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:

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 distances (see distances$name). Default: "euclidean".

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

[Package distantia version 2.0.2 Index]