Compute distance matrix.

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
)

Arguments

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.

Value

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.

Details

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)).

Author

Dirk U. Wulff

Jonas M. B. Haslbeck

Examples

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