The ggRandomForests package extracts tidy data objects from either `randomForestSRC` or `randomForest` fits and feeds them into familiar `ggplot2` workflows. This vignette highlights the most common objects— `gg_error`, `gg_variable`, and `gg_vimp`—along with a small helper for building balanced conditioning intervals.

## Error trajectories with `gg_error()`

    library(randomForest)
    set.seed(42)
    rf_iris <- randomForest(Species ~ ., data = iris, ntree = 200, keep.forest = TRUE)
    err_df <- ggRandomForests::gg_error(rf_iris, training = TRUE)
    head(err_df)

    <gg_error>  from randomForest  |  family: classification  |  ntree: 200  |  n: 150

The `gg_error()` object stores the cumulative OOB error rate for each outcome column plus the `ntree` counter. When `training = TRUE`, the function reconstructs the original model frame and appends the in-bag error trajectory (`train`). Plotting overlays both curves by default:

    plot(err_df)

## Marginal dependence via `gg_variable()`

    set.seed(99)
    boston <- MASS::Boston
    rf_boston <- randomForest(medv ~ ., data = boston, ntree = 150)
    var_df <- ggRandomForests::gg_variable(rf_boston)
    str(var_df[, c("lstat", "yhat")])

    Classes 'gg_variable', 'regression' and 'data.frame':   506 obs. of  2 variables:
     $ lstat: num  4.98 9.14 4.03 2.94 5.33 ...
     $ yhat : num  29.2 22.5 35.1 36.4 33.4 ...

Because the original training data are recovered from the model call, `gg_variable()` works even when the forest was trained within helper functions or against a `subset()` expression. The output keeps the raw predictors plus either a continuous `yhat` column (regression) or per-class probabilities (`yhat.<class>` for classification). Plotting a single variable is straightforward:

    plot(var_df, xvar = "lstat")

    `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Survival forests can request multiple horizons using the `time` argument; non-OOB predictions are available by setting `oob = FALSE`.

## Variable importance with `gg_vimp()`

    vimp_df <- ggRandomForests::gg_vimp(rf_boston)
    head(vimp_df)

    <gg_vimp>  from randomForest  |  family: regression  |  ntree: 150  |  n: 506  |  variables: 6

    plot(vimp_df)

If a `randomForest` object lacks stored importance scores, `gg_vimp()` tries to compute them on the fly. When the forest truly cannot provide the information (for example when `importance = FALSE` and the predictors are no longer accessible), the function emits a warning and returns `NA` placeholders so plots still render.

## Balanced conditioning cuts with `quantile_pts()`

    rm_breaks <- ggRandomForests::quantile_pts(boston$rm, groups = 6, intervals = TRUE)
    rm_groups <- cut(boston$rm, breaks = rm_breaks)
    table(rm_groups)

    rm_groups
    (3.56,5.76] (5.76,5.99] (5.99,6.21] (6.21,6.44] (6.44,6.85] (6.85,8.78]
             85          84          84          85          84          84

The helper wraps `stats::quantile()` to produce evenly populated strata that drop directly into `cut()` when building coplots or facet labels.

## Next steps

- Inspect the full API reference at https://ehrlinger.github.io/ggRandomForests/.
- Use `?gg_error`, `?gg_variable`, `?gg_vimp`, and `?quantile_pts` for additional arguments and examples.
- Pair these data objects with your own `ggplot2` themes to align with your preferred publication style.