Conduct statistical testing on time-series feature classification performance to identify top features or compare entire sets

Usage

compare_features(
  data,
  metric = c("accuracy", "precision", "recall", "f1"),
  by_set = TRUE,
  hypothesis = c("null", "pairwise"),
  p_adj = c("none", "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr")
)

Arguments

data: list object containing the classification outputs produce by tsfeature_classifier
metric: character denoting the classification performance metric to use in statistical testing. Can be one of "accuracy", "precision", "recall", "f1". Defaults to "accuracy"
by_set: Boolean specifying whether you want to compare feature sets (if TRUE) or individual features (if FALSE). Defaults to TRUE but this is contingent on whether you computed by set or not in tsfeature_classifier
hypothesis: character denoting whether p-values should be calculated for each feature set or feature (depending on by_set argument) individually relative to the null if use_null = TRUE in tsfeature_classifier through "null", or whether pairwise comparisons between each set or feature should be conducted on main model fits only through "pairwise". Defaults to "null"
p_adj: character denoting the adjustment made to p-values for multiple comparisons. Should be a valid argument to stats::p.adjust. Defaults to "none" for no adjustment. "holm" is recommended as a starting point for adjustments

Value

data.frame containing the results

References

Henderson, T., Bryant, A. G., and Fulcher, B. D. Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification. 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, (2023).

Author

Trent Henderson

Examples


library(theft)

features <- theft::calculate_features(theft::simData,
  group_var = "process",
  feature_set = NULL,
  features = list("mean" = mean, "sd" = sd))
#> No IDs removed. All value vectors good for feature extraction.
#> Running computations for user-supplied features...
#> 
#> Calculations completed for user-supplied features.

classifiers <- classify(features,
                        by_set = FALSE,
                        n_resamples = 3)
#> Only one set of 'catch22', 'feasts', 'tsfeatures', or 'Kats' with potential duplicates is in your feature data. Exiting and returning original input data.
#> Fitting model 1/6
#> Fitting model 2/6
#> Fitting model 3/6
#> Fitting model 4/6
#> Fitting model 5/6
#> Fitting model 6/6

compare_features(classifiers,
                 by_set = FALSE,
                 hypothesis = "pairwise")
#> Calculating comparison 1/1
#>             hypothesis   names_a names_b   metric names_a_mean names_b_mean
#> 1 User_mean != User_sd User_mean User_sd accuracy    0.1851852    0.6148148
#>   t_statistic  p.value
#> 1   -8.043153 1.984892