# Computes distance among pairs of aligned samples in two or more multivariate time-series. Computes the distance (one of: "manhattan", "euclidean", "chi", or "hellinger") between pairs of aligned samples (same order/depth/age) in two or more multivariate time-series. ```r distancePairedSamples( sequences = NULL, grouping.column = NULL, time.column = NULL, exclude.columns = NULL, same.time = FALSE, method = "manhattan", sum.distances = FALSE, parallel.execution = TRUE ) ``` ## Arguments - `sequences`: dataframe with multiple sequences identified by a grouping column. Generally the ouput of `prepareSequences`. - `grouping.column`: character string, name of the column in `sequences` to be used to identify separates sequences within the file. This argument is ignored if `sequence.A` and `sequence.B` are provided. - `time.column`: character string, name of the column with time/depth/rank data. The data in this column is not modified. - `exclude.columns`: character string or character vector with column names in `sequences`, or `squence.A` and `sequence.B` to be excluded from the analysis. - `same.time`: boolean. If `TRUE`, samples in the sequences to compare will be tested to check if they have the same time/age/depth according to `time.column`. This argument is only useful when the user needs to compare two sequences taken at different sites but same time frames. - `method`: character string naming a distance metric. Valid entries are: "manhattan", "euclidean", "chi", and "hellinger". Invalid entries will throw an error. - `sum.distances`: boolean, if `TRUE` (default option), the distances between samples are summed, and the output of the function (now a list with a single number on each slot) can be directly used as input for the argument `least.cost` in the function `psi`. - `parallel.execution`: boolean, if `TRUE` (default), execution is parallelized, and serialized if `FALSE`. ## Returns A list with named slots (names of the sequences separated by a vertical line, as in "A|B") containing numeric vectors with the distance between paired samples of every possible combination of sequences according to `grouping.column`. ## Details Distances are computed as: * `manhattan`: `d \<- sum(abs(x - y))` * `euclidean`: `d \<- sqrt(sum((x - y)^2))` * `chi`: `xy \<- x + y y. \<- y / sum(y) x. \<- x / sum(x) d \<- sqrt(sum(((x. -y.)^2) / (xy / sum(xy))))` * `hellinger`: `d \<- sqrt(1/2 * sum(sqrt(x) - sqrt(y))^2)` Note that zeroes are replaced by 0.00001 whem `method` equals "chi" or "hellinger". ## Examples ```r #loading data data(climate) #preparing sequences #notice the argument paired.samples climate.prepared <- prepareSequences( sequences = climate, grouping.column = "sequenceId", time.column = "time", paired.samples = TRUE ) #compute pairwise distances between paired samples climate.prepared.distances <- distancePairedSamples( sequences = climate.prepared, grouping.column = "sequenceId", time.column = "time", exclude.columns = NULL, method = "manhattan", sum.distances = FALSE, parallel.execution = FALSE ) ``` ## See Also `distance` ## Author(s) Blas Benito

distancePairedSamples function