Score new samples using similarity methods

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



A apd_similarity object.


A data frame or matrix of new predictors.


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.


A single logical; should the percentile of the similarity score relative to the training set values by computed?


Not used, but required for extensibility.


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.


# \donttest{ data(qsar_binary) jacc_sim <- apd_similarity(binary_tr) mean_sim <- score(jacc_sim, new_data = binary_unk) mean_sim
#> # A tibble: 5 x 2 #> similarity similarity_pctl #> <dbl> <dbl> #> 1 0.376 49.8 #> 2 0.284 13.5 #> 3 0.218 6.46 #> 4 0.452 100 #> 5 0.0971 5.59
# }