prob_metrics_sf | R Documentation |
sf
objectstidysdm
provides specialised metrics for SDMs, which have their own help
pages(boyce_cont()
, kap_max()
, and tss_max()
). Additionally, it also
provides methods to handle sf::sf objects for the following standard
yardstick
metrics:
yardstick::average_precision()
yardstick::brier_class()
yardstick::classification_cost()
yardstick::gain_capture()
yardstick::mn_log_loss()
yardstick::pr_auc()
yardstick::roc_auc()
yardstick::roc_aunp()
yardstick::roc_aunu()
## S3 method for class 'sf'
average_precision(data, ...)
## S3 method for class 'sf'
brier_class(data, ...)
## S3 method for class 'sf'
classification_cost(data, ...)
## S3 method for class 'sf'
gain_capture(data, ...)
## S3 method for class 'sf'
mn_log_loss(data, ...)
## S3 method for class 'sf'
pr_auc(data, ...)
## S3 method for class 'sf'
roc_auc(data, ...)
## S3 method for class 'sf'
roc_aunp(data, ...)
## S3 method for class 'sf'
roc_aunu(data, ...)
data |
an sf::sf object |
... |
any other parameters to pass to the |
Note that roc_aunp
and roc_aunu
are multiclass metrics, and as such are
are not relevant for SDMs (which work on a binary response). They are
included for completeness, so that all class probability metrics from
yardstick
have an sf
method, for applications other than SDMs.
A tibble with columns .metric
, .estimator
, and .estimate
and 1
row of values.
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