dilp_outliers() will typically only be called internally by dilp().
However, it can be used on its own to locate specimens that may have been
misreported or measured incorrectly. dilp_outliers() returns a data frame
listing specimens that have unusually high or low values for the four key
parameters used in DiLP analyses. If flagged, it may be worth taking a look at the
raw measurements and evaluating if the specimen should be used.
Arguments
- specimen_data
Processed specimen level leaf physiognomic data. The structure should match the structure of the output from
dilp_processing()
Value
A 4 by X data frame. Each row represents one of the DiLP parameters, and the specimens that are outliers for that parameter.
Examples
# Check for outliers in the provided McAbeeExample dataset. Each
# of these outliers has been manually re-examined and was found acceptable.
dilp_dataset <- dilp_processing(McAbeeExample)
dilp_outliers <- dilp_outliers(dilp_dataset)
dilp_outliers
#> Variable Outlier1 Outlier2 Outlier3 Outlier4
#> 1 fdr <NA> <NA> <NA> <NA>
#> 2 tc_ip BU-712-1117 BU-712-1169A BU-712-1176A <NA>
#> 3 leaf_area BU-712-2173A BU-712-2105A BU-712-2124 <NA>
#> 4 perimeter_ratio M-2015-1-1 BU-712-1073A BU-712-1165 M-2015-1-62