Calculate interval summaries with a measure of central tendency of classification results

Usage

interval(
  data,
  metric = c("accuracy", "precision", "recall", "f1"),
  by_set = TRUE,
  type = c("sd", "se", "quantile"),
  interval = NULL,
  model_type = c("main", "null")
)

calculate_interval(
  data,
  metric = c("accuracy", "precision", "recall", "f1"),
  by_set = TRUE,
  type = c("sd", "se", "quantile"),
  interval = NULL,
  model_type = c("main", "null")
)

Arguments

data: list object containing the classification outputs produce by tsfeature_classifier
metric: character denoting the classification performance metric to calculate intervals for. Can be one of "accuracy", "precision", "recall", "f1". Defaults to "accuracy"
by_set: Boolean specifying whether to compute intervals for each feature set. Defaults to TRUE. If FALSE, the function will instead calculate intervals for each feature
type: character denoting whether to calculate a +/- SD interval with "sd", confidence interval based off the t-distribution with "se", or based on a quantile with "quantile". Defaults to "sd"
interval: numeric scalar denoting the width of the interval to calculate. Defaults to 1 if type = "sd" to produce a +/- 1 SD interval. Defaults to 0.95 if type = "se" or type = "quantile" for a 95 per cent interval
model_type: character denoting whether to calculate intervals for main models with "main" or null models with "null" if the use_null argument when using tsfeature_classifier was use_null = TRUE. Defaults to "main"

Value

interval_calculations object which is a data frame containing the results

Author

Trent Henderson

Examples


library(theft)

features <- theft::calculate_features(theft::simData,
  feature_set = NULL,
  features = list("mean" = mean, "sd" = sd))
#> Running computations 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.
#> Generating resampled 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

interval(classifiers,
  by_set = FALSE,
  type = "sd",
  interval = 1)
#>       names     .mean    .lower    .upper
#> 1 User_mean 0.1259259 0.1130959 0.1387559
#> 2   User_sd 0.5629630 0.5501330 0.5757930