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

  m.target = NULL,
  importance = c(FALSE, TRUE, "none", "anti", "permute", "random"),
  get.tree = NULL,
  block.size = if (any(is.element(as.character(importance),
                     c("none", "FALSE")))) NULL else 10,
  na.action = c("na.omit", "na.impute", "na.random"),
  outcome = c("train", "test"),
  perf.type = NULL,
  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,



An object of class (rfsrc, grow) or (rfsrc, forest).


Test data. If missing, the original grow (training) data is used.


Character vector for multivariate families specifying the target outcomes to be used. The default uses all coordinates.


Method used for variable importance (VIMP). Also see vimp for more flexibility, including joint vimp calculations. See holdoutvimp for an alternate importance measure.


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 VIMP , 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. See example below illustrating how to extract VIMP for each tree.


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 compromise between ensemble and permutation VIMP.


Missing value action. The default na.omit removes the entire record if any entry is NA. Selecting na.random uses fast random imputation, while na.impute uses the imputation method described in rfsrc.


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.


Optional character value for requesting metric used for predicted value, variable importance (VIMP) and error rate. If not specified, values returned are calculated by the default action used for the family. Currently applicable only to classification and multivariate classification; allowed values are perf.type="misclass" (default), perf.type="brier" and perf.type="gmean".


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.


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.


Should the forest weight matrix for test observations be calculated? Choices are the same as proximity.


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.


Record the number of times a variable is split?


Return minimal depth for each variable for each case?


Negative integer specifying seed for the random number generator.


Number of seconds between updates to the user on approximate time to completion.


Should terminal node membership and inbag information be returned?


Should split statistics be returned? Values can be parsed using stat.split.


Further arguments passed to or from other methods.


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

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 trained forest. This feature allows extracting outputs from the forest 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 trained 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.

csv=TRUE returns case specific VIMP; cse=TRUE returns case specific error rates. Applies to all families except survival. These options can also be applied while training.


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


The original grow call to rfsrc.


The family used in the analysis.


Sample size of test data (depends upon NA values).


Number of trees in the grow forest.


Test set y-outcomes or original grow y-outcomes if none.


A character vector of the y-outcome names.


Data frame of test set x-variables.


A character vector of the x-variable names.


Number of terminal nodes for each tree in the grow forest. Vector of length ntree.


Symmetric proximity matrix of the test data.


The grow forest.


Matrix recording terminal node membership for the test data where each column contains the node number that a case falls in for that tree.


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.


Count of the number of times a variable was used in growing the forest.


Vector of indices of records in test data with missing values.


Data frame comprising imputed test data. The first columns are the y-outcomes followed by the x-variables.


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.


Split statistics returned when statistics=TRUE which can be parsed using stat.split.


Cumulative OOB error rate for the test data if y-outcomes are present.


Test set variable importance (VIMP). Can be NULL.


Test set predicted value.


OOB predicted value (NULL unless outcome="test").


Quantile value at probabilities requested.


OOB quantile value at probabilities requested (NULL unless outcome="test").


for classification settings, additionally ++++++++


In-bag predicted class labels.


OOB predicted class labels (NULL unless outcome="test").


for multivariate settings, additionally ++++++++


List containing performance values for test multivariate regression responses (applies only in multivariate settings).


List containing performance values for test multivariate categorical (factor) responses (applies only in multivariate settings).


for survival settings, additionally ++++++++


Cumulative hazard function (CHF).


OOB CHF (NULL unless outcome="test").


Survival function.


OOB survival function (NULL unless outcome="test").


Ordered unique death times.


Number of deaths.


for competing risks, additionally ++++++++


Cause-specific cumulative hazard function (CSCHF) for each event.


OOB CSCHF for each event (NULL unless outcome="test").


Cumulative incidence function (CIF) for each event.


OOB CIF for each event (NULL unless outcome="test").


Ordered unique event times.


Number of events.


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.


Hemant Ishwaran and Udaya B. Kogalur


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.


## ------------------------------------------------------------
## 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, ])

# \donttest{
## ------------------------------------------------------------
## restore mode
## - if predict is called without specifying the test data
##   the original training data is used and the forest is restored
## ------------------------------------------------------------

## first train the forest
airq.obj <- rfsrc(Ozone ~ ., data = airquality)

## now we restore it and compare it to the original call
## they are identical

## 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

## 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

## ------------------------------------------------------------
## prediction when test data has missing values
## ------------------------------------------------------------

data(pbc, package = "randomForestSRC")
trn <- pbc[1:312,]
tst <- pbc[-(1:312),]
o <- rfsrc(Surv(days, status) ~ ., trn)

## default imputation method used by rfsrc
print(predict(o, tst, na.action = "na.impute"))

## random imputation
print(predict(o, tst, na.action = "na.random"))

## ------------------------------------------------------------
## requesting different performance for classification
## ------------------------------------------------------------

## default performance is misclassification
o <- rfsrc(Species~., iris)

## get (normalized) brier performance
print(predict(o, perf.type = "brier"))

## ------------------------------------------------------------
## vimp for each tree: illustrates get.tree 
## ------------------------------------------------------------

## regression analysis but no VIMP
o <- rfsrc(mpg~., mtcars)

## now extract VIMP for each tree using get.tree
vimp.tree <- do.call(rbind, lapply(1:o$ntree, function(b) {
     predict(o, get.tree = b, importance = TRUE)$importance

## boxplot of tree VIMP
boxplot(vimp.tree, outline = FALSE, col = "cyan")
abline(h = 0, lty = 2, col = "red")

## summary information of tree VIMP

## extract tree-averaged VIMP using importance=TRUE
## remember to set block.size to 1
print(predict(o, importance = TRUE, block.size = 1)$importance)

## use direct call to vimp() for tree-averaged VIMP
print(vimp(o, block.size = 1)$importance)

## ------------------------------------------------------------
## vimp for just a few trees
## illustrates how to get vimp if you have a large data set
## ------------------------------------------------------------

## survival analysis but no VIMP
data(pbc, package = "randomForestSRC")
o <- rfsrc(Surv(days, status) ~ ., pbc, ntree = 2000)

## get vimp for a small number of trees
print(predict(o, get.tree=1:250, importance = TRUE)$importance)

## ------------------------------------------------------------
## case-specific vimp
## returns VIMP for each case
## ------------------------------------------------------------

o <- rfsrc(mpg~., mtcars)
op <- predict(o, importance = TRUE, csv = TRUE)
csvimp <- get.mv.csvimp(op, standardize=TRUE)

## ------------------------------------------------------------
## case-specific error rate
## returns tree-averaged error rate for each case
## ------------------------------------------------------------

o <- rfsrc(mpg~., mtcars)
op <- predict(o, importance = TRUE, cse = TRUE)
cserror <- get.mv.cserror(op, standardize=TRUE)

## ------------------------------------------------------------
## 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), ])

## ------------------------------------------------------------
## 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")
veteran2 <- data.frame(lapply(veteran, factor))
veteran2$time <- veteran$time
veteran2$status <- veteran$status

## split the data into unbalanced train/test data (25/75)
## the train/test data have the same levels, but different labels
train <- sample(1:nrow(veteran2), round(nrow(veteran2) * .25))

## train the forest and use this to predict on test data
o.grow <- rfsrc(Surv(time, status) ~ ., veteran2[train, ]) 
o.pred <- predict(o.grow, veteran2[-train , ])

## even harder ... factor level not previously encountered in training
veteran3 <- veteran2[1:3, ]
veteran3$celltype <- factor(c("newlevel", "1", "3"))
o2.pred <- predict(o.grow, veteran3)
## the unusual level is treated like a missing value but is not removed

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

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

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

## notice that error rates are the same!!

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

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

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

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

## standard prediction 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
## ------------------------------------------------------------

## train survival forest using pbc data
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")

# }