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 × 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
# }