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.

1 2 3 4 5 6 7 8 9 | ```
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` |
[ |

`preds` |
[ |

`resps` |
[ |

`brush` |
[ |

`scriptvars` |
[ |

`return.results` |
[ |

`...` |
[ANY] |

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 |

`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 |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# 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
)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.