Aggregate Dissimilarity Data Frames Across Parameter Combinations

The functions distantia() and momentum() allow dissimilarity assessments based on several combinations of arguments at once. For example, when the argument distance is set to c("euclidean", "manhattan"), the output data frame will show two dissimilarity scores for each pair of compared time series, one based on euclidean distances, and another based on manhattan distances.

The functions distantia_aggregate() and momentum_aggregate() compute the stats of dissimilarity metrics across combinations of parameters.

When df is the result of distantia(), the input data is grouped by pairs of time series, and the function f is applied to the column "psi" by group

When df is the result of momentum(), the input data is grouped by pairs of time series and variables, and the function f is applied to the columns "importance", "psi_only_with" and "psi_without" by group. However, if the values TRUE and FALSE appear in the column "robust" (which is not allowed by default in momentum()), then the aggregation is cancelled with an error, as the results of both methods should not be aggregated together.

If psi scores smaller than zero occur in the aggregated output, then the the smaller psi value is added to the column psi to start dissimilarity scores at zero.

If there are no different combinations of arguments in the input data frame, no aggregation happens, but all parameter columns are removed.

Usage

distantia_aggregate(df = NULL, f = mean, ...)

momentum_aggregate(df = NULL, f = mean, ...)

Arguments

df

(required, data frame) Output of distantia() or momentum(). Default: NULL

f

(optional, function) Function to summarize psi scores (for example, mean) when there are several combinations of parameters in df. Ignored when there is a single combination of arguments in the input. Default: mean

...

(optional, arguments of f) Further arguments to pass to the function f.

Value

data frame

See also

Other dissimilarity_analysis: distantia_boxplot(), distantia_cluster_hclust(), distantia_cluster_kmeans(), distantia_matrix(), distantia_plot(), distantia_spatial_network(), distantia_stats(), distantia_time_shift(), momentum_boxplot(), momentum_stats(), momentum_to_wide()

Examples

#three time series
#climate and ndvi in Fagus sylvatica stands in Spain, Germany, and Sweden
tsl <- tsl_initialize(
  x = fagus_dynamics,
  name_column = "name",
  time_column = "time"
) |>
  tsl_transform(
    f = f_scale_global
  )

if(interactive()){
  tsl_plot(
    tsl = tsl,
    guide_columns = 3
    )
}

#distantia with multiple parameter combinations
#-------------------------------------
df <- distantia(
  tsl = tsl,
  distance = c("euclidean", "manhattan"),
  lock_step = TRUE
)

df[, c(
  "x",
  "y",
  "distance",
  "psi"
)]
#>         x      y  distance       psi
#> 2 Germany Sweden euclidean 0.8576700
#> 5 Germany Sweden manhattan 0.8591195
#> 4 Germany  Spain manhattan 1.2698922
#> 1 Germany  Spain euclidean 1.3061327
#> 3   Spain Sweden euclidean 1.4708497
#> 6   Spain Sweden manhattan 1.4890286

#aggregation using means
df <- distantia_aggregate(
  df = df,
  f = mean
)

df
#>         x      y       psi
#> 1 Germany  Spain 1.2880125
#> 2 Germany Sweden 0.8583947
#> 3   Spain Sweden 1.4799391

#momentum with multiple parameter combinations
#-------------------------------------
df <- momentum(
  tsl = tsl,
  distance = c("euclidean", "manhattan"),
  lock_step = TRUE
)

df[, c(
  "x",
  "y",
  "variable",
  "distance",
  "importance"
)]
#>          x      y    variable  distance importance
#> 1  Germany  Spain         evi euclidean   0.241829
#> 2  Germany  Spain    rainfall euclidean  19.051052
#> 3  Germany  Spain temperature euclidean -30.814944
#> 4  Germany Sweden         evi euclidean  28.539736
#> 5  Germany Sweden    rainfall euclidean  -4.845232
#> 6  Germany Sweden temperature euclidean -25.011608
#> 7    Spain Sweden         evi euclidean -22.912397
#> 8    Spain Sweden    rainfall euclidean   9.732110
#> 9    Spain Sweden temperature euclidean  12.501949
#> 10 Germany  Spain         evi manhattan   1.900935
#> 11 Germany  Spain    rainfall manhattan  21.154702
#> 12 Germany  Spain temperature manhattan -29.727709
#> 13 Germany Sweden         evi manhattan  29.249949
#> 14 Germany Sweden    rainfall manhattan  -3.444891
#> 15 Germany Sweden temperature manhattan -26.684626
#> 16   Spain Sweden         evi manhattan -25.080233
#> 17   Spain Sweden    rainfall manhattan  10.764640
#> 18   Spain Sweden temperature manhattan  12.742187

#aggregation using means
df <- momentum_aggregate(
  df = df,
  f = mean
)

df[, c(
  "x",
  "y",
  "variable",
  "importance"
)]
#>         x      y    variable importance
#> 1 Germany  Spain         evi   1.071382
#> 2 Germany Sweden         evi  28.894843
#> 3   Spain Sweden         evi -23.996315
#> 4 Germany  Spain    rainfall  20.102877
#> 5 Germany Sweden    rainfall  -4.145061
#> 6   Spain Sweden    rainfall  10.248375
#> 7 Germany  Spain temperature -30.271327
#> 8 Germany Sweden temperature -25.848117
#> 9   Spain Sweden temperature  12.622068