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Data Frame with Contribution of Individual Variables to Dissimilarity to Wide Format

Source: R/utils_importance_df_to_wide.R
utils_importance_df_to_wide.Rd

Takes as input a data frame returned by distantia_importance() to return a data frame with one pair of time series per row, and the following columns:

  • most_similar: name of the variable with the highest contribution to similarity (most negative value in the importance column) for each pair of time series.

  • most_dissimilar: name of the variable with the highest contribution to dissimilarity (most positive value in the importance column) for each pair of time series.

  • importance__variable_name: contribution to similarity (negative values) or dissimilarity (positive values) of the given variable.

  • psi_only_with__variable_name: dissimilarity of the two time series when only using the given variable.

  • psi_without__variable_name: dissimilarity of the two time series when removing the given variable.

Usage

utils_importance_df_to_wide(df = NULL, sep = "__")

Arguments

df

(required, data frame) Output of distantia_importance(). Default: NULL

sep

(required, character string) Separator between the name of the importance metric and the time series variable. Default: "__".

Value

data frame

See also

Other internal_dissimilarity_analysis: utils_block_size(), utils_cluster_hclust_optimizer(), utils_cluster_kmeans_optimizer(), utils_cluster_silhouette()

Examples

#prepare time series
data("fagus_dynamics")

tsl <- tsl_initialize(
  x = fagus_dynamics,
  name_column = "name",
  time_column = "time"
) |>
  tsl_transform(
    f = f_scale
  )

#importance data frame
df <- distantia_importance(
  tsl = tsl
)
df
#>         x      y       psi    variable importance               effect
#> 1 Germany  Spain 1.3429956         evi   6.512321 decreases similarity
#> 2 Germany  Spain 1.3429956    rainfall  12.505764 decreases similarity
#> 3 Germany  Spain 1.3429956 temperature -26.509115 increases similarity
#> 4 Germany Sweden 0.8571217         evi  29.026504 decreases similarity
#> 5 Germany Sweden 0.8571217    rainfall  -4.209397 increases similarity
#> 6 Germany Sweden 0.8571217 temperature -26.661768 increases similarity
#> 7   Spain Sweden 1.4803954         evi  -6.625437 increases similarity
#> 8   Spain Sweden 1.4803954    rainfall  -4.416941 increases similarity
#> 9   Spain Sweden 1.4803954 temperature  13.668290 decreases similarity
#>   psi_difference psi_without psi_only_with  distance diagonal weighted
#> 1     0.08746019   1.3091727     1.3966329 euclidean     TRUE     TRUE
#> 2     0.16795186   1.2442260     1.4121779 euclidean     TRUE     TRUE
#> 3    -0.35601625   1.4170621     1.0610458 euclidean     TRUE     TRUE
#> 4     0.24879247   0.7850271     1.0338196 euclidean     TRUE     TRUE
#> 5    -0.03607966   0.8800855     0.8440058 euclidean     TRUE     TRUE
#> 6    -0.22852381   0.9121250     0.6836012 euclidean     TRUE     TRUE
#> 7    -0.09808266   1.5155933     1.4175106 euclidean     TRUE     TRUE
#> 8    -0.06538819   1.5451489     1.4797607 euclidean     TRUE     TRUE
#> 9     0.20234474   1.4411407     1.6434855 euclidean     TRUE     TRUE
#>   ignore_blocks lock_step robust
#> 1         FALSE     FALSE   TRUE
#> 2         FALSE     FALSE   TRUE
#> 3         FALSE     FALSE   TRUE
#> 4         FALSE     FALSE   TRUE
#> 5         FALSE     FALSE   TRUE
#> 6         FALSE     FALSE   TRUE
#> 7         FALSE     FALSE   TRUE
#> 8         FALSE     FALSE   TRUE
#> 9         FALSE     FALSE   TRUE

#to wide format
df_wide <- utils_importance_df_to_wide(
  df = df
)
df_wide
#>         x      y       psi most_similar most_dissimilar importance__evi
#> 1 Germany  Spain 1.3429956  temperature        rainfall        6.512321
#> 2 Germany Sweden 0.8571217  temperature             evi       29.026504
#> 3   Spain Sweden 1.4803954          evi     temperature       -6.625437
#>   importance__rainfall importance__temperature psi_only_with__evi
#> 1            12.505764               -26.50912           1.396633
#> 2            -4.209397               -26.66177           1.033820
#> 3            -4.416941                13.66829           1.417511
#>   psi_only_with__rainfall psi_only_with__temperature psi_without__evi
#> 1               1.4121779                  1.0610458        1.3091727
#> 2               0.8440058                  0.6836012        0.7850271
#> 3               1.4797607                  1.6434855        1.5155933
#>   psi_without__rainfall psi_without__temperature
#> 1             1.2442260                 1.417062
#> 2             0.8800855                 0.912125
#> 3             1.5451489                 1.441141