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Calculate Mahalanobis distance

Source: R/m_dist.R
m_dist.Rd

This function is used to calculate the Mahalanobis distance for a multivariate time series.

Usage

m_dist(
  data,
  sampling_rate = 1,
  smooth_dur = 0,
  overlap = 0,
  consec = FALSE,
  cum_sum = FALSE,
  bl_start = 0,
  bl_end = floor(nrow(data)/sampling_rate),
  bl_cov = FALSE
)

Arguments

data

A data frame or matrix with one row for each time point. Note that the Mahalanobis distance calculation should be carried out on continuous data only, so if your data contain logical, factor or character data, proceed at your own risk...errors (or at least meaningless results) will probably ensue.

sampling_rate

The sampling rate in Hz (data should be regularly sampled). If not specified it will be assumed to be 1 Hz.

smooth_dur

The length, in minutes, of the window to use for calculation of "comparison" values. If not specified or zero (the default), there will be no smoothing (a distance will be calculated for each data observation).

overlap

The amount of overlap, in minutes, between consecutive "comparison" windows. smooth_dur - overlap will give the time resolution of the output distance time series. Default is 0, which means no overlap. Overlap will also be set to zero if smooth_dur is unspecified or zero.

consec

Logical (default FALSE). If consec is TRUE, then the calculated distances are between *consecutive windows* of duration smooth_dur, sliding forward over the data set by a time step of (smooth_dur - overlap) minutes. Default is consec = FALSE, which means each output distance will be the distance between the current "comparison" window and the baseline window. If consec is TRUE, bl_start and bl_end inputs will be used *only* to define the period used to calculate the data covariance matrix.

cum_sum

Logical (default FALSE). If cum_sum is TRUE, then output will be the cumulative sum of the calculated distances, rather than the distances themselves. Default is cum_sum = FALSE.

bl_start

Start time (in seconds since start of the data set) of the baseline period. The mean data values for this period will be used as the 'control' to which all "comparison" data points (or windows) will be compared. If not specified, it will be assumed to be 0 (start of record). If consec is TRUE, then adjacent windows will be compared, and this input will have no effect except to define the data used to compute the covariance matrix, if bl_cov is TRUE.

bl_end

End time (in seconds since start of the data set) of the baseline period. If not specified, the entire data set will be used (baseline_end will be the last sampled time-point in the data set). If consec = TRUE, then adjacent windows will be compared, and this input will have no effect except to define the data used to compute the covariance matrix, if bl_cov is TRUE.

bl_cov

Logical. If bl_cov is TRUE, then a covariance matrix using all data *in the baseline period* will be used for calculating the Mahalanobis distance. Default is bl_cov = FALSE, which uses *all* data (in the entire dataset) to compute the covariance matrix.

Value

Data frame containing results: variable seconds is times (in seconds since start of dataset) at which Mahalanobis distances are reported. If a smooth_dur window was applied, then the reported times will be the midpoint of each "comparison" window. Variable dist contains the computed Mahalanobis distances.

Examples

BW <- beaked_whale
m_dist_result <- m_dist(BW$A$data, BW$A$sampling_rate)