An object of class (rfsrc, grow) or
(rfsrc, predict).
plots.one.page
Should plots be placed on one page?
show.plots
Should plots be displayed?
subset
Vector indicating which individuals we want estimates for.
All individuals are used if not specified.
collapse
Collapse the survival and cumulative hazard function
across the individuals specified by subset? Only applies
when subset is specified.
haz.model
Method for estimating the hazard. See details below.
Applies only when subset is specified.
k
The number of natural cubic spline knots used
for estimating the hazard function. Applies only when subset
is specified.
span
The fraction of the observations in the span of Friedman's
super-smoother used for estimating the hazard function. Applies only when
subset is specified.
cens.model
Method for estimating the censoring distribution
used in the inverse probability of censoring weights (IPCW) for the
Brier score:
km:
Uses the Kaplan-Meier estimator.
rfscr:
Uses random survival forests.
...
Further arguments passed to or from other methods.
Details
If subset is not specified, generates the following three
plots (going from top to bottom, left to right):
Forest estimated survival function for each individual (thick
red line is overall ensemble survival, thick green line is
Nelson-Aalen estimator).
Brier score (0=perfect, 1=poor, and 0.25=guessing) stratified
by ensemble mortality. Based on the IPCW method described in Gerds
et al. (2006). Stratification is into 4 groups corresponding to the
0-25, 25-50, 50-75 and 75-100 percentile values of mortality. Red
line is the overall (non-stratified) Brier score.
Plot of mortality of each individual versus observed time.
Points in blue correspond to events, black points are censored
observations.
When subset is specified, then for each individual in
subset, the following three plots are generated:
Forest estimated survival function.
Forest estimated cumulative hazard function (CHF) (displayed
using black lines). Blue lines are the CHF from the estimated hazard
function. See the next item.
A smoothed hazard function derived from the forest estimated
CHF (or survival function). The default method,
haz.model="spline", models the log CHF using natural cubic
splines as described in Royston and Parmar (2002). The lasso is
used for model selection, implemented using the glmnet
package (this package must be installed for this option to work).
If haz.model="ggamma", a three-parameter generalized gamma
distribution (using the parameterization described in Cox et al,
2007) is fit to the smoothed forest survival function, where
smoothing is imposed using Friedman's supersmoother (implemented by
supsmu). If haz.model="nonpar", Friedman's
supersmoother is applied to the forest estimated hazard function
(obtained by taking the crude derivative of the smoothed forest
CHF). Finally, setting haz.model="none" suppresses
hazard estimation and no hazard estimate is provided.
At this time, please note that all hazard estimates are considered
experimental and users should interpret the results with caution.
Note that when the object x is of class (rfsrc, predict)
not all plots will be produced. In particular, Brier scores are not
calculated.
Only applies to survival families. In particular, fails for competing
risk analyses. Use plot.competing.risk in such cases.
Whenever possible, out-of-bag (OOB) values are used.
Value
Invisibly, the conditional and unconditional Brier scores, and the
integrated Brier score (if they are available).
Author(s)
Hemant Ishwaran and Udaya B. Kogalur
References
Cox C., Chu, H., Schneider, M. F. and Munoz, A. (2007). Parametric
survival analysis and taxonomy of hazard functions for the
generalized gamma distribution. Statistics in Medicine
26:4252-4374.
Gerds T.A and Schumacher M. (2006). Consistent estimation of the
expected Brier score in general survival models with right-censored
event times, Biometrical J., 6:1029-1040.
Graf E., Schmoor C., Sauerbrei W. and Schumacher M. (1999).
Assessment and comparison of prognostic classification
schemes for survival data, Statist. in Medicine,
18:2529-2545.
Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R,
Rnews, 7(2):25-31.
Royston P. and Parmar M.K.B. (2002). Flexible parametric
proportional-hazards and proportional-odds models for censored
survival data, with application to prognostic modelling and
estimation of treatment effects, Statist. in Medicine,
21::2175-2197.
See Also
plot.competing.risk,
predict.rfsrc,
rfsrc
Examples
## Not run:
## veteran data
data(veteran, package = "randomForestSRC")
plot.survival(rfsrc(Surv(time, status)~ ., veteran), cens.model = "rfsrc")
## pbc data
data(pbc, package = "randomForestSRC")
pbc.obj <- rfsrc(Surv(days, status) ~ ., pbc, nsplit = 10)
# default spline approach
plot.survival(pbc.obj, subset = 3)
plot.survival(pbc.obj, subset = 3, k = 100)
# three-parameter generalized gamma is approximately the same
# but notice that its CHF estimate (blue line) is not as accurate
plot.survival(pbc.obj, subset = 3, haz.model = "ggamma")
# nonparametric method is too wiggly or undersmooths
plot.survival(pbc.obj, subset = 3, haz.model = "nonpar", span = 0.1)
plot.survival(pbc.obj, subset = 3, haz.model = "nonpar", span = 0.8)
## End(Not run)