The minimum step "auto" chooses for each trajectory

Returns the threshold that min_step = "auto" uses in add_kinematics() and summarise_path(), one per trajectory, with the positional noise it is estimated from. Use it to see whether the automatic threshold suits your data, to report it, or to choose a threshold of your own relative to it.

The threshold is computed by the same code that add_kinematics() uses, so the two always agree. See the section "The minimum step" in add_kinematics() for how it is estimated and when to set it yourself.

Usage

compute_min_step(data)

Arguments

data

An anipoint with two or three spatial axes, grouped one trajectory per group, as it is by its declared keys. Any coordinate system works; non-Cartesian frames are converted to Cartesian for the computation.

Value

A data frame with one row per trajectory: the key columns, then

• positional_noise: the estimated standard deviation of the tracking noise on each axis, in the frame's spatial unit. 0 when the trajectory is too short, or moves too little, to estimate it.

• min_step: the threshold "auto" uses, in the frame's spatial unit: three times positional_noise, or half the trajectory's median step if that is smaller. Where it is less than three times positional_noise, the cap on the median step set it.

See also

add_kinematics(), whose min_step takes the threshold.

Examples

af <- anicore::example_anipoint(n_obs = 50, n_individuals = 2, n_keypoints = 1)
compute_min_step(af)
#> # A tibble: 2 × 6
#>   individual keypoint session trial positional_noise min_step
#>        <int> <fct>      <int> <int>            <dbl>    <dbl>
#> 1          1 centroid       1     1            0.780    0.403
#> 2          2 centroid       1     1            1.10     0.409

# One threshold for every trajectory, so their turning is filtered alike
thresholds <- compute_min_step(af)
add_kinematics(af, min_step = max(thresholds$min_step))
#> # Individuals: 1, 2
#> # Keypoints:   centroid
#> # Sessions:    1
#> # Trials:      1
#>    individual keypoint session trial  time      x       y confidence speed
#>         <int> <fct>      <int> <int> <int>  <dbl>   <dbl>      <dbl> <dbl>
#>  1          1 centroid       1     1     1 -0.187 -0.870       0.511 1.04 
#>  2          1 centroid       1     1     2 -0.812 -0.0392      0.540 0.748
#>  3          1 centroid       1     1     3 -1.64  -0.518       0.588 0.791
#>  4          1 centroid       1     1     4  0.508 -0.912       0.904 1.73 
#>  5          1 centroid       1     1     5  1.75   0.150       0.754 0.677
#>  6          1 centroid       1     1     6  0.592  0.440       0.621 0.691
#>  7          1 centroid       1     1     7  1.02   1.32        0.806 0.255
#>  8          1 centroid       1     1     8  0.122  0.247       0.822 1.06 
#>  9          1 centroid       1     1     9 -1.08   0.942       0.746 0.698
#> 10          1 centroid       1     1    10 -1.14  -0.342       0.795 0.668
#> # ℹ 90 more rows
#> # ℹ 12 more variables: acceleration <dbl>, cumulative_distance <dbl>,
#> #   v_x <dbl>, v_y <dbl>, a_x <dbl>, a_y <dbl>, course <dbl>,
#> #   course_unwrapped <dbl>, turning_speed <dbl>, turning_rate <dbl>,
#> #   turning_acceleration <dbl>, cumulative_turning <dbl>