Transforms a data frame returned by momentum() to wide format with the following columns:
most_similar: name of the variable with the highest contribution to similarity (most negative value in theimportancecolumn) for each pair of time series.most_dissimilar: name of the variable with the highest contribution to dissimilarity (most positive value in theimportancecolumn) 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.
Arguments
- df
(required, data frame) Output of
momentum(),momentum_ls(), ormomentum_dtw(). Default: NULL- sep
(required, character string) Separator between the name of the importance metric and the time series variable. Default: "__".
See also
Other momentum_support:
momentum_aggregate(),
momentum_boxplot(),
momentum_model_frame(),
momentum_spatial(),
momentum_stats()
Examples
tsl <- tsl_initialize(
x = distantia::albatross,
name_column = "name",
time_column = "time"
) |>
tsl_transform(
f = f_scale_global
)
#importance data frame
df <- momentum(
tsl = tsl
)
df
#> x y psi variable importance effect
#> 1 X132 X134 1.163499 x 89.721187 decreases similarity
#> 2 X132 X134 1.163499 y 101.305396 decreases similarity
#> 3 X132 X134 1.163499 speed -28.286386 increases similarity
#> 4 X132 X134 1.163499 temperature 78.130508 decreases similarity
#> 5 X132 X134 1.163499 heading -43.644053 increases similarity
#> 6 X132 X136 2.307844 x 15.687570 decreases similarity
#> 7 X132 X136 2.307844 y 82.867368 decreases similarity
#> 8 X132 X136 2.307844 speed -67.196851 increases similarity
#> 9 X132 X136 2.307844 temperature 382.039900 decreases similarity
#> 10 X132 X136 2.307844 heading -104.245839 increases similarity
#> 11 X132 X153 2.306471 x 467.261463 decreases similarity
#> 12 X132 X153 2.306471 y 159.727491 decreases similarity
#> 13 X132 X153 2.306471 speed -44.549191 increases similarity
#> 14 X132 X153 2.306471 temperature -4.016121 increases similarity
#> 15 X132 X153 2.306471 heading -88.852346 increases similarity
#> 16 X134 X136 2.206298 x 36.205194 decreases similarity
#> 17 X134 X136 2.206298 y 90.757712 decreases similarity
#> 18 X134 X136 2.206298 speed -61.923595 increases similarity
#> 19 X134 X136 2.206298 temperature 348.244258 decreases similarity
#> 20 X134 X136 2.206298 heading -96.737145 increases similarity
#> 21 X134 X153 2.115504 x 761.132445 decreases similarity
#> 22 X134 X153 2.115504 y 26.329542 decreases similarity
#> 23 X134 X153 2.115504 speed -62.801312 increases similarity
#> 24 X134 X153 2.115504 temperature -23.264433 increases similarity
#> 25 X134 X153 2.115504 heading -76.874072 increases similarity
#> 26 X136 X153 2.686551 x 530.402462 decreases similarity
#> 27 X136 X153 2.686551 y 24.217594 decreases similarity
#> 28 X136 X153 2.686551 speed -67.659029 increases similarity
#> 29 X136 X153 2.686551 temperature 255.711643 decreases similarity
#> 30 X136 X153 2.686551 heading -119.877355 increases similarity
#> psi_difference psi_without psi_only_with distance diagonal bandwidth
#> 1 1.04390518 1.111188 2.1550935 euclidean TRUE 1
#> 2 1.17868735 1.092833 2.2715204 euclidean TRUE 1
#> 3 -0.32911184 1.284514 0.9554020 euclidean TRUE 1
#> 4 0.90904774 1.099009 2.0080565 euclidean TRUE 1
#> 5 -0.50779815 1.437155 0.9293573 euclidean TRUE 1
#> 6 0.36204459 2.311210 2.6732545 euclidean TRUE 1
#> 7 1.91244927 2.224086 4.1365352 euclidean TRUE 1
#> 8 -1.55079824 2.760115 1.2093171 euclidean TRUE 1
#> 9 8.81688350 1.406874 10.2237578 euclidean TRUE 1
#> 10 -2.40583096 3.521415 1.1155843 euclidean TRUE 1
#> 11 10.77725027 1.804499 12.5817493 euclidean TRUE 1
#> 12 3.68406831 2.009827 5.6938953 euclidean TRUE 1
#> 13 -1.02751418 2.625354 1.5978393 euclidean TRUE 1
#> 14 -0.09263067 2.327298 2.2346670 euclidean TRUE 1
#> 15 -2.04935361 3.405979 1.3566257 euclidean TRUE 1
#> 16 0.79879461 2.207099 3.0058938 euclidean TRUE 1
#> 17 2.00238595 2.142738 4.1451235 euclidean TRUE 1
#> 18 -1.36621929 2.664565 1.2983462 euclidean TRUE 1
#> 19 7.68330753 1.397224 9.0805312 euclidean TRUE 1
#> 20 -2.13431009 3.282323 1.1480127 euclidean TRUE 1
#> 21 16.10178922 1.392314 17.4941037 euclidean TRUE 1
#> 22 0.55700259 2.064727 2.6217298 euclidean TRUE 1
#> 23 -1.32856443 2.504608 1.1760437 euclidean TRUE 1
#> 24 -0.49216006 2.155119 1.6629588 euclidean TRUE 1
#> 25 -1.62627425 3.037239 1.4109647 euclidean TRUE 1
#> 26 14.24953216 2.359901 16.6094327 euclidean TRUE 1
#> 27 0.65061800 2.672038 3.3226560 euclidean TRUE 1
#> 28 -1.81769426 3.166674 1.3489801 euclidean TRUE 1
#> 29 6.86982348 1.968105 8.8379282 euclidean TRUE 1
#> 30 -3.22056616 4.409902 1.1893363 euclidean TRUE 1
#> lock_step robust
#> 1 FALSE TRUE
#> 2 FALSE TRUE
#> 3 FALSE TRUE
#> 4 FALSE TRUE
#> 5 FALSE TRUE
#> 6 FALSE TRUE
#> 7 FALSE TRUE
#> 8 FALSE TRUE
#> 9 FALSE TRUE
#> 10 FALSE TRUE
#> 11 FALSE TRUE
#> 12 FALSE TRUE
#> 13 FALSE TRUE
#> 14 FALSE TRUE
#> 15 FALSE TRUE
#> 16 FALSE TRUE
#> 17 FALSE TRUE
#> 18 FALSE TRUE
#> 19 FALSE TRUE
#> 20 FALSE TRUE
#> 21 FALSE TRUE
#> 22 FALSE TRUE
#> 23 FALSE TRUE
#> 24 FALSE TRUE
#> 25 FALSE TRUE
#> 26 FALSE TRUE
#> 27 FALSE TRUE
#> 28 FALSE TRUE
#> 29 FALSE TRUE
#> 30 FALSE TRUE
#to wide format
df_wide <- momentum_to_wide(
df = df
)
df_wide
#> x y psi most_similarity most_dissimilarity importance__heading
#> 1 X132 X134 1.163499 heading y -43.64405
#> 2 X132 X136 2.307844 heading temperature -104.24584
#> 3 X132 X153 2.306471 heading x -88.85235
#> 4 X134 X136 2.206298 heading temperature -96.73714
#> 5 X134 X153 2.115504 heading x -76.87407
#> 6 X136 X153 2.686551 heading x -119.87735
#> importance__speed importance__temperature importance__x importance__y
#> 1 -28.28639 78.130508 89.72119 101.30540
#> 2 -67.19685 382.039900 15.68757 82.86737
#> 3 -44.54919 -4.016121 467.26146 159.72749
#> 4 -61.92359 348.244258 36.20519 90.75771
#> 5 -62.80131 -23.264433 761.13244 26.32954
#> 6 -67.65903 255.711643 530.40246 24.21759
#> psi_difference__heading psi_difference__speed psi_difference__temperature
#> 1 -0.5077982 -0.3291118 0.90904774
#> 2 -2.4058310 -1.5507982 8.81688350
#> 3 -2.0493536 -1.0275142 -0.09263067
#> 4 -2.1343101 -1.3662193 7.68330753
#> 5 -1.6262742 -1.3285644 -0.49216006
#> 6 -3.2205662 -1.8176943 6.86982348
#> psi_difference__x psi_difference__y psi_only_with__heading
#> 1 1.0439052 1.1786874 0.9293573
#> 2 0.3620446 1.9124493 1.1155843
#> 3 10.7772503 3.6840683 1.3566257
#> 4 0.7987946 2.0023859 1.1480127
#> 5 16.1017892 0.5570026 1.4109647
#> 6 14.2495322 0.6506180 1.1893363
#> psi_only_with__speed psi_only_with__temperature psi_only_with__x
#> 1 0.955402 2.008057 2.155094
#> 2 1.209317 10.223758 2.673254
#> 3 1.597839 2.234667 12.581749
#> 4 1.298346 9.080531 3.005894
#> 5 1.176044 1.662959 17.494104
#> 6 1.348980 8.837928 16.609433
#> psi_only_with__y psi_without__heading psi_without__speed
#> 1 2.271520 1.437155 1.284514
#> 2 4.136535 3.521415 2.760115
#> 3 5.693895 3.405979 2.625354
#> 4 4.145123 3.282323 2.664565
#> 5 2.621730 3.037239 2.504608
#> 6 3.322656 4.409902 3.166674
#> psi_without__temperature psi_without__x psi_without__y
#> 1 1.099009 1.111188 1.092833
#> 2 1.406874 2.311210 2.224086
#> 3 2.327298 1.804499 2.009827
#> 4 1.397224 2.207099 2.142738
#> 5 2.155119 1.392314 2.064727
#> 6 1.968105 2.359901 2.672038