Description Usage Arguments Details Value Author(s) References See Also Examples
Create a new dataset with smoothed classes and nMC observations per class.
1 | smooth.data(target, predictors, nMC = 100)
|
target |
Vector or target Column with names of classes. |
predictors |
Dataframe or matrix with predictor variables. |
nMC |
Number of smoothed observations returned per class (default = 100) |
For each class, the function will shuffle the rows of each predictor separately and extract nMC rows for each class. By doing so we generate nMC in silico observations for each class, but mantaining the range (observed variability) for each predictor.
Dataframe with nMC smoothed observations. First column named 'class' contains the target classes, predictor names are mantained.
Pedro Martinez Arbizu & Sven Rossel
Rossel, S. & P. Martinez Arbizu (2018) Automatic specimen identification of Harpacticoids (Crustacea:Copepoda) using Random Forest and MALDIāTOF mass spectra, including a post hoc test for false positive discovery. Methods in Ecology and Evolution, 9(6):1421-1434.
https://doi.org/10.1111/2041-210X.13000
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