fit a random walk with time-varying move persistence to temporally regular or irregular location data
fit_mpm(
x,
what = "predicted",
model = c("jmpm", "mpm"),
coords = 3:4,
control = mpm_control(),
inner.control = NULL,
optim = NULL,
optMeth = NULL,
verbose = NULL
)
a ssm_df
fit object or a data frame of observations (see details)
if a ssm_df
fit object is supplied then what
determines
whether fitted, predicted (default), or rerouted values are mapped; ignored if
x
is a data frame
mpm model to fit; either mpm
with unpooled random walk
variance parameters (sigma_(g,i)
) or jmpm
with a single,
pooled random variance parameter (sigma_g
)
column numbers of the location coordinates (default = 3:4)
list of control settings for the outer optimizer (see mpm_control for details)
list of control parameters for the inner optimization
is deprecated, use ssm_control(optim = "optim") instead, see ssm_control for details
is deprecated, use ssm_control(method = "L-BFGS-B") instead, see ssm_control for details
is deprecated, use ssm_control(verbose = 1) instead, see ssm_control for details
a list with components
fitted
a dataframe of fitted locations
par
model parameter summary
data
input data.frame
tmb
the TMB
object
opt
the object returned by the optimizer
Jonsen ID, McMahon CR, Patterson TA, et al. (2019) Movement responses to environment: fast inference of variation among southern elephant seals with a mixed effects model. Ecology. 100(1):e02566
## fit jmpm to two southern elephant seal tracks
xs <- fit_ssm(sese2, spdf=FALSE, model = "rw", time.step=72, control = ssm_control(verbose = 0))
#>
#>
fmpm <- fit_mpm(xs, model = "jmpm")
#> fitting jmpm...
#>
pars: 0 0 0
pars: -0.63038 -0.77416 0.05745
pars: -0.21961 -0.90381 0.95993
pars: 0.1024 -0.34798 0.19353
pars: -0.12912 -0.74761 0.74455
pars: -0.17853 -0.8329 0.86216
pars: -0.23363 -0.801 0.86597
pars: -0.27635 -0.61987 0.76146
pars: -0.40458 -0.79384 0.62587
pars: -0.36412 -0.6719 0.40545
pars: -0.38583 -0.70377 0.60569
pars: -0.43697 -0.67572 0.577
pars: -0.38583 -0.70377 0.60569