join() joins ssm-predicted locations and mpm-estimated behavioural index into a single tibble. If the ssm-predicted tibble is a projected sf object then the output of join will also be an sf object (default). This can be avoided by using as_sf = FALSE.
join(
ssm,
mpm,
what.ssm = "predicted",
as_sf = FALSE,
normalise = FALSE,
group = FALSE
)an aniMotum ssm fitted model object
an aniMotum mpm fitted model object
specifies whether ssm predicted or fitted values are to be extracted
logical; if FALSE then return a tibble with un-projected lonlat coordinates, otherwise return an sf tibble
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).
logical; should g be normalised among individuals as a group, a 'relative g', or separately to highlight regions of lowest and highest move persistence along a track (default = FALSE).
a single tbl with all individuals
## load example aniMotum fit objects (to save time)
## generate a ssm fit object
xs <- fit_ssm(ellie, spdf=FALSE, model = "rw", time.step=24, control = ssm_control(verbose = 0))
#>
xm <- fit_mpm(xs, what = "p", model = "mpm")
#> fitting mpm...
#>
pars: 0 0 0
pars: -0.65944 -0.75114 -0.03049
pars: -2.63775 -3.00457 -0.12194
pars: -2.73937 -4.15687 -0.30416
pars: -0.86743 -1.09171 -0.05785
pars: -1.18976 -1.82073 -0.12602
pars: -0.8491 -2.52724 -0.28347
pars: -1.13954 -1.92487 -0.14923
pars: -1.13979 -1.98651 -0.24977
pars: -1.12989 -1.92935 -0.35244
pars: -1.12789 -1.95509 -0.46751
pars: -1.12669 -1.93037 -0.58281
pars: -1.12848 -1.92558 -0.81238
pars: -1.12944 -1.87726 -1.377
pars: -0.809 -1.84174 -2.25192
pars: -1.0756 -1.9101 -1.45917
pars: -1.1263 -1.96239 -1.53282
pars: -1.1094 -1.91349 -1.46293
pars: -1.1047 -1.90968 -1.49657
pars: -1.10821 -1.90297 -1.55182
pars: -1.1032 -1.91244 -1.6407
pars: -1.10231 -1.91103 -1.70906
pars: -1.10231 -1.91103 -1.70906
## join predicted values as an un-projected tibble
xsm <- join(xs, xm)
xsm
#> # A tibble: 113 × 11
#> id date lon lat x y x.se y.se logit_g
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 54591 2012-03-05 05:00:00 111. -66.4 12309. -9956. 1.38 1.44 0.103
#> 2 54591 2012-03-06 05:00:00 110. -66.4 12287. -9947. 25.6 0.419 0.103
#> 3 54591 2012-03-07 05:00:00 110. -66.5 12298. -9963. 4.48 2.47 0.103
#> 4 54591 2012-03-08 05:00:00 110. -66.4 12288. -9949. 32.7 28.8 0.107
#> 5 54591 2012-03-09 05:00:00 110. -66.5 12299. -9972. 9.07 2.55 0.114
#> 6 54591 2012-03-10 05:00:00 111. -66.4 12302. -9958. 21.3 18.6 0.123
#> 7 54591 2012-03-11 05:00:00 111. -66.5 12323. -9967. 22.3 19.6 0.136
#> 8 54591 2012-03-12 05:00:00 110. -66.5 12287. -9962. 28.2 9.78 0.149
#> 9 54591 2012-03-13 05:00:00 110. -66.4 12288. -9950. 35.4 31.1 0.168
#> 10 54591 2012-03-14 05:00:00 110. -66.4 12291. -9938. 32.8 28.9 0.192
#> # ℹ 103 more rows
#> # ℹ 2 more variables: logit_g.se <dbl>, g <dbl>