cleanRFdata: Clean Random Forest Input Data
In rfPermute: Estimate Permutation p-Values for Random Forest Importance Metrics
cleanRFdata R Documentation
Clean Random Forest Input Data
Description
Removes cases for a Random Forest classification model
with missing data and predictors that are constant.
Usage
cleanRFdata(x, y, data, max.levels = 30)
Arguments
x
columns used as predictor variables as character or numeric vector.
y
column used as response variable as character or numeric.
data
data.frame containing x and y columns.
max.levels
maximum number of levels in response variable y.
Value
a data.frame containing cleaned data.
Author(s)
Eric Archer eric.archer@noaa.gov
rfPermute documentation built on Aug. 24, 2023, 1:08 a.m.
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| cleanRFdata | R Documentation |
Clean Random Forest Input Data
Description
Removes cases for a Random Forest classification model with missing data and predictors that are constant.
Usage
cleanRFdata(x, y, data, max.levels = 30)
Arguments
x |
columns used as predictor variables as character or numeric vector. |
y |
column used as response variable as character or numeric. |
data |
data.frame containing |
max.levels |
maximum number of levels in response variable |
Value
a data.frame containing cleaned data.
Author(s)
Eric Archer eric.archer@noaa.gov
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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