Computes the point- or vector-wise dissimilarity between each pair of trajectories.

- data
a mousetrap data object created using one of the mt_import functions (see mt_example for details). Alternatively, a trajectory array can be provided directly (in this case

`use`

will be ignored).- use
a character string specifying which trajectory data should be used.

- save_as
a character string specifying where the resulting data should be stored.

- dimensions
a character vector specifying which trajectory variables should be used. Can be of length 2 or 3 for two-dimensional or three-dimensional trajectories respectively.

- weights
numeric vector specifying the relative importance of the variables specified in

`dimensions`

. Defaults to a vector of 1s implying equal importance. Technically, each variable is rescaled so that the standard deviation matches the corresponding value in`weights`

. To use the original variables, set`weights = NULL`

.- pointwise
boolean specifying the way dissimilarity between the trajectories is measured (see Details). If

`TRUE`

(the default),`mt_distmat`

measures the average dissimilarity and then sums the results. If`FALSE`

,`mt_distmat`

measures dissimilarity once (by treating the various points as independent dimensions).- minkowski_p
an integer specifying the distance metric.

`minkowski_p = 1`

computes the city-block distance,`minkowski_p = 2`

(the default) computes the Euclidian distance,`minkowski_p = 3`

the cubic distance, etc.- na_rm
logical specifying whether trajectory points containing NAs should be removed. Removal is done column-wise. That is, if any trajectory has a missing value at, e.g., the 10th recorded position, the 10th position is removed for all trajectories. This is necessary to compute distance between trajectories.

A mousetrap data object (see mt_example) with an additional
object added (by default called `distmat`

) containing the distance
matrix. If a trajectory array was provided directly as `data`

, only
the distance matrix will be returned.

`mt_distmat`

computes point- or vector-wise dissimilarities between
pairs of trajectories. Point-wise dissimilarity refers to computing the
distance metric defined by `minkowski_p`

for every point of the
trajectory and then summing the results. That is, if `minkowski_p = 2`

the point-wise dissimilarity between two trajectories, each defined by a set
of x and y coordinates, is calculated as `sum(sqrt((x_i-x_j)^2 + (y_i-y_j)^2))`

.
Vector-wise dissimilarity, on the other hand refers to computing the distance
metric once for the entire trajectory. That is, vector-wise dissimilarity is
computed as `sqrt(sum((x_i-x_j)^2 + (y_i-y_j)^2))`

.

```
# Length normalize trajectories
mt_example <- mt_length_normalize(mt_example)
# Compute distance matrix
mt_example <- mt_distmat(mt_example, use="ln_trajectories")
```