The True Skills Statistic, which is defined as
Usage
tss(data, ...)
# S3 method for class 'data.frame'
tss(
data,
truth,
estimate,
estimator = NULL,
na_rm = TRUE,
case_weights = NULL,
event_level = "first",
...
)Arguments
- data
Either a data.frame containing the columns specified by the truth and estimate arguments, or a table/matrix where the true class results should be in the columns of the table.
- ...
Not currently used.
- truth
The column identifier for the true class results (that is a factor). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a factor vector.
- estimate
The column identifier for the predicted class results (that is also factor). As with truth this can be specified different ways but the primary method is to use an unquoted variable name. For _vec() functions, a factor vector.
- estimator
One of: "binary", "macro", "macro_weighted", or "micro" to specify the type of averaging to be done. "binary" is only relevant for the two class case. The other three are general methods for calculating multiclass metrics. The default will automatically choose "binary" or "macro" based on estimate.
- na_rm
A logical value indicating whether NA values should be stripped before the computation proceeds.
- case_weights
The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector.
- event_level
A single string. Either "first" or "second" to specify which level of truth to consider as the "event". This argument is only applicable when estimator = "binary". The default is "first".
Value
A tibble with columns .metric, .estimator, and .estimate and 1 row of values. For grouped data frames, the number of rows returned will be the same as the number of groups.
Details
sensitivity+specificity +1
This function is a wrapper around yardstick::j_index(), another name for the
same quantity. Note that this function takes the classes as predicted by the
model without any calibration (i.e. making a split at 0.5 probability). This
is usually not the metric used for Species Distribution Models, where the
threshold is recalibrated to maximise TSS; for that purpose, use tss_max().
Examples
# Two class
data("two_class_example")
tss(two_class_example, truth, predicted)
#> # A tibble: 1 × 3
#> .metric .estimator .estimate
#> <chr> <chr> <dbl>
#> 1 tss binary 0.673
# Multiclass
library(dplyr)
data(hpc_cv)
# Groups are respected
hpc_cv %>%
group_by(Resample) %>%
tss(obs, pred)
#> # A tibble: 10 × 4
#> Resample .metric .estimator .estimate
#> <chr> <chr> <chr> <dbl>
#> 1 Fold01 tss macro 0.434
#> 2 Fold02 tss macro 0.422
#> 3 Fold03 tss macro 0.533
#> 4 Fold04 tss macro 0.449
#> 5 Fold05 tss macro 0.431
#> 6 Fold06 tss macro 0.413
#> 7 Fold07 tss macro 0.398
#> 8 Fold08 tss macro 0.468
#> 9 Fold09 tss macro 0.435
#> 10 Fold10 tss macro 0.412