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Predicted response data object

Source: R/gg_rfsrc.R
gg_rfsrc.rfsrc.Rd

Extracts the predicted response values from the rfsrc object, and formats data for plotting the response using plot.gg_rfsrc.

Usage

# S3 method for class 'rfsrc'
gg_rfsrc(object, oob = TRUE, by, ...)

Arguments

object

A fitted rfsrc or randomForest object.

oob

Logical; if TRUE (default) return out-of-bag predictions. Set to FALSE to use full in-bag (training) predictions. Forced to FALSE automatically for predict.rfsrc objects, which carry no OOB estimates.

by

Optional stratifying variable. Either a character column name present in the training data, or a vector/factor of the same length as the training set. When supplied, a group column is added to the returned data and bootstrap CI bands (survival) are computed per group. Omit or leave missing to return an unstratified result.

...

Additional arguments controlling output for specific forest families:

surv_type

Character; one of "surv" (default), "chf", or "mortality" for survival forests.

conf.int

Numeric coverage probability (e.g. 0.95) to request bootstrap pointwise confidence bands for survival forests. Triggers wide-format output with lower, upper, median, and mean columns.

bs.sample

Integer; number of bootstrap resamples when conf.int is set. Defaults to the number of observations.

Value

A gg_rfsrc object (a classed data.frame) whose structure depends on the forest family:

regression

Columns yhat and the response name; optionally a group column when by is supplied.

classification

One column per class with predicted probabilities; a y column with observed class labels; optionally group.

survival (no CI / grouping)

Long-format with columns variable (event time), value (survival probability), obs_id, and event.

survival (with conf.int or by)

Wide-format with pointwise bootstrap CI columns (lower, upper, median, mean) per time point; a group column when by is supplied.

The object carries class attributes for the forest family so that plot.gg_rfsrc dispatches correctly.

Details

For survival forests, use the surv_type argument ("surv", "chf", or "mortality") to select the predicted quantity. Bootstrap confidence bands are requested by passing conf.int (e.g. conf.int = 0.95); the number of resamples is controlled by bs.sample.

See also

plot.gg_rfsrc, rfsrc, gg_survival

Examples

## ------------------------------------------------------------
## classification example (small, runs on CRAN)
## ------------------------------------------------------------
## -------- iris data
set.seed(42)
rfsrc_iris <- rfsrc(Species ~ ., data = iris, ntree = 50)
gg_dta <- gg_rfsrc(rfsrc_iris)
plot(gg_dta)


# \donttest{
## ------------------------------------------------------------
## Additional regression / survival examples are guarded with
## \donttest because the cumulative example time exceeds the
## 10-second CRAN budget. Run locally with `R CMD check --run-donttest`
## (or `devtools::check(run_dont_test = TRUE)`) to exercise them.
## ------------------------------------------------------------

## -------- air quality data (regression)
rfsrc_airq <- rfsrc(Ozone ~ ., data = airquality,
                    na.action = "na.impute", ntree = 50)
plot(gg_rfsrc(rfsrc_airq))


## -------- Boston data (rfsrc + randomForest)
if (requireNamespace("MASS", quietly = TRUE)) {
  data(Boston, package = "MASS")
  Boston$chas <- as.logical(Boston$chas)
  rfsrc_boston <- rfsrc(medv ~ ., data = Boston, ntree = 50,
                        forest = TRUE, importance = TRUE,
                        tree.err = TRUE, save.memory = TRUE)
  plot(gg_rfsrc(rfsrc_boston))

  rf_boston <- randomForest::randomForest(medv ~ ., data = Boston,
                                          ntree = 50)
  plot(gg_rfsrc(rf_boston))
}


## -------- mtcars data
rfsrc_mtcars <- rfsrc(mpg ~ ., data = mtcars, ntree = 50)
plot(gg_rfsrc(rfsrc_mtcars))


## -------- veteran data (survival; with CI and group-by)
data(veteran, package = "randomForestSRC")
rfsrc_veteran <- rfsrc(Surv(time, status) ~ ., data = veteran,
                       ntree = 50)
plot(gg_rfsrc(rfsrc_veteran))

plot(gg_rfsrc(rfsrc_veteran, conf.int = .95))

plot(gg_rfsrc(rfsrc_veteran, by = "trt"))

# }