grab() lets you obtain fitted, predicted, rerouted or
data tibble's from a compound tibble created when fitting to multiple
individual data sets. The specified tibble's are appended to a single output
tibble.
grab(x, what = "fitted", as_sf = FALSE, normalise = FALSE, group = FALSE)a aniMotum ssm_df or mpm_df model object
the tibble to be grabbed; either fitted, predicted,
rerouted (ssm_df only), or data (single letters can be used).
logical; if FALSE (default) then return a tibble with
un-projected longlat coordinates, otherwise return an sf tibble. Ignored
if x is an mpm model object.
logical; if output includes a move persistence estimate,
should g (the move persistence index) be normalised to have minimum = 0 and
maximum = 1 (default = FALSE). Note, this normalisation is not applied to the
standard errors of the logit-scale move persistence estimates (logit_g,
logit_g.se).
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).
a tibble with all individual tibble's appended
if multiple ssm_df model objects are present in x, as_sf = TRUE,
and at least 1 estimated track has a coordinate reference system (crs) with
longitude centered on 180 (e.g. a track straddling -180,180) then all tracks
will be re-projected to that crs.
## generate an ssm fit object
xs <- fit_ssm(ellie, spdf=FALSE, model = "rw", time.step=24, control = ssm_control(verbose = 0))
#>
## grab predicted values as an un-projected tibble
preds <- grab(xs, what = "predicted")