Prepare Argos data for fitting state-space model

Source: R/prefilter.R

prefilter does the following: (1) flags (keep = FALSE) observations with duplicate dates & removes subsequent observations occurring within min.dt s of one another (2) determines Argos data type (LS, KF, G, or GL); (3) uses sda filter to identify extreme locations. sda is a fast, vectorized version of the now CRAN-archived argosfilter::sdafilter. sda is a native implementation of that found in the currently archived trip package (by MD Sumner: https://github.com/Trackage/trip). (4) projects lonlat coords to mercator x,y coords (in km) & shifts longitudes that straddle -180,180 to 0,360 and vice-versa; (5) adds location error multiplication factors based on Argos location class (for type LS);

prefilter(
  x,
  vmax = 5,
  ang = c(15, 25),
  distlim = c(2500, 5000),
  spdf = TRUE,
  min.dt = 0,
  emf = NULL
)

Arguments

x

input data, must have 5 (LS), or 8 (KF) columns (see details)

vmax

max travel rate (m/s)

ang

angles of outlier location "spikes" (default is c(15,25) deg); ang = NA turns off sda filter in favour of speedfilter

distlim

lengths of outlier location "spikes" in km (default is c(2.5, 5) m); distlim = NA turns off sda filter in favour of speedfilter. Either ang = NA or distlim = NA are sufficient.

spdf

turn speed filter on/off (logical; default is TRUE)

min.dt

minimum allowable time difference in s between observations; dt < min.dt will be ignored by the SSM. Default is NA: all time differences > 0 are allowed

emf

optionally supplied data.frame of error multiplication factors for Argos location quality classes. see Details

Value

an sf object with all observations passed from data and the following appended columns

keep

logical indicating whether observation should be ignored by sfilter (FALSE)

obs.type

flag indicating whether KF or LS measurement model applies

emf_x

error multiplication factors for x direction

emf_y

error multiplication factors for y direction

geometry

sf POINT object giving x,y coordinates in km

Details

called by fit_ssm.