Score new samples using similarity methods

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

## Arguments

object 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.

## 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

# \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# }