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,
  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, 
  ...)

Arguments

Details

Predicted values are obtained by dropping test data down the grow forest (the forest grown using the training data). The overall error rate and VIMP are also returned if the test data contains y-outcome values. Single as well as joint VIMP measures can be requested. Note that calculating VIMP can be computationally expensive (especially when the dimension is high), thus if VIMP is not needed, computational times can be significantly improved by setting importance="none" which turns VIMP off.

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 no test data is provided, then 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. See the examples below for illustration.

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.

Value

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

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 detailed definitions of returned values (such as predicted) see the help file for rfsrc.

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

plot.competing.risk, plot.rfsrc, plot.survival, plot.variable, rfsrc, vimp

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), ], nsplit = 10)
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, 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)

# 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, ],
        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)