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

In ehrlinger/randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC)

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, outcome.target = NULL,
  importance = c(FALSE, TRUE, "none", "permute", "random", "anti",
                 "permute.ensemble", "random.ensemble", "anti.ensemble")[1],
  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 = 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.
outcome.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). 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. Selecting na.impute imputes the test data.
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.
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 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 and only training data is used to impute test data to avoid biasing error rates and VIMP (Ishwaran et al. 2008). See the rfsrc help file for details.

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.

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:

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").

++++++++

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 regression responses (if such values are present).
clasOutput	List containing performance values for test categorical (factor) responses (if such values are present).
++++++++	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.

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

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, stat.split, 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)

## Not run: 
## ------------------------------------------------------------
## 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)

## ------------------------------------------------------------
## 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, nsplit = 10)
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, nsplit = 10)

# 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 <- randomForestSRC:::cindex(yvar$days, yvar$status, rsf.pred)
mix.err <- 1 - randomForestSRC:::cindex(yvar$days, yvar$status, mix.pred)
cat("RSF                :", rsf.err, "\n")
cat("multivariate forest:", mix.err, "\n")


## End(Not run)

ehrlinger/randomForestSRC documentation built on May 16, 2019, 1:20 a.m.