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

Description Usage Arguments Details Value See Also Examples

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

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

`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.

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.

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.

randomForestPredict

# 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
                    )