extract

[.BinomialVariate

[,DiscreteVariate,ANY,missing,missing-method

[.ModelFrame

[,ModelFrame,ANY,ANY,ANY-method

[,ModelFrame,ANY,missing,ANY-method

[,ModelFrame,missing,missing,ANY-method

[,RecipeGrid,ANY,ANY,ANY-method

[,Resamples,ANY,ANY,ANY-method

[,Resamples,ANY,missing,ANY-method

[,Resamples,missing,missing,ANY-method

[,SurvMatrix,ANY,ANY,ANY-method

object from which to extract elements.

indices specifying elements to extract.

i, j, ...

logical indicating that the result be returned as an object
coerced to the lowest dimension possible if <code>TRUE</code> or
with the original dimensions and class otherwise.

drop

Operators acting on data structures to extract elements.

Meta-package for statistical and machine learning with a unified
interface for model fitting, prediction, performance assessment, and
presentation of results.  Approaches for model fitting and prediction of
numerical, categorical, or censored time-to-event outcomes include
traditional regression models, regularization methods, tree-based methods,
support vector machines, neural networks, ensembles, data preprocessing,
filtering, and model tuning and selection.  Performance metrics are provided
for model assessment and can be estimated with independent test sets, split
sampling, cross-validation, or bootstrap resampling.  Resample estimation
can be executed in parallel for faster processing and nested in cases of
model tuning and selection.  Modeling results can be summarized with
descriptive statistics; calibration curves; variable importance; partial
dependence plots; confusion matrices; and ROC, lift, and other performance
curves.

extract: Extract Elements of an Object

Description

Usage

Arguments