Predict from a ww_area_of_applicability

Predict from a ww_area_of_applicability

Usage

# S3 method for class 'ww_area_of_applicability'
predict(object, new_data, ...)

Arguments

object

A ww_area_of_applicability object.

new_data

A data frame or matrix of new samples.

...

Not used.

Value

A tibble of predictions, with two columns: di, numeric, contains the "dissimilarity index" of each point in new_data, while aoa, logical, contains whether a row is inside (TRUE) or outside (FALSE) the area of applicability.

Note that this function is often called using terra::predict(), in which case aoa will be converted to numeric implicitly; 1 values correspond to cells "inside" the area of applicability and 0 corresponds to cells "outside" the AOA.

The number of rows in the tibble is guaranteed to be the same as the number of rows in new_data. Rows with NA predictor values will have NA di and aoa values.

Details

The function computes the distance indices of the new data and whether or not they are "inside" the area of applicability.

See also

Other area of applicability functions: ww_area_of_applicability()

Examples

library(vip)
#> 
#> Attaching package: ‘vip’
#> The following object is masked from ‘package:utils’:
#> 
#>     vi
train <- gen_friedman(1000, seed = 101) # ?vip::gen_friedman
test <- train[701:1000, ]
train <- train[1:700, ]
pp <- stats::ppr(y ~ ., data = train, nterms = 11)
metric_name <- ifelse(
  packageVersion("vip") > package_version("0.3.2"),
  "rsq",
  "rsquared"
)

importance <- vip::vi_permute(
  pp,
  target = "y",
  metric = metric_name,
  pred_wrapper = predict,
  train = train
)

aoa <- ww_area_of_applicability(y ~ ., train, test, importance = importance)
predict(aoa, test)
#> # A tibble: 300 × 2
#>        di aoa  
#>     <dbl> <lgl>
#>  1 0.175  TRUE 
#>  2 0.191  TRUE 
#>  3 0.190  TRUE 
#>  4 0.0802 TRUE 
#>  5 0.141  TRUE 
#>  6 0.287  TRUE 
#>  7 0.178  TRUE 
#>  8 0.205  TRUE 
#>  9 0.134  TRUE 
#> 10 0.177  TRUE 
#> # ℹ 290 more rows