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,
...
)
[data.frame
]
Dataset with named columns. The names correspond to predictors and responses.
[character
]
Character vector of predictor variables.
[character
]
Character vector of response variables.
[logical
]
Logical vector of length nrow(dataset)
.
Flags brushed rows in Cornerstone.
[list
]
Named list of script variables set via the Cornerstone "Script Variables" menu.
For details see below.
[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:
General statistics about the random forest.
Variable importance of prediction variables in descending order of importance (most important first)
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 ...
.
For categorical response variables or brush state only. A table with counts of each distinct combination of predicted and actual values.
List of ranger.forest
objects with fitted random forests.
The following script variables are summarized in scriptvars
list:
[logical(1)
]
Use brush
vector as additional predictor.
Default is FALSE
.
[character(1)
]
Rows to use in model fit. Possible values are all
,
non-brushed
, or brushed
.
Default is all
.
[integer(1)
]
Number of trees to fit in ranger
.
Default is 500
.
[character(1)
]
Variable importance mode. For details see
ranger
.
Default is permutation
.
[character(1)
]
Handling of unordered factor covariates. For details see
ranger
.
Default is NULL
.
# 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$statistics
#> Statistic Value
#> <char> <char>
#> 1: Type Classification
#> 2: Number of Trees 500
#> 3: Sample Size 150
#> 4: Number of Independent Variables 4
#> 5: Mtry 2
#> 6: Minimal Node Size 1
#> 7: Variable Importance Mode permutation
#> 8: Splitrule gini
#> 9: OOB Prediction Error [%] 4.667
#> 10: Runtime R Script [s] 0.04491
# Prediction
modelPredict(iris[, 1:4], c("Sepal.Length", "Sepal.Width",
"Petal.Length", "Petal.Width"), robject = res$rgobjects,
scriptvars = list(Output.fmla = FALSE),
return.results = TRUE
)
#> $predictions
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Pred.Species
#> <num> <num> <num> <num> <fctr>
#> 1: 5.1 3.5 1.4 0.2 setosa
#> 2: 4.9 3.0 1.4 0.2 setosa
#> 3: 4.7 3.2 1.3 0.2 setosa
#> 4: 4.6 3.1 1.5 0.2 setosa
#> 5: 5.0 3.6 1.4 0.2 setosa
#> ---
#> 146: 6.7 3.0 5.2 2.3 virginica
#> 147: 6.3 2.5 5.0 1.9 virginica
#> 148: 6.5 3.0 5.2 2.0 virginica
#> 149: 6.2 3.4 5.4 2.3 virginica
#> 150: 5.9 3.0 5.1 1.8 virginica
#>