Score new samples using similarity methods
# S3 method for apd_similarity score(object, new_data, type = "numeric", add_percentile = TRUE, ...)
object | A |
---|---|
new_data | A data frame or matrix of new predictors. |
type | A single character. The type of predictions to generate. Valid options are:
|
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. |
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# }