importance_ls_cpp {distantia} | R Documentation |
(C++) Contribution of Individual Variables to the Dissimilarity Between Two Aligned Time Series
Description
Computes the contribution of individual variables to the similarity/dissimilarity between two aligned multivariate time series. This function generates a data frame with the following columns:
variable: name of the individual variable for which the importance is being computed, from the column names of the arguments
x
andy
.psi: global dissimilarity score
psi
of the two time series.psi_only_with: dissimilarity between
x
andy
computed from the given variable alone.psi_without: dissimilarity between
x
andy
computed from all other variables.psi_difference: difference between
psi_only_with
andpsi_without
.importance: contribution of the variable to the similarity/dissimilarity between
x
andy
, computed as(psi_difference * 100) / psi_all
. Positive scores represent contribution to dissimilarity, while negative scores represent contribution to similarity.
Usage
importance_ls_cpp(x, y, distance = "euclidean")
Arguments
x |
(required, numeric matrix) multivariate time series. |
y |
(required, numeric matrix) multivariate time series with the same number of columns and rows as 'x'. |
distance |
(optional, character string) distance name from the "names"
column of the dataset |
Value
data frame
See Also
Other Rcpp_importance:
importance_dtw_cpp()
,
importance_dtw_legacy_cpp()
Examples
#simulate two regular time series
x <- zoo_simulate(
seed = 1,
irregular = FALSE
)
y <- zoo_simulate(
seed = 2,
irregular = FALSE
)
#same number of rows
nrow(x) == nrow(y)
#compute importance
df <- importance_ls_cpp(
x = x,
y = y,
distance = "euclidean"
)
df