randomForestSRC (version 1.2)

predict.rfsrc: Prediction for Random Forests for Survival, Regression, and Classification

Description

Prediction on test data.

Usage

## S3 method for class 'rfsrc':
predict(object, newdata,
  importance = c("permute", "random", "permute.ensemble", "random.ensemble", "none"),
  na.action = c("na.omit", "na.impute"), outcome = c("train", "test"),
  proximity = FALSE, var.used = c(FALSE, "all.trees", "by.tree"),
  split.depth = c(FALSE, "all.trees", "by.tree"), seed = NULL,
  do.trace = FALSE,
  membership = TRUE,
  statistics = FALSE, ...)

Arguments

object

An object of class (rfsrc, grow) or (rfsrc, forest). Requires forest=TRUE in the original rfsrc call.

newdata

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

importance

Method for computing variable importance (VIMP). See rfsrc for details. Only applies when the test data contains y-outcome values.

na.action

Missing value action. The default na.omit removes the entire record if even one of its entries is NA. Use na.impute to impute the test data.

outcome

Determines which data should be used to calculate the grow predictor. The default and natural choice train uses the training data, but see details below.

proximity

Should proximity measure between test observations be calculated? Can be large.

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

Should trace output be enabled? Integer values can also be passed. A positive value causes output to be printed each do.trace iteration.

membership

Should terminal node membership and inbag information be returned?

statistics

Should statistics be returned?

...

Further arguments passed to or from other methods.

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.
• forest
  The grow forest.
• proximity
  Symmetric proximity matrix of the test data.
• membership
  Matrix of dimension nxntree 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 of dimension nxntree 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.
• imputed.indv
  Vector of indices of records in test data with missing values.
• imputed.data
  Data frame comprising imputed test data. First columns are the y-outcomes.
• split.depth
  Matrix of size nxp where entry [i][j] is the minimal depth for variable [j] for test case [i].
• 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").
• ...... class
  for classification settings, additionally the following ......
• class
  In-bag predicted class labels.
• class.oob
  OOB predicted class labels (NULL unless outcome="test").
• ...... surv
  for survival settings, additionally the following ......
• 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.
• ...... surv-CR
  for competing risks, additionally the following ......
• 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.

Details

Predicted values are obtained by dropping test data down the grow forest (the forest grown using the the training data). If the test data contains y-outcome values, the overall error rate and VIMP are also returned. Single as well as joint VIMP measures can be requested. Note that calculating VIMP can be computationally expensive, especially when the dimension is high. If VIMP is not needed, computational times can be significantly improved by setting importance="none" which turns VIMP calculations off entirely. Setting na.action="na.impute" imputes missing test data (x-variables and/or y-outcomes). Imputation uses the grow-forest such that only training data is used when imputing test data to avoid biasing error rates and VIMP (Ishwaran et al. 2008). If outcome="test", the predictor is calculated by using y-outcomes from the test data (outcome information must be present). In this case, the terminal nodes from the grow-forest are recalculated using the 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 the predicted value is based on the 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 for illustration.

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

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)


## ------------------------------------------------------------
## 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"
## use this to get out-of-bag error rate
## ------------------------------------------------------------

# first, we make the grow call
data(pbc, package = "randomForestSRC")
pbc.grow <- rfsrc(Surv(days, status) ~ ., pbc, nsplit = 10)

# 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)

# 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, outcome = "test", na.action = "na.impute"))

# similar example for classification
iris.obj <- rfsrc(Species ~., data = iris)
print(iris.obj)
print(predict.rfsrc(iris.obj, 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, ],
        nsplit = 10)

# 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$time.interest
matplot(Time, t(exp(-pbc.train$chf)[1:3,]), ylab = "Survival", col = 1, type = "l")
matlines(Time, t(exp(-pbc.test$chf)[1:3,]), col = 2)