# Introduction to aorsf

This article covers core features of the `aorsf` package.

    library(aorsf)

## Background

The oblique random forest (RF) is an extension of the traditional (axis-based) RF. Instead of using a single variable to split data and grow new branches, trees in the oblique RF use a weighted combination of multiple variables.

## Oblique RFs for survival, classification, and regression

The purpose of `aorsf` (‘a’ is short for accelerated) is to provide a unifying framework to fit oblique RFs that can scale adequately to large data sets. The fastest algorithms available in the package are used by default because they often have equivalent prediction accuracy to more computational approaches.

The center piece of `aorsf` is the `orsf()` function. In the initial versions of `aorsf`, the `orsf()` function only fit `o`blique `r`andom `s`urvival `f`orests, but now it allows for classification, regression, and survival forests. (I may introduce an `orf()` function in the future if the name `orsf()` is misleading to users.)

For classification, we fit an oblique RF to predict penguin species using `penguin` data from the magnificent `palmerpenguins` R package

    # An oblique classification RF
    penguin_fit <- orsf(data = penguins_orsf, formula = species ~ .)

    penguin_fit
    #> ---------- Oblique random classification forest
    #>
    #>      Linear combinations: Accelerated Logistic regression
    #>           N observations: 333
    #>                N classes: 3
    #>                  N trees: 500
    #>       N predictors total: 7
    #>    N predictors per node: 3
    #>  Average leaves per tree: 5.682
    #> Min observations in leaf: 5
    #>           OOB stat value: 1.00
    #>            OOB stat type: AUC-ROC
    #>      Variable importance: anova
    #>
    #> -----------------------------------------

For regression, we use the same data but predict bill length of penguins:

    # An oblique regression RF
    bill_fit <- orsf(data = penguins_orsf, formula = bill_length_mm ~ .)

    bill_fit
    #> ---------- Oblique random regression forest
    #>
    #>      Linear combinations: Accelerated Linear regression
    #>           N observations: 333
    #>                  N trees: 500
    #>       N predictors total: 7
    #>    N predictors per node: 3
    #>  Average leaves per tree: 50.128
    #> Min observations in leaf: 5
    #>           OOB stat value: 0.82
    #>            OOB stat type: RSQ
    #>      Variable importance: anova
    #>
    #> -----------------------------------------

My personal favorite is the oblique survival RF with accelerated Cox regression because it has a great combination of prediction accuracy and computational efficiency (see JCGS paper). Here, we predict mortality risk following diagnosis of primary biliary cirrhosis:

    # An oblique survival RF
    pbc_fit <- orsf(data = pbc_orsf,
                    n_tree = 5,
                    formula = Surv(time, status) ~ . - id)

    pbc_fit
    #> ---------- Oblique random survival forest
    #>
    #>      Linear combinations: Accelerated Cox regression
    #>           N observations: 276
    #>                 N events: 111
    #>                  N trees: 5
    #>       N predictors total: 17
    #>    N predictors per node: 5
    #>  Average leaves per tree: 20.6
    #> Min observations in leaf: 5
    #>       Min events in leaf: 1
    #>           OOB stat value: 0.81
    #>            OOB stat type: Harrell's C-index
    #>      Variable importance: anova
    #>
    #> -----------------------------------------

you may notice that the first input of `aorsf` is `data`. This is a design choice that makes it easier to use `orsf` with pipes (i.e., `%>%` or `|>`). For instance,

    library(dplyr)

    pbc_fit <- pbc_orsf |>
     select(-id) |>
     orsf(formula = Surv(time, status) ~ .,
          n_tree = 5)

## Interpretation

`aorsf` includes several functions dedicated to interpretation of ORSFs, both through estimation of partial dependence and variable importance.

### Variable importance

There are multiple methods to compute variable importance, and each can be applied to any type of oblique forest.

- To compute negation importance, ORSF multiplies each coefficient of that variable by -1 and then re-computes the out-of-sample (sometimes referred to as out-of-bag) accuracy of the ORSF model.

      orsf_vi_negate(pbc_fit)
      #>          bili         stage       albumin           sex           age
      #>  0.1562831697  0.0520732827  0.0309153515  0.0293622528  0.0263033691
      #>       spiders          chol          trig       protime         edema
      #>  0.0151495968  0.0117066134  0.0114025079  0.0100381426  0.0064478327
      #>       ascites      alk.phos      platelet           ast        hepato
      #>  0.0056226669  0.0043125505  0.0039744544  0.0037676982  0.0012937169
      #>        copper           trt
      #>  0.0008073227 -0.0099703489

- You can also compute variable importance using permutation, a more classical approach that noises up a predictor and then assigned the resulting degradation in prediction accuracy to be the importance of that predictor.

      orsf_vi_permute(penguin_fit)
      #>    bill_length_mm flipper_length_mm     bill_depth_mm            island
      #>       0.169723177       0.100482315       0.082210893       0.063499087
      #>       body_mass_g               sex              year
      #>       0.062724823       0.018467278       0.001506407

- A faster alternative to permutation and negation importance is ANOVA importance, which computes the proportion of times each variable obtains a low p-value (p < 0.01) while the forest is grown.

      orsf_vi_anova(bill_fit)
      #>           species               sex            island flipper_length_mm
      #>        0.35791720        0.20735914        0.11028325        0.08654496
      #>       body_mass_g     bill_depth_mm              year
      #>        0.07870294        0.06648474        0.01549007

### Partial dependence (PD)

Partial dependence (PD) shows the expected prediction from a model as a function of a single predictor or multiple predictors. The expectation is marginalized over the values of all other predictors, giving something like a multivariable adjusted estimate of the model’s prediction.

For more on PD, see the vignette

### Individual conditional expectations (ICE)

Unlike partial dependence, which shows the expected prediction as a function of one or multiple predictors, individual conditional expectations (ICE) show the prediction for an individual observation as a function of a predictor.

For more on ICE, see the vignette

## What about the original ORSF?

The original ORSF (i.e., `obliqueRSF`) used `glmnet` to find linear combinations of inputs. `aorsf` allows users to implement this approach using the `orsf_control_survival(method = 'net')` function:

    orsf_net <- orsf(data = pbc_orsf,
                     formula = Surv(time, status) ~ . - id,
                     control = orsf_control_survival(method = 'net'))

`net` forests fit a lot faster than the original ORSF function in `obliqueRSF`. However, `net` forests are still much slower than `cph` ones.

## aorsf and other machine learning software

The unique feature of `aorsf` is its fast algorithms to fit ORSF ensembles. `RLT` and `obliqueRSF` both fit oblique random survival forests, but `aorsf` does so faster. `ranger` and `randomForestSRC` fit survival forests, but neither package supports oblique splitting. `obliqueRF` fits oblique random forests for classification and regression, but not survival. `PPforest` fits oblique random forests for classification but not survival.

Note: The default prediction behavior for `aorsf` models is to produce predicted risk at a specific prediction horizon, which is not the default for `ranger` or `randomForestSRC`. I think this will change in the future, as computing time independent predictions with `aorsf` could be helpful.

## Learning more

`aorsf` began as a dedicated package for oblique random survival forests, and so most papers published so far have focused on survival analysis and risk prediction. However, the routines for regression and classification oblique RFs in `aorsf` have high overlap with the survival ones.

- See orsf for more details on oblique random survival forests.

- see the JCGS paper for more details on algorithms used specifically by `aorsf`.