`psi.Rd`

Computes the sum of distances between consecutive samples in a multivariate time-series. Required to compute the measure of dissimilarity `psi`

(Birks and Gordon 1985). Distances can be computed through the methods "manhattan", "euclidean", "chi", and "hellinger", and are implemented in the function `distance`

.

psi( least.cost = NULL, autosum = NULL, parallel.execution = TRUE)

least.cost | character string, name of the column with time/depth/rank data. The data in this column is not modified. |
---|---|

autosum | dataframe with one or several multivariate time-series identified by a grouping column. |

parallel.execution | boolean, if |

A list with named slots, each one with a psi value.

The measure of dissimilarity `psi`

is computed as: `least.cost - (autosum of sequences)) / autosum of sequences`

. It has a lower limit at 0, while there is no upper limit.

#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 ) #computing least cost matrix AB.least.cost.matrix <- leastCostMatrix( distance.matrix = AB.distance.matrix, diagonal = FALSE, parallel.execution = FALSE ) AB.least.cost.path <- leastCostPath( least.cost.matrix = AB.least.cost.matrix, distance.matrix = AB.distance.matrix, parallel.execution = FALSE ) #extracting least cost AB.least.cost <- leastCost( least.cost.path = AB.least.cost.path, parallel.execution = FALSE ) #autosum AB.autosum <- autoSum( sequences = AB.sequences, least.cost.path = AB.least.cost.path, grouping.column = "id", parallel.execution = FALSE ) AB.autosum#> $`A|B` #> [1] 46.86205 #>AB.psi <- psi( least.cost = AB.least.cost, autosum = AB.autosum, parallel.execution = FALSE ) AB.psi#> $`A|B` #> [1] 2.263633 #>