sim_fit()
.sim_post()
, a function for posterior simulations from SSM fits, conditional on data and movement parameterssim_post
objectssim_filter()
can now use arbitrary variables, including user-appended environmental variables, for filtering tracks simulated with sim_fit()
sim_fit()
now simulates tracks from user-specified start
and end
locations that differ from the estimated track start and end.fit_ssm()
now handles "generic location" data provided the locations have x
and y
standard errors. These data can be light-level geolocations, acoustic telemetry positions, or other location data. Input data should have lc = "GL"
for all generic locations.min.dt
argument in fit_ssm()
, the default is now min.dt = 0
(no minimum time interval between observations, but any subsequent observations that occur at the same time are ignored when fitting an SSM).route_path()
where simulated tracks that are entirely on land resulted in an errorgrab()
where multiple data sets with lon
modulo 0,360 resulted in an errorroute_path()
where rerouting tracks that have no locations on land resulted in an error. In these cases, route_path()
now returns a tibble identical to that supplied (fitted or predicted locations) and issues a message on the console.aniMotum
, an R package for animal movement data: rapid quality control, behavioural estimation and simulation. Accepted 06/12/2022.format_data()
to pre-process non-default data formats into that expected by fit_ssm
fit_ssm
), related to format_data
sf-tibble
or sf-data.frame
that caused an error when fitting SSM's via fit_ssm
Overview
vignette, describing input data structures in greater detailroute_path()
a wrapper function calling pathroutr
to re-route fit_ssm
estimated or simfit
simulated tracks around land barriersfit_ssm(model = "mp")
to allow simultaneous estimation of locations and move persistence. This approach may be preferable to using fit_mpm()
on a fit_ssm
model object. fit_mpm()
is retained for less error-prone (GPS) location datagrab()
, either on tracks separately or as a group for a relative measure that spans 0 - 1. sim_filter()
to calculate similarity between simulated and ssm-estimated tracks, and returns the most similar simulated tracks based on a user-specified quantilemap()
to replace fmap()
for faster, more flexible estimated track maps & fixes to coastline and other mapping issues for tracks that cross -180,180map()
via ggspatial::annotation_map_tile
for more detailed coastlines on large-scale mapscrw
model fitting via fit_ssm()
by turning off travel rate standard error (s.se) estimation in ssm_control()
as the default. SE estimation can be turned on via control = ssm_control(se = TRUE)
.summary
function for displaying information about SSM fits. hcl.pals()
palette, using the pal
argument in many of the plot functions.fG_ssm
, fG_mpm
migrated to ssm_df
, mpm_df
plot.ssm_df
, plot.mpm_df
, plot.osar
, plot.sim
, plot.simfit
sim()
to simulate animal tracks using the rw
, crw
or mpm
process models. The rw
and crw
models can also be specified with state-switching between multiple behavioural states. Tracks can be simulated with or without Argos (LS or KF) errors, as time-regular or time-irregularsimfit()
to simulate animal tracks from fit_ssm
fit objectsplot()
methods for sim
and simfit
objectsfit_mpm
can take a fit_ssm
object directly as input, removing need for user to manipulate data prior to calling fit_mpm
fit_mpm
can fit to SSM-predicted
(time regular) or SSM-fitted
(time irregular) locations, via what
argumentfit_mpm
can fit to lon,lat or x,y coordinates, via coords
argumentssm_control()
for centralized control over optimizer and optimization method choices, optimizer parameters, and foieGras
model parameter boundsacf()
plots as option when visualising prediction residuals calculated from osar()
hist
plots as option when visualising prediction residuals calculated from osar()
verbose
, optim
, optMeth
, and lpsi
arguments to fit_ssm
wesanderson::wes_palette("Zissou1")
as default palette for plots/mapsfG_ssm
objects can now plot individuals all on 1 page (pages = 1
) or on separate pages (pages = 0
)fG_mpm
objects now availablefmap
) can optionally take an fG_mpm
object to colour locations by behavioural index (gamma_t
)time.step=NA
causing locations to be estimated only at observation timesdata.frame
for Least-Squares and/or GPS locationsrw
process model is fit to Argos KF/KS datadplyr::do
, which is superseded as of dplyr 1.0.0
argosfilter::sdafilter
in favour of trip::sda
(which is a faster, vectorized version of the former) to prefilter outlier locationsfit_mpm()
lonerr, laterr
variables to input dataosar()
and generic plot()
method for osar
outputplot()
method for fG_ssm
fit objectssf
-enabled mapping function, via fmap()
+init:epsg=
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