Computes the point- or vector-wise dissimilarity between each pair of trajectories.
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).
a character string specifying which trajectory data should be used.
a character string specifying where the resulting data should be stored.
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.
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
To use the original variables, set
weights = NULL.
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
mt_distmat measures dissimilarity once
(by treating the various points as independent dimensions).
an integer specifying the distance metric.
minkowski_p = 1 computes the city-block distance,
= 2 (the default) computes the Euclidian distance,
minkowski_p = 3
the cubic distance, etc.
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
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
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")