -1.26878 0.5154
pars: -1.54606 -1.47357 -0.04788 -0.37299 -1.26878 0.5154
#>
pars: 1 1 0 0 -1.35869 0
pars: 0.26969 0.48413 0.13062 0.37138 -1.50855 0.15196
pars: -1.92124 -1.06346 0.52247 1.48551 -1.95813 0.60783
pars: -3.94772 -2.40713 -0.47652 -1.45121 -2.46454 1.06517
pars: -2.05317 -1.14577 0.37728 1.06076 -1.99643 0.6397
pars: -2.24177 -1.28178 0.31364 0.65504 -2.04218 0.69747
pars: -2.61685 -1.51707 0.42349 0.63143 -2.07707 0.83261
pars: -3.26866 -1.92596 0.61438 0.59039 -2.13769 1.06746
pars: -4.1194 -2.03377 -0.24849 1.01778 -1.97888 1.21674
pars: -3.31866 -1.88614 0.45421 0.68271 -2.10153 1.06158
pars: -3.44366 -1.81523 0.38836 0.67524 -1.98086 1.06261
pars: -3.53173 -1.85375 0.53088 0.65662 -1.88523 1.03885
pars: -3.63928 -1.97209 0.44911 0.66534 -1.80171 1.02159
pars: -3.67057 -1.9039 0.47921 0.72471 -1.6359 0.97625
pars: -3.68464 -1.93999 0.44261 0.64383 -1.6141 0.983
pars: -3.68464 -1.93999 0.44261 0.64383 -1.6141 0.983