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

predict(object,
  newdata,
  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,
   
  ...)

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 uses all coordinates.

importance

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

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 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.

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 compromise between ensemble and permutation VIMP.

na.action

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.

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.

perf.type

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".

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 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.

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

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.

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)

# \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
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))

## ------------------------------------------------------------
## 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)
print(o)

## 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
print(summary(vimp.tree))

## 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)
print(csvimp)

## ------------------------------------------------------------
## 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)
print(cserror)


## ------------------------------------------------------------
## 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)


## ------------------------------------------------------------
## 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))
summary(veteran2[train,])
summary(veteran2[-train,])

## 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 , ])
print(o.grow)
print(o.pred)

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

## ------------------------------------------------------------
## 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!!
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
## ------------------------------------------------------------

## training step
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 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")

# }