R: Prediction for Random Forests for Survival, Regression, and...

predict.rfsrc {randomForestSRC} | R Documentation

## Prediction for Random Forests for Survival, Regression, and Classification

### Description

Obtain predicted values using a forest. Also returns performance values if the test data contains y-outcomes.

### Usage

    ## S3 method for class 'rfsrc'
    predict(object,
      newdata,
      m.target = NULL,
      importance = c(FALSE, TRUE, "none", "permute", "random", "anti"),
      get.tree = NULL,
      block.size = if (any(is.element(as.character(importance),
                         c("none", "FALSE")))) NULL else 10,
      ensemble = NULL,
      na.action = c("na.omit", "na.impute"),
      outcome = c("train", "test"),
      proximity = FALSE,
      forest.wt = FALSE,
      ptn.count = 0,
      distance = FALSE,
      var.used = c(FALSE, "all.trees", "by.tree"),
      split.depth = c(FALSE, "all.trees", "by.tree"), seed = NULL,
      do.trace = FALSE, membership = FALSE, statistics = FALSE,

      ...)

### Arguments

`object` | An object of class `(rfsrc, grow)` or `(rfsrc, forest)`.
`newdata` | Test data. If missing, the original grow (training) data is used.
`m.target` | Character vector for multivariate families specifying the target outcomes to be used. The default is to use all coordinates.
`importance` | Method for computing variable importance (VIMP) when test data contains y-outcomes values. Also see `vimp` for more flexibility, including joint vimp calculations. Another way to calculate VIMP is `holdoutvimp`.
`get.tree` | Vector of integer(s) identifying trees over which the ensembles are calculated over. By default, uses all trees in the forest. As an example, the user can extract the ensemble, the variable importance, or proximity from a single tree (or several trees). Note that `block.size` will be over-ridden so that it is no larger than the requested number of trees.
`block.size` | Should the error rate be calculated on every tree? When `NULL`, it will only be calculated on the last tree. To view the error rate on every nth tree, set the value to an integer between `1` and `ntree`. If importance is requested, VIMP is calculated in "blocks" of size equal to `block.size`, thus resulting in a useful compromise between ensemble and permutation VIMP.
`ensemble` | Optional parameter for specifying the type of ensemble. Can be `oob`, `inbag` or `all`, although not all choices will be applicable depending on the setting (e.g. when predicting on newdata there is no notion of out-of-bag).
`na.action` | Missing value action. The default `na.omit` removes the entire record if even one of its entries is `NA`. Selecting na.impute imputes the test data. Also see the function `impute` for fast direct imputation.
`outcome` | Determines whether the y-outcomes from the training data or the test data are used to calculate the predicted value. The default and natural choice is `train` which uses the original training data. Option is ignored when `newdata` is missing as the training data is used for the test data in such settings. The option is also ignored whenever the test data is devoid of y-outcomes. See the details and examples below for more information.
`proximity` | Should proximity between test observations be calculated? Possible choices are `"inbag"`, `"oob"`, `"all"`, `TRUE`, or `FALSE` — but some options may not be valid and will depend on the context of the predict call. The safest choice is `TRUE` if proximity is desired.
`distance` | Should distance between test observations be calculated? Possible choices are `"inbag"`, `"oob"`, `"all"`, `TRUE`, or `FALSE` — but some options may not be valid and will depend on the context of the predict call. The safest choice is `TRUE` if distance is desired.
`forest.wt` | Should the forest weight matrix for test observations be calculated? Choices are the same as proximity.
`ptn.count` | The number of terminal nodes that each tree in the grow forest should be pruned back to. The terminal node membership for the pruned forest is returned but no other action is taken. The default is `ptn.count=0` which does no pruning.
`var.used` | Record the number of times a variable is split?
`split.depth` | Return minimal depth for each variable for each case?
`seed` | Negative integer specifying seed for the random number generator.
`do.trace` | Number of seconds between updates to the user on approximate time to completion.
`membership` | Should terminal node membership and inbag information be returned?
`statistics` | Should split statistics be returned? Values can be parsed using `stat.split`.
`...` | Further arguments passed to or from other methods.

### Details

Predicted values are obtained by "dropping" test data down the training forest (the forest grown using the training data). Performance values are returned if test data contains y-outcome values. Single as well as joint VIMP are also returned if requested.

Setting na.action="na.impute" imputes missing test data (x-variables and/or y-outcomes). Test imputation uses only the grow-forest and training data to avoid biasing error rates and VIMP (Ishwaran et al. 2008). Also see the function `impute` for an alternate way to do fast and accurate imputation.

If no test data is provided, the original training data is used, and the code reverts to restore mode allowing the user to restore the original grow forest. This is useful, because it gives the user the ability to extract outputs from the forest that were not asked for in the original grow call.

If outcome="test", the predictor is calculated by using y-outcomes from the test data (outcome information must be present). Terminal nodes from the grow-forest are recalculated using y-outcomes from the test set. This yields a modified predictor in which the topology of the forest is based solely on the training data, but where predicted values are obtained from test data. Error rates and VIMP are calculated by bootstrapping the test data and using out-of-bagging to ensure unbiased estimates. See the examples below for illustration.

### Value

An object of class `(rfsrc, predict)`, which is a list with the following components:

`call` | The original grow call to `rfsrc`.
`family` | The family used in the analysis.
`n` | Sample size of test data (depends upon `NA` values).
`ntree` | Number of trees in the grow forest.
`yvar` | Test set y-outcomes or original grow y-outcomes if none.
`yvar.names` | A character vector of the y-outcome names.
`xvar` | Data frame of test set x-variables.
`xvar.names` | A character vector of the x-variable names.
`leaf.count` | Number of terminal nodes for each tree in the grow forest. Vector of length `ntree`.
`proximity` | Symmetric proximity matrix of the test data.
`forest` | The grow forest.
`membership` | Matrix recording terminal node membership for the test data where each column contains the node number that a case falls in for that tree.
`inbag` | Matrix recording inbag membership for the test data where each column contains the number of times that a case appears in the bootstrap sample for that tree.
`var.used` | Count of the number of times a variable was used in growing the forest.
`imputed.indv` | Vector of indices of records in test data with missing values.
`imputed.data` | Data frame comprising imputed test data. The first columns are the y-outcomes followed by the x-variables.
`split.depth` | Matrix [i][j] or array [i][j][k] recording the minimal depth for variable [j] for case [i], either averaged over the forest, or by tree [k].
`node.stats` | Split statistics returned when `statistics=TRUE` which can be parsed using `stat.split`.
`err.rate` | Cumulative OOB error rate for the test data if y-outcomes are present.
`importance` | Test set variable importance (VIMP). Can be `NULL`.
`predicted` | Test set predicted value.
`predicted.oob` | OOB predicted value (`NULL` unless outcome="test").

`quantile` | Quantile value at probabilities requested.
`quantile.oob` | OOB quantile value at probabilities requested (`NULL` unless outcome="test").

`++++++++` | for classification settings, additionally ++++++++

`class` | In-bag predicted class labels.
`class.oob` | OOB predicted class labels (`NULL` unless outcome="test").

`++++++++` | for multivariate settings, additionally ++++++++

`regrOutput` | List containing performance values for test multivariate regression responses (applies only in multivariate settings).
`clasOutput` | List containing performance values for test multivariate categorical (factor) responses (applies only in multivariate settings).
`++++++++` | for survival settings, additionally ++++++++

`chf` | Cumulative hazard function (CHF).
`chf.oob` | OOB CHF (`NULL` unless outcome="test").
`survival` | Survival function.
`survival.oob` | OOB survival function (`NULL` unless outcome="test").
`time.interest` | Ordered unique death times.
`ndead` | Number of deaths.

`++++++++` | for competing risks, additionally ++++++++

`chf` | Cause-specific cumulative hazard function (CSCHF) for each event.
`chf.oob` | OOB CSCHF for each event (`NULL` unless outcome="test").
`cif` | Cumulative incidence function (CIF) for each event.
`cif.oob` | OOB CIF for each event (`NULL` unless outcome="test").
`time.interest` | Ordered unique event times.
`ndead` | Number of events.

### Note

The dimensions and values of returned objects depend heavily on the underlying family and whether y-outcomes are present in the test data. In particular, items related to performance will be `NULL` when y-outcomes are not present. For multivariate families, predicted values, VIMP, error rate, and performance values are stored in the lists `regrOutput` and `clasOutput` which can be extracted using functions `get.mv.error`, `get.mv.predicted` and `get.mv.vimp`.

### Author(s)

Hemant Ishwaran and Udaya B. Kogalur

### References

Breiman L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests, Ann. App. Statist., 2:841-860.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

### See Also

`holdout.vimp.rfsrc`, `plot.competing.risk.rfsrc`, `plot.rfsrc`, `plot.survival.rfsrc`, `plot.variable.rfsrc`, `rfsrc`, `rfsrc.fast`, `stat.split.rfsrc`, `synthetic.rfsrc`, `vimp.rfsrc`

### Examples

    ## ------------------------------------------------------------
    ## typical train/testing scenario
    ## ------------------------------------------------------------

    data(veteran, package = "randomForestSRC")
    train <- sample(1:nrow(veteran), round(nrow(veteran) * 0.80))
    veteran.grow <- rfsrc(Surv(time, status) ~ ., veteran[train, ], ntree = 100)
    veteran.pred <- predict(veteran.grow, veteran[-train , ])
    print(veteran.grow)
    print(veteran.pred)

    ## ------------------------------------------------------------
    ## predicted probability and predicted class labels are returned
    ## in the predict object for classification analyses
    ## ------------------------------------------------------------

    data(breast, package = "randomForestSRC")
    breast.obj <- rfsrc(status ~ ., data = breast[(1:100), ])
    breast.pred <- predict(breast.obj, breast[-(1:100), ])
    print(head(breast.pred$predicted))
    print(breast.pred$class)

    ## ------------------------------------------------------------
    ## example illustrating restore mode
    ## if predict is called without specifying the test data
    ## the original training data is used and the forest is restored
    ## ------------------------------------------------------------

    ## first we make the grow call
    airq.obj <- rfsrc(Ozone ~ ., data = airquality)

    ## now we restore it and compare it to the original call
    ## they are identical
    predict(airq.obj)
    print(airq.obj)

    ## we can retrieve various outputs that were not asked for in
    ## in the original call

    ## here we extract the proximity matrix
    prox <- predict(airq.obj, proximity = TRUE)$proximity
    print(prox[1:10,1:10])

    ## here we extract the number of times a variable was used to grow
    ## the grow forest
    var.used <- predict(airq.obj, var.used = "by.tree")$var.used
    print(head(var.used))

    ## ------------------------------------------------------------
    ## unique feature of randomForestSRC
    ## cross-validation can be used when factor labels differ over
    ## training and test data
    ## ------------------------------------------------------------

    ## first we convert all x-variables to factors
    data(veteran, package = "randomForestSRC")
    veteran.factor <- data.frame(lapply(veteran, factor))
    veteran.factor$time <- veteran$time
    veteran.factor$status <- veteran$status

    ## split the data into unbalanced train/test data (5/95)
    ## the train/test data have the same levels, but different labels
    train <- sample(1:nrow(veteran), round(nrow(veteran) * .05))
    summary(veteran.factor[train,])
    summary(veteran.factor[-train,])

    ## grow the forest on the training data and predict on the test data
    veteran.f.grow <- rfsrc(Surv(time, status) ~ ., veteran.factor[train, ])
    veteran.f.pred <- predict(veteran.f.grow, veteran.factor[-train , ])
    print(veteran.f.grow)
    print(veteran.f.pred)

    ## ------------------------------------------------------------
    ## example illustrating the flexibility of outcome = "test"
    ## illustrates restoration of forest via outcome = "test"
    ## ------------------------------------------------------------

    ## first we make the grow call
    data(pbc, package = "randomForestSRC")
    pbc.grow <- rfsrc(Surv(days, status) ~ ., pbc)

    ## now use predict with outcome = TEST
    pbc.pred <- predict(pbc.grow, pbc, outcome = "test")

    ## notice that error rates are the same!!
    print(pbc.grow)
    print(pbc.pred)

    ## note this is equivalent to restoring the forest
    pbc.pred2 <- predict(pbc.grow)
    print(pbc.grow)
    print(pbc.pred)
    print(pbc.pred2)

    ## similar example, but with na.action = "na.impute"
    airq.obj <- rfsrc(Ozone ~ ., data = airquality, na.action = "na.impute")
    print(airq.obj)
    print(predict(airq.obj))
    ## ... also equivalent to outcome="test" but na.action = "na.impute" required
    print(predict(airq.obj, airquality, outcome = "test", na.action = "na.impute"))

    ## classification example
    iris.obj <- rfsrc(Species ~., data = iris)
    print(iris.obj)
    print(predict.rfsrc(iris.obj, iris, outcome = "test"))

    ## ------------------------------------------------------------
    ## another example illustrating outcome = "test"
    ## unique way to check reproducibility of the forest
    ## ------------------------------------------------------------

    ## primary call
    set.seed(542899)
    data(pbc, package = "randomForestSRC")
    train <- sample(1:nrow(pbc), round(nrow(pbc) * 0.50))
    pbc.out <- rfsrc(Surv(days, status) ~ .,  data=pbc[train, ])

    ## standard predict call
    pbc.train <- predict(pbc.out, pbc[-train, ], outcome = "train")
    ##non-standard predict call: overlays the test data on the grow forest
    pbc.test <- predict(pbc.out, pbc[-train, ], outcome = "test")

    ## check forest reproducibilility by comparing "test" predicted survival
    ## curves to "train" predicted survival curves for the first 3 individuals
    Time <- pbc.out$time.interest
    matplot(Time, t(pbc.train$survival[1:3,]), ylab = "Survival", col = 1, type = "l")
    matlines(Time, t(pbc.test$survival[1:3,]), col = 2)

    ## ------------------------------------------------------------
    ## ... just for _fun_ ...
    ## survival analysis using mixed multivariate outcome analysis
    ## compare the predicted value to RSF
    ## ------------------------------------------------------------

    ## fit the pbc data using RSF
    data(pbc, package = "randomForestSRC")
    rsf.obj <- rfsrc(Surv(days, status) ~ ., pbc)
    yvar <- rsf.obj$yvar

    ## fit a mixed outcome forest using days and status as y-variables
    pbc.mod <- pbc
    pbc.mod$status <- factor(pbc.mod$status)
    mix.obj <- rfsrc(Multivar(days, status) ~., pbc.mod)

    ## compare oob predicted values
    rsf.pred <- rsf.obj$predicted.oob
    mix.pred <- mix.obj$regrOutput$days$predicted.oob
    plot(rsf.pred, mix.pred)

    ## compare C-index error rate
    rsf.err <- get.cindex(yvar$days, yvar$status, rsf.pred)
    mix.err <- 1 - get.cindex(yvar$days, yvar$status, mix.pred)
    cat("RSF                :", rsf.err, "\n")
    cat("multivariate forest:", mix.err, "\n")

[Package randomForestSRC version 2.9.3 Index]