R: Conditional Random Forests

cforest {partykit} | R Documentation

## Conditional Random Forests

### Description

An implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners.

### Usage

    cforest(formula, data, weights, subset, offset, cluster, strata,
            na.action = na.pass,
    	control = ctree_control(teststat = "quad", testtype = "Univ",
                mincriterion = 0, saveinfo = FALSE, ...),
            ytrafo = NULL, scores = NULL, ntree = 500L,
            perturb = list(replace = FALSE, fraction = 0.632),
            mtry = ceiling(sqrt(nvar)), applyfun = NULL, cores = NULL,
            trace = FALSE, ...)
    ## S3 method for class 'cforest'
    predict(object, newdata = NULL,
            type = c("response", "prob", "weights", "node"),
            OOB = FALSE, FUN = NULL, simplify = TRUE, scale = TRUE, ...)
    ## S3 method for class 'cforest'
    gettree(object, tree = 1L, ...)

### Arguments

`formula` | a symbolic description of the model to be fit.
`data` | a data frame containing the variables in the model.
`subset` | an optional vector specifying a subset of observations to be used in the fitting process.
`weights` | an optional vector of weights to be used in the fitting process. Non-negative integer valued weights are allowed as well as non-negative real weights. Observations are sampled (with or without replacement) according to probabilities `weights / sum(weights)`. The fraction of observations to be sampled (without replacement) is computed based on the sum of the weights if all weights are integer-valued and based on the number of weights greater zero else. Alternatively, `weights` can be a double matrix defining case weights for all `ncol(weights)` trees in the forest directly. This requires more storage but gives the user more control.
`offset` | an optional vector of offset values.
`cluster` | an optional factor indicating independent clusters. Highly experimental, use at your own risk.
`strata` | an optional factor for stratified sampling.
`na.action` | a function which indicates what should happen when the data contain missing value.
`control` | a list with control parameters, see `ctree_control`. The default values correspond to those of the default values used by `cforest` from the `party` package. `saveinfo = FALSE` leads to less memory hungry representations of trees. Note that arguments `mtry`, `cores` and `applyfun` in `ctree_control` are ignored for `cforest`, because they are already set.
`ytrafo` | an optional named list of functions to be applied to the response variable(s) before testing their association with the explanatory variables. Note that this transformation is only performed once for the root node and does not take weights into account (which means, the forest bootstrap or subsetting is ignored, which is almost certainly not a good idea). Alternatively, `ytrafo` can be a function of `data` and `weights`. In this case, the transformation is computed for every node and the corresponding weights. This feature is experimental and the user interface likely to change.
`scores` | an optional named list of scores to be attached to ordered factors.
`ntree` | Number of trees to grow for the forest.
`perturb` | a list with arguments `replace` and `fraction` determining which type of resampling with `replace = TRUE` referring to the n-out-of-n bootstrap and `replace = FALSE` to sample splitting. `fraction` is the number of observations to draw without replacement.
`mtry` | number of input variables randomly sampled as candidates at each node for random forest like algorithms. Bagging, as special case of a random forest without random input variable sampling, can be performed by setting `mtry` either equal to `Inf` or manually equal to the number of input variables.
`applyfun` | an optional `lapply`-style function with arguments `function(X, FUN, ...)`. It is used for computing the variable selection criterion. The default is to use the basic `lapply` function unless the `cores` argument is specified (see below).
`cores` | numeric. If set to an integer the `applyfun` is set to `mclapply` with the desired number of `cores`.
`trace` | a logical indicating if a progress bar shall be printed while the forest grows.
`object` | An object as returned by `cforest`
`newdata` | An optional data frame containing test data.
`type` | a character string denoting the type of predicted value returned, ignored when argument `FUN` is given. For `"response"`, the mean of a numeric response, the predicted class for a categorical response or the median survival time for a censored response is returned. For `"prob"` the matrix of conditional class probabilities (`simplify = TRUE`) or a list with the conditional class probabilities for each observation (`simplify = FALSE`) is returned for a categorical response. For numeric and censored responses, a list with the empirical cumulative distribution functions and empirical survivor functions (Kaplan-Meier estimate) is returned when `type = "prob"`. `"weights"` returns an integer vector of prediction weights. For `type = "where"`, a list of terminal node ids for each of the trees in the forest ist returned.
`OOB` | a logical defining out-of-bag predictions (only if `newdata = NULL`).
`FUN` | a function to compute summary statistics. Predictions for each node have to be computed based on arguments `(y, w)` where `y` is the response and `w` are case weights.
`simplify` | a logical indicating whether the resulting list of predictions should be converted to a suitable vector or matrix (if possible).
`scale` | a logical indicating scaling of the nearest neighbor weights by the sum of weights in the corresponding terminal node of each tree. In the simple regression forest, predicting the conditional mean by nearest neighbor weights will be equivalent to (but slower!) the aggregation of means.
`tree` | an integer, the number of the tree to extract from the forest.
`...` | additional arguments.

### Details

This implementation of the random forest (and bagging) algorithm differs from the reference implementation in `randomForest` with respect to the base learners used and the aggregation scheme applied.

Conditional inference trees, see `ctree`, are fitted to each of the `ntree` perturbed samples of the learning sample. Most of the hyper parameters in `ctree_control` regulate the construction of the conditional inference trees.

Hyper parameters you might want to change are:

1. The number of randomly preselected variables `mtry`, which is fixed to the square root of the number of input variables.

2. The number of trees `ntree`. Use more trees if you have more variables.

3. The depth of the trees, regulated by `mincriterion`. Usually unstopped and unpruned trees are used in random forests. To grow large trees, set `mincriterion` to a small value.

The aggregation scheme works by averaging observation weights extracted from each of the `ntree` trees and NOT by averaging predictions directly as in `randomForest`. See Hothorn et al. (2004) and Meinshausen (2006) for a description.

Predictions can be computed using `predict`. For observations with zero weights, predictions are computed from the fitted tree when `newdata = NULL`.

Ensembles of conditional inference trees have not yet been extensively tested, so this routine is meant for the expert user only and its current state is rather experimental. However, there are some things available in `cforest` that can't be done with `randomForest`, for example fitting forests to censored response variables (see Hothorn et al., 2004, 2006a) or to multivariate and ordered responses. Using the rich `partykit` infrastructure allows additional functionality in `cforest`, such as parallel tree growing and probabilistic forecasting (for example via quantile regression forests). Also plotting of single trees from a forest is much easier now.

Unlike `cforest`, `cforest` is entirely written in R which makes customisation much easier at the price of longer computing times. However, trees can be grown in parallel with this R only implemention which renders speed less of an issue. Note that the default values are different from those used in package `party`, most importantly the default for mtry is now data-dependent. `predict(, type = "node")` replaces the `where` function and `predict(, type = "prob")` the `treeresponse` function.

Moreover, when predictors vary in their scale of measurement of number of categories, variable selection and computation of variable importance is biased in favor of variables with many potential cutpoints in `randomForest`, while in `cforest` unbiased trees and an adequate resampling scheme are used by default. See Hothorn et al. (2006b) and Strobl et al. (2007) as well as Strobl et al. (2009).

### Value

An object of class `cforest`.

### References

Breiman L (2001). Random Forests. Machine Learning, 45(1), 5–32.

Hothorn T, Lausen B, Benner A, Radespiel-Troeger M (2004). Bagging Survival Trees. Statistics in Medicine, 23(1), 77–91.

Hothorn T, B?hlmann P, Dudoit S, Molinaro A, Van der Laan MJ (2006a). Survival Ensembles. Biostatistics, 7(3), 355–373.

Hothorn T, Hornik K, Zeileis A (2006b). Unbiased Recursive Partitioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15(3), 651–674.

Hothorn T, Zeileis A (2015). partykit: A Modular Toolkit for Recursive Partytioning in R. Journal of Machine Learning Research, 16, 3905–3909.

Meinshausen N (2006). Quantile Regression Forests. Journal of Machine Learning Research, 7, 983–999.

Strobl C, Boulesteix AL, Zeileis A, Hothorn T (2007). Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution. BMC Bioinformatics, 8, 25. http://www.biomedcentral.com/1471-2105/8/25

Strobl C, Malley J, Tutz G (2009). An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests. Psychological Methods, 14(4), 323–348.

### Examples

    ## basic example: conditional inference forest for cars data
    cf <- cforest(dist ~ speed, data = cars)

    ## prediction of fitted mean and visualization
    nd <- data.frame(speed = 4:25)
    nd$mean  <- predict(cf, newdata = nd, type = "response")
    plot(dist ~ speed, data = cars)
    lines(mean ~ speed, data = nd)

    ## predict quantiles (aka quantile regression forest)
    myquantile <- function(y, w) quantile(rep(y, w), probs = c(0.1, 0.5, 0.9))
    p <- predict(cf, newdata = nd, type = "response", FUN = myquantile)
    colnames(p) <- c("lower", "median", "upper")
    nd <- cbind(nd, p)

    ## visualization with conditional (on speed) prediction intervals
    plot(dist ~ speed, data = cars, type = "n")
    with(nd, polygon(c(speed, rev(speed)), c(lower, rev(upper)),
      col = "lightgray", border = "transparent"))
    points(dist ~ speed, data = cars)
    lines(mean ~ speed, data = nd, lwd = 1.5)
    lines(median ~ speed, data = nd, lty = 2, lwd = 1.5)
    legend("topleft", c("mean", "median", "10% - 90% quantile"),
      lwd = c(1.5, 1.5, 10), lty = c(1, 2, 1),
      col = c("black", "black", "lightgray"), bty = "n")

    ### we may also use predicted conditional (on speed) densities
    mydensity <- function (y, w) approxfun(density(y, weights = w/sum(w))[1:2], rule = 2)
    pd <- predict(cf, newdata = nd, type = "response", FUN = mydensity)

    ## visualization in heatmap (instead of scatterplot)
    ## with fitted curves as above
    dist <- -10:150
    dens <- t(sapply(seq_along(pd), function(i) pd[[i]](dist)))
    image(nd$speed, dist, dens, xlab = "speed", col = rev(gray.colors(9)))
    lines(mean ~ speed, data = nd, lwd = 1.5)
    lines(median ~ speed, data = nd, lty = 2, lwd = 1.5)
    lines(lower ~ speed, data = nd, lty = 2)
    lines(upper ~ speed, data = nd, lty = 2)

    ## Not run:

    ### honest (i.e., out-of-bag) cross-classification of
    ### true vs. predicted classes
    data("mammoexp", package = "TH.data")
    table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp, ntree = 50),
                               OOB = TRUE, type = "response"))

    ### fit forest to censored response
    if (require("TH.data") && require("survival")) {

        data("GBSG2", package = "TH.data")
        bst <- cforest(Surv(time, cens) ~ ., data = GBSG2, ntree = 50)

        ### estimate conditional Kaplan-Meier curves
        print(predict(bst, newdata = GBSG2[1:2,], OOB = TRUE, type = "prob"))

        print(gettree(bst))
    }

    ## End(Not run)

[Package partykit version 1.2-7 Index]