CornerstoneR: randomForest – R documentation

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randomForest

Random Forest

Description

Random Forest via ranger. Predicts response variables or brushed set of rows from predictor variables, using Random Forest classification or regression.

Usage

randomForest(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  brush = cs.in.brushed(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`resps`	[`character`] Character vector of response variables.
`brush`	[`logical`] Logical vector of length `nrow(dataset)`. Flags brushed rows in Cornerstone.
`scriptvars`	[`list`] Named list of script variables set via the Cornerstone "Script Variables" menu. For details see below.
`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.

Details

The following script variables are summarized in scriptvars list:

brush.pred: [logical(1)]
Use brush vector as additional predictor.
Default is FALSE.
use.rows: [character(1)]
Rows to use in model fit. Possible values are all, non-brushed, or brushed.
Default is all.
num.trees: [integer(1)]
Number of trees to fit in ranger.
Default is 500.
importance.mode: [character(1)]
Variable importance mode. For details see ranger.
Default is permutation.
respect.unordered.factors: [character(1)]
Handling of unordered factor covariates. For details see ranger.
Default is NULL.

Value

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

`statistics`	General statistics about the random forest.
`importances`	Variable importance of prediction variables in descending order of importance (most important first)
`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 ...`.
`confusion`	For categorical response variables or brush state only. A table with counts of each distinct combination of predicted and actual values.
`rgobjects`	List of `ranger.forest` objects with fitted random forests.

Examples

# Fit random forest to iris data:
res = randomForest(iris, c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"), "Species"
                   , scriptvars = list(brush.pred = FALSE, use.rows = "all", num.trees = 500
                                       , importance.mode = "permutation"
                                       , respect.unordered.factors = "ignore"
                                       )
                   , brush = rep(FALSE, nrow(iris)), return.results = TRUE
                   )
# Show general statistics:
res$statistics
# Prediction
randomForestPredict(iris[, 1:4], c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
                    , robject = res$rgobjects
                    , return.results = TRUE
                    )

CornerstoneR

Collection of Scripts for Interface Between 'Cornerstone' and 'R'

v2.0.2

GPL-3

Authors

Gerrith Djaja [aut, cre], Dirk Surmann [aut] (<https://orcid.org/0000-0003-0873-137X>)

Initial release