rsf {randomSurvivalForest} | R Documentation |
Prediction and variable selection for right censored survival and competing risk data using Random Survival Forests (RSF) (Ishwaran, Kogalur, Blackstone and Lauer, 2008). A random forest (Breiman, 2001) of survival trees is used for ensemble estimation of the cumulative hazard function (CHF) in right-censored settings and the conditional cumulative hazard function (CCHF) in the case of competing risks. Different survival tree splitting rules can be used to grow trees. An “out-of-bag” estimate of Harrell's concordance index (Harrell, 1982) is provided for assessing prediction accuracy. Variable importance (VIMP) for single, as well as grouped variables, can be used to filter variables and to assess variable predictiveness. Minimal depth variable selection is also available. Missing data (x-variables, survival times, censoring indicators) can be imputed on both training and test data.
rsf(formula, data = NULL, ntree = 1000, mtry = NULL, nodesize = NULL, splitrule = NULL, nsplit = 0, importance = c("randomsplit", "permute", "none")[1], big.data = FALSE, na.action = c("na.omit", "na.impute")[1], nimpute = 1, predictorWt = NULL, forest = TRUE, proximity = FALSE, varUsed = NULL, split.depth = FALSE, seed = NULL, do.trace = FALSE, ...)
formula |
A symbolic description of the model to be fit. |
data |
Data frame containing the data used in the formula.
Missing values allowed. See |
ntree |
Number of trees to grow. This should not be set to a number too small, in order to ensure that every input row gets predicted at least a few times. |
mtry |
Number of variables randomly sampled at each split.
The default is sqrt( |
nodesize |
Minimum number of deaths with unique survival times required for a terminal node. Default is approximately 3 for right-censoring and 6 for competing risk data. Larger values create smaller trees. |
splitrule |
Splitting rule used to grow trees. See details below. |
nsplit |
Non-negative integer value. If non-zero, the specified tree splitting rule is randomized which can significantly increase speed. See details below. |
importance |
Method used to compute variable importance. See details below. |
big.data |
Set this value to TRUE when the number of
variables |
na.action |
Action to be taken if the data contains NA's.
Possible values are " |
nimpute |
Number of iterations of the missing data algorithm. |
predictorWt |
Vector of non-negative weights where entry
|
forest |
Should the forest object be returned? Used for prediction on new data and required for many of the wrappers to work. |
proximity |
Should the proximity between observations be
calculated? Creates an |
varUsed |
Analyzes which variables are used (split upon) in the
forest. Default is NULL. Possible values are " |
split.depth |
Return minimal depth for each variable for each case. Default is FALSE. Used for variable selection: see details below. |
seed |
Seed for random number generator. Must be a negative integer (the R wrapper handles incorrectly set seed values). |
do.trace |
Should trace output be enabled? Default is
FALSE. Integer values can also be passed. A positive value
causes output to be printed each |
... |
Further arguments passed to or from other methods. |
—> Splitting Rules:
Four primary splitting rules are available for growing a survival
forest for right-censored data: "logrank
", "conserve
",
"logrankscore
", and "random
".
The default rule, "logrank
", splits tree nodes by maximization
of the log-rank test statistic (Segal, 1988; Leblanc and Crowley,
1993). The "conserve
" rule splits nodes by finding daughters
closest to the conservation of events principle (see Naftel,
Blackstone and Turner, 1985). The "logrankscore
" rule uses a
standardized log-rank statistic (Hothorn and Lausen, 2003). The
"random
" rule implements pure random splitting. For each node,
a variable is randomly selected from a random set of mtry
variables and the node is split using a random split point (Cutler and
Zhao, 2001; Lin and Jeon, 2006). Note, however, that because random
splitting promotes splits near the edges, node splitting can terminate
early resulting in extremely unbalanced trees. To correct this, the
definition of nodesize
is taken in this setting (and this
setting alone) to equal the minimum number of unique deaths within a
node required to split the node.
A random version of the "logrank
", "conserve
" and
"logrankscore
" splitting rules can be invoked using
nsplit
. If nsplit
is set to a non-zero positive
integer, then a maximum of nsplit
split points are chosen
randomly for each of the mtry
variables within a node (this is
in contrast to deterministic splitting, i.e. nsplit
=0, where
all possible split points for each of the mtry
variables are
considered). The splitting rule is applied to these random split
points and the node is split on that variable and random split point
maximizing survival difference (as measured by the splitting rule).
A detailed study carried out by Ishwaran et al. (2008) found
"logrank
" and "logrankscore
" to be the most accurate in
terms of prediction error, followed by "conserve
". Setting
nsplit
=1 and using "logrank
" splitting gave performance
close to "logrank
", but with significantly shorter
computational times. Accuracy can be further improved without overly
compromising speed by using larger values of nsplit
.
Trees tend to favor splits on continuous variables (Loh and Shih,
1997), so it is good practice to use the nsplit
option when the
data contains a mix of continuous and discrete variables. Using a
reasonably small value mitigates bias.
—> Large Data Sets:
Computation times for very large data sets can be improved by
discretizing continuous variables and/or the observed survival times;
in addition to using random splitting. Discretization does not have
to be overly granular for substantial gains to be seen. Users may
also consider setting big.data
=TRUE for data with a large
number of variables. This bypasses the large overhead R needs to
create design matrices and parse formula. Be aware, however, that
variables are not processed and are interpreted as is under
this option. Think of the data frame as containing time and censoring
information and the rest of the data as the pre-processed design
matrix. In particular, transformations used in the formula (such as
logs etc.) are ignored.
—> Formula:
A typical RSF formula has the form Surv(time, censoring) ~
terms
, where "time
" is survival time and "censoring
" is a
binary censoring indicator. Censoring must be coded as a non-negative
integer with 0 reserved for censoring and (usually) 1=death (event).
Also, "time
" must be strictly positive.
—> Factors and Variable Types:
Variables encoded as factors are treated as such. If the factor is
ordered, then splits are similar to real valued variables. If the
factor is unordered, a split will move a subset of the levels in the
parent node to the left daughter, and the complementary subset to the
right daughter. All possible complementary pairs are considered and
apply to factors with an unlimited number of levels. However, there
is an optimization check to ensure that the number of splits attempted
is not greater than the number of cases in a node (this internal check
will override the nsplit
value in random splitting mode if
nsplit
is large enough). Note that when predicting on test
data involving factors, the factor labels in the test data must be the
same as in the grow (training) data. Consider setting labels that are
unique in the test data to missing to avoid issues.
Other than factors, all other x-variables are coerced and treated as being real valued.
—> Variable Importance:
Variable importance (VIMP) is computed similar to Breiman (2001),
although there are two ways to perturb a variable to determine its
VIMP: "randomsplit
", "permute
". The default method is
"randomsplit
" which works as follows. Out-of-bag (OOB) cases are
dropped down the in-bag (bootstrap) survival tree. A case is assigned
a daughter node randomly whenever an x
-split is encountered.
An OOB ensemble cumulative hazard function (CHF) is computed from the
forest of such trees and its OOB error rate calculated. The VIMP for
x
is the difference between this and the OOB error rate for the
original forest (without random node assignment using x
). If
"permute
" is used, then x
is randomly permuted in OOB data
and dropped down the in-bag tree. See Ishwaran et al. (2008) for
further details.
—> Predition Error:
Prediction error is measured by 1-C, where C is Harrell's concordance index. Prediction error is between 0 and 1, and measures how well the ensemble correctly ranks (classifies) two random individuals in terms of survival. A value of 0.5 is no better than random guessing. A value of 0 is perfect. Because VIMP is based on the concordance index, VIMP indicates how much misclassification increases, or decreases, for a new test case if a given variable were not available for that case (given that the forest was grown using that variable).
—> Competing Risks:
The implementation is similar to right-censoring but with the following caveats:
(1) Censoring must be coded as a non-negative integer where 0
indicates right-censoring and non-zero values indicate different event
types. While 0,1,2,..,J
is standard, events can be coded
non-sequentially, although 0 must always be used for censoring.
(2) The default splitting rule is "logrankCR
", a modified
log-rank splitting rule tailored for competing risks. Over-riding
this by manually selecting any split rule other than "logrankCR
"
or "random
" will result in a right-censored analysis in which all
(non-censored) events are treated as if they are one event type
(indeed, they will coerced as such). Note that nsplit
works as
in right-censoring.
(3) The ensemble (see below) is a 3-D array in which the 3rd dimension
is reserved for the ensemble CHF and each of the J
ensemble
conditional CHFs (CCHFs). The wrapper competing.risk
can be
used to process the ensemble and to generate event-specific cumulative
incidence functions (CIF) and subsurvival functions (see Gray (1988)
for background and definitions).
(4) The cases within a terminal node are used to estimate both the
unconditional survival function and the event-specific subsurvival
functions and for this reason nodesize
should generally be set
larger than in right-censored data settings.
—> Missing Data and Imputation:
Setting na.action
="na.impute
" implements a tree
imputation method whereby missing data (x-variables or outcomes) are
imputed dynamically as a tree is grown by randomly sampling from the
distribution within the current node (Ishwaran et al. 2008). OOB data
is not used in imputation to avoid biasing prediction error and VIMP
estimates. Final imputation for integer valued variables and
censoring indicators use a maximal class rule, whereas continuous
variables and survival time use a mean rule. Records in which all
outcome and x-variable information are missing are removed. Variables
having all missing values are removed. The algorithm can be iterated
by setting nimpute
to a positive integer greater than 1. A few
iterations should be used in heavy missing data settings to improve
accuracy of imputed values (see Ishwaran et al., 2008). Note if the
algorithm is iterated, a side effect is that missing values in the
returned objects predictors
, time
and cens
are
replaced by imputed values. Further, imputed objects such as
imputedData
are set to NULL. See the examples below. Also see
the wrapper impute.rsf
for a fast impute interface.
—> Miscellanea:
Setting varUsed
="all.trees
" returns a vector where each
element is a count of the number of times a split occurred on a
variable. If varUsed
="by.tree
", a matrix of size
ntree
x p
is returned. Each element [i][j] is the count
of the number of times a split occurred on variable [j] in tree [i].
Setting split.depth
=TRUE returns a matrix of size
n
x p
where entry [i][j] is the mean minimal depth for
variable [j] for case [i]. Used to select variables at the
case-level. See max.subtree
for more details regarding minimal
depth.
An object of class (rsf, grow)
with the following components:
call |
The original call to |
formula |
The formula used in the call. |
n |
Sample size of the data (depends upon NA's, see |
ndead |
Number of deaths. |
ntree |
Number of trees grown. |
mtry |
Number of variables randomly selected for splitting at each node. |
nodesize |
Minimum size of terminal nodes. |
splitrule |
Splitting rule used. |
nsplit |
Number of randomly selected split points. |
time |
Vector of length |
cens |
Vector of length |
timeInterest |
Sorted unique event times. Ensemble values are given for these time points only. |
predictorNames |
A character vector of the variable names used in growing the forest. |
predictorWt |
Vector of non-negative weights used for randomly sampling variables for splitting. |
predictors |
Data frame comprising x-variables used to grow the forest. |
ensemble |
Matrix for the in-bag ensemble CHF with each
row corresponding to an individual's CHF evaluated at each of
the time points in |
oob.ensemble |
Same as |
poe |
Matrix for the in-bag ensemble probability of an event (poe) for each individual: used to estimate the CIF. Rows correspond to each of the event types. Applies only to competing risk data. NULL otherwise. |
oob.poe |
Same as |
mortality |
A vector of length |
oob.mortality |
Same as |
err.rate |
Vector of length |
leaf.count |
Number of terminal nodes for each tree in the
forest. Vector of length |
importance |
Vector recording VIMP for each variable. For competing risks, a matrix with rows corresponding to the ensemble CHF and each of the CCHFs, respectively. |
forest |
If |
proximity |
If |
varUsed |
Count of the number of times a variable is used in growing the forest. Can be a vector, matrix, or NULL. |
imputedIndv |
Vector of indices for cases with missing values. Can be NULL. |
imputedData |
Data frame comprising imputed data. First two
columns are censoring and survival time, respectively.
Remaining columns are the x-variables. Row i contains imputed
outcomes and x-variables for row j of |
splitDepth |
Matrix of size |
The key deliverable is the matrix ensemble
(and its OOB
counterpart, oob.ensemble
) containing the ensemble CHF function
for each individual evaluated at a set of distinct time points. The
vector mortality
(likewise oob.mortality
) is a weighted
sum over the columns of ensemble
, weighted by the number of
individuals at risk at the different time points. Entry [i] of the
vector represents the estimated total mortality of individual i
in terms of total number of deaths. In other words, if i
has a
mortality value of 100, then if all individuals had the same x-values
as i
, there would be on average 100 deaths in the dataset.
Different R-wrappers are provided to aid in parsing the ensemble.
Hemant Ishwaran hemant.ishwaran@gmail.com
Udaya B. Kogalur kogalurshear@gmail.com
Breiman L. (2001). Random forests, Machine Learning, 45:5-32.
Cutler A. and Zhao G. (2001). Pert-Perfect random tree ensembles. Comp. Sci. Statist., 33: 490-497.
Gray R.J. (1988). A class of k-sample tests for comparing the cumulative incidence of a competing risk, Ann. Statist., 16:1141-1154.
Harrell F.E. et al. (1982). Evaluating the yield of medical tests, J. Amer. Med. Assoc., 247:2543-2546.
Hothorn T. and Lausen B. (2003). On the exact distribution of maximally selected rank statistics, Comp. Statist. Data Anal., 43:121-137.
Ishwaran H. (2007). Variable importance in binary regression trees and forests, Electronic J. Statist., 1:519-537.
Ishwaran H., Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.
Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests, Ann. App. Statist., 2:841-860.
Ishwaran H., Kogalur U.B., Gorodeski E.Z, Minn A.J. and Lauer M.S. (2010). High-dimensional variable selection for survival data. J. Amer. Statist. Assoc., 105:205-217.
Ishwaran H., Kogalur U.B., Chen X. and Minn A.J. (2010). Random survival forests for high-dimensional data.
Ishwaran H., Kogalur U.B., Moore R.D., Gange S.J. and Lau B.M. (2010). Random survival forests for competing risks.
LeBlanc M. and Crowley J. (1993). Survival trees by goodness of split, J. Amer. Statist. Assoc., 88:457-467.
Liaw A. and Wiener M. (2002). Classification and regression by randomForest, R News, 2:18-22.
Lin Y. and Jeon Y. (2006). Random forests and adaptive nearest neighbors, J. Amer. Statist. Assoc., 101:578-590.
Loh W.-Y and Shih Y.-S (1997). Split selection methods for classification trees, Statist. Sinica, 7:815-840, 1997.
Naftel N.C., Blackstone E.H. and Turner M.E. (1985). Conservation of events, unpublished notes.
Segal M.R. (1988). Regression trees for censored data, Biometrics, 44:35-47.
competing.risk
,
find.interaction
,
impute.rsf
,
max.subtree
,
plot.ensemble
,
plot.variable
,
plot.error
,
plot.proximity
,
pmml2rsf
,
predict.rsf
,
print.rsf
,
rsf2rfz
,
rsf2pmml
,
varSel
,
vimp
.
#------------------------------------------------------------ # Example 1: Veteran's Administration lung cancer data # Randomized trial of two treatment regimens for lung cancer # See Kalbfleisch & Prentice data(veteran, package = "randomSurvivalForest") veteran.out <- rsf(Surv(time, status) ~ ., data = veteran) print(veteran.out) plot(veteran.out) #------------------------------------------------------------ # Example 2: More detailed call (veteran data) #read the data, set various options data(veteran, package = "randomSurvivalForest") veteran.f <- as.formula(Surv(time, status) ~ .) ntree <- 200 nsplit <- 3 varUsed <- "by.tree" # coerce 'celltype' as a factor and 'karnofsky score' # as an ordered factor to illustrate factor useage veteran$celltype <- factor(veteran$celltype, labels=c("squamous", "smallcell", "adeno", "large")) veteran$karno <- factor(veteran$karno, ordered = TRUE) # grow call veteran2.out <- rsf(veteran.f, veteran, ntree = ntree, nsplit = nsplit, varUsed = varUsed) # plot of ensemble survival for a single individual surv.ensb <- t(exp(-veteran2.out$oob.ensemble)) plot(veteran2.out$timeInterest, surv.ensb[, 1]) # take a peek at the forest head(veteran2.out$forest$nativeArray) # average number of times a variable was split apply(veteran2.out$varUsed, 2, mean) # partial plot of top variable plot.variable(veteran2.out, partial = TRUE, npred = 1) ## Not run: #------------------------------------------------------------ # Example 3: Competing risks # Follicular Cell Lymphoma data(follic, package = "randomSurvivalForest") follic.out <- rsf(Surv(time, status) ~ ., follic, nsplit = 3, ntree = 400) print(follic.out) plot(follic.out, sorted = FALSE) competing.risk(follic.out) # Hodgkin's disease data(hd, package = "randomSurvivalForest") hd.out <- rsf(Surv(time, status) ~ ., hd, nsplit = 3, ntree = 400) print(hd.out) plot(hd.out, sorted = FALSE) competing.risk(hd.out) #------------------------------------------------------------ # Example 4: Primary biliary cirrhosis (PBC) of the liver # See Appendix D.1 of Fleming and Harrington data(pbc, package = "randomSurvivalForest") pbc.out <- rsf(Surv(days, status) ~ ., pbc, nsplit = 3) print(pbc.out) #------------------------------------------------------------ # Example 5: Same as Example 4, but with data imputation # Also see the R-wrapper "impute.rsf" # rsf call with imputation data(pbc, package = "randomSurvivalForest") pbc2.out <- rsf(Surv(days, status)~., pbc, nsplit = 3, na.action="na.impute") print(pbc2.out) # here's a nice wrapper to combine original data + imputed data combine.impute <- function(object) { imputed.data <- cbind(cens = object$cens, time = object$time, object$predictors) if (!is.null(object$imputedIndv)) { imputed.data[object$imputedIndv, ] <- object$imputedData } colnames(imputed.data)[c(2,1)] <- all.vars(object$formula)[1:2] imputed.data } # combine original data + imputed data pbc.imputed.data <- combine.impute(pbc2.out) # iterate the missing data algorithm # compare to non-iterated algorithm pbc3.out <- rsf(Surv(days, status)~., pbc, nsplit=5, na.action="na.impute", nimpute = 3) pbc.iterate.imputed.data <- combine.impute(pbc3.out) tail(pbc.imputed.data) tail(pbc.iterate.imputed.data) #------------------------------------------------------------ # Example 6: German breast cancer data # Variable selection using minimal depth data(breast, package = "randomSurvivalForest") breast.out <- rsf(Surv(time, cens) ~ . , breast, nsplit = 3) # use varSel to select variables # see the help file of varSel for details/examples breast.vs <- varSel(object=breast.out) #------------------------------------------------------------ # Example 7: Compare Cox regression to RSF using PBC data # OOB estimate of C-index for Cox based on 100 bootstraps # Assumes "Hmisc" and "survival" libraries are loaded if (library("survival", logical.return = TRUE) & library("Hmisc", logical.return = TRUE)) { data(pbc, package = "randomSurvivalForest") rsf.f <- as.formula(Surv(days, status) ~ .) pbc3.out <- rsf(rsf.f, pbc, nsplit = 10, mtry = 2) B <- 100 cox.err <- rep(NA, B) pbc.data <- pbc[apply(is.na(pbc), 1, sum) == 0,] ##remove NA's cat("Out-of-bag Cox Analysis ...", "\n") for (b in 1:B) { cat("Cox bootstrap:", b, "\n") bag.sample <- sample(1:nrow(pbc.data), nrow(pbc.data), replace = TRUE) oob.sample <- setdiff(1:nrow(pbc.data), bag.sample) train <- pbc.data[bag.sample,] test <- pbc.data[oob.sample,] cox.out <- tryCatch({coxph(rsf.f, train)}, error=function(ex){NULL}) if (is.list(cox.out)) { cox.predict <- predict(cox.out, test) cox.err[b] <- rcorr.cens(cox.predict, Surv(pbc.data$days[oob.sample], pbc.data$status[oob.sample]))[1] } } cat("Error rates:", "\n") cat("Random Survival Forests:", pbc3.out$err.rate[pbc3.out$ntree], "\n") cat(" Cox Regression:", mean(cox.err, na.rm = TRUE), "\n") } ## End(Not run)