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

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", "qt", "quantile"),
  interval = NULL,
  model_type = c("main", "null")
)

calculate_interval(
  data,
  metric = c("accuracy", "precision", "recall", "f1"),
  by_set = TRUE,
  type = c("sd", "qt", "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 "qt", 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 = "qt" 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

data.frame containing the results

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

interval(classifiers,
  by_set = FALSE,
  type = "sd",
  interval = 1)
#>       names     .mean    .lower    .upper
#> 1 User_mean 0.1851852 0.1723552 0.1980152
#> 2   User_sd 0.6148148 0.5634948 0.6661348