ef: Implementation of enrichment factor

In zamanianlab/ZamanianLabVSTools: A Set of Tools and Metrics for Analyzing and Evaluating Virtual Screening Data

Implementation of enrichment factor

Description

Calculates a metric often used in virtual screening - enrichment factor.

Usage

ef(data, ...)

## S3 method for class 'data.frame'
ef(
  data,
  truth,
  ...,
  cutoff = 0.01,
  estimator = NULL,
  na_rm = TRUE,
  event_level = "first"
)

ef_vec(
  truth,
  estimate,
  cutoff = 0.01,
  estimator = NULL,
  event_level = "first",
  na_rm = TRUE
)

Arguments

data	A data.frame containing the truth and estimate columns.
...	A set of unquoted column names or one or more dplyr selector functions to choose which variables contain the class probabilities. If truth is binary, only 1 column should be selected. Otherwise, there should be as many columns as factor levels of truth.
truth	The column identifier for the true class results (that is a factor). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a factor vector.
cutoff	The percent value for enrichment (defaults to 0.01 or 1%)
estimator	One of "binary", "hand_till", "macro", or "macro_weighted" to specify the type of averaging to be done. "binary" is only relevant for the two class case. The others are general methods for calculating multiclass metrics. The default will automatically choose "binary" or "hand_till" based on truth.
na_rm	A logical value indicating whether NA values should be stripped before the computation proceeds.
event_level	A single string. Either "first" or "second" to specify which level of truth to consider as the "event". This argument is only applicable when estimator = "binary". The default uses an internal helper that generally defaults to "first", however, if the deprecated global option yardstick.event_first is set, that will be used instead with a warning.

Value

A tibble with columns .metric, .estimator, and .estimate and 1 row of values.

For grouped data frames, the number of rows returned will be the same as the number of groups.

For ef_vec(), a single numeric value (or NA).

See Also

bedroc() for computing the Boltzmann-enhanced discrimination of ROC and rie() for computing the Robust Initial Enhancement.

zamanianlab/ZamanianLabVSTools documentation built on April 16, 2022, 12:53 a.m.