# Computes distance matrices among the samples of two or more multivariate time-series. Computes distance matrices among the samples of two or more multivariate time-series provided in a single dataframe (generally produced by `prepareSequences`), identified by a grouping column (argument `grouping.column`). Distances can be computed with the methods "manhattan", "euclidean", "chi", and "hellinger", and are implemented in the function `distance`. The function uses the packages `parallel`, `foreach`, and `doParallel` to compute distances matrices among different sequences in parallel. It is configured to use all processors available minus one. ```r distanceMatrix( sequences = NULL, grouping.column = NULL, time.column = NULL, exclude.columns = NULL, method = "manhattan", 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. - `method`: character string naming a distance metric. Valid entries are: "manhattan", "euclidean", "chi", and "hellinger". Invalid entries will throw an error. - `parallel.execution`: boolean, if `TRUE` (default), execution is parallelized, and serialized if `FALSE`. ## Returns A list with named slots containing the the distance matrices 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(sequenceA) data(sequenceB) #preparing datasets AB.sequences <- prepareSequences( sequence.A = sequenceA, sequence.A.name = "A", sequence.B = sequenceB, sequence.B.name = "B", merge.mode = "complete", if.empty.cases = "zero", transformation = "hellinger" ) #computing distance matrix AB.distance.matrix <- distanceMatrix( sequences = AB.sequences, grouping.column = "id", method = "manhattan", parallel.execution = FALSE ) #plot plotMatrix(distance.matrix = AB.distance.matrix) ``` ## See Also `distance` ## Author(s) Blas Benito

distanceMatrix function