randomForestPredict: Random Forest Prediction
In CornerstoneR: Collection of Scripts for Interface Between 'Cornerstone' and 'R'

Description Usage Arguments Value See Also

Random Forest prediction via predict.ranger. Predicts response variables from predictor variables, using ranger objects. All ranger objects have to work on the same set of prediction variables. These variables are exactly available in the prediction dataset. A response is not necessary, it will be predicted via this function.

randomForestPredict(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  robject = cs.in.Robject(),
  return.results = FALSE,
  ...
)

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`robject`	[`list`] Named list of `ranger` object(s) set via Cornerstone menu "Input R Objects".
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.
`...`	[ANY] Additional arguments to be passed to `ranger` . Please consider possible script variables (`scriptvars`) to prevent duplicates.

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame objects:

predictions

Dataset to brush with predicted values for dataset. The original input and other columns can be added to this dataset through the menu Columns -> Add from Parent ....

randomForest

CornerstoneR documentation built on Aug. 28, 2020, 5:09 p.m.