Computes the point- or vector-wise dissimilarity between each pair of trajectories.
mt_distmat(
data,
use = "ln_trajectories",
save_as = "distmat",
dimensions = c("xpos", "ypos"),
weights = rep(1, length(dimensions)),
pointwise = TRUE,
minkowski_p = 2,
na_rm = FALSE
)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.
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 weights.
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
results. If FALSE, 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, minkowski_p
= 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.
Dirk U. Wulff
Jonas M. B. Haslbeck
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")
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