score.apd_similarity: Score new samples using similarity methods

In applicable: A Compilation of Applicability Domain Methods

Score new samples using similarity methods

Description

Score new samples using similarity methods

Usage

## S3 method for class 'apd_similarity'
score(object, new_data, type = "numeric", add_percentile = TRUE, ...)

Arguments

object	A apd_similarity object.
new_data	A data frame or matrix of new predictors.
type	A single character. The type of predictions to generate. Valid options are: "numeric" for a numeric value that summarizes the similarity values for each sample across the training set.
add_percentile	A single logical; should the percentile of the similarity score relative to the training set values by computed?
...	Not used, but required for extensibility.

Value

A tibble of predictions. The number of rows in the tibble is guaranteed to be the same as the number of rows in new_data. For type = "numeric", the tibble contains a column called "similarity". If add_percentile = TRUE, an additional column called similarity_pctl will be added. These values are in percent units so that a value of 11.5 indicates that, in the training set, 11.5 percent of the training set samples had smaller values than the sample being scored.

Examples

data(qsar_binary)

jacc_sim <- apd_similarity(binary_tr)

mean_sim <- score(jacc_sim, new_data = binary_unk)
mean_sim

applicable documentation built on May 29, 2024, 6:34 a.m.