plot.variable {randomSurvivalForest} | R Documentation |
Plot of ensemble mortality for each predictor. Users can select between marginal and partial plots.
plot.variable(x, plots.per.page = 4, granule = 5, sort = TRUE, freq = TRUE, partial = FALSE, predictorNames = NULL, n.pred = NULL, n.pts = 25, ...)
x |
An object of class (rsf, grow) or (rsf,
predict) . |
plots.per.page |
Integer value controlling page layout. |
granule |
Integer value controlling whether a plot for a specific predictor should be given as a boxplot or scatter plot. Larger values coerce boxplots. |
sort |
Should predictors be sorted by importance values (only applies if importance values are available)? Default is TRUE. |
freq |
Should y-axis be in terms of expected number of deaths (the default), or should relative frequencies be used? |
partial |
Logical. Should partial plots be created? Default is FALSE. |
predictorNames |
Character vector of predictor names. Only these predictors will be plotted. Default is all. |
n.pred |
Number of predictors to be plotted (only applies when
predictorNames =NULL). Default is all. |
n.pts |
Maximum number of points used when generating partial plots for continuous predictors. |
... |
Further arguments passed to or from other methods. |
Ensemble mortality is plotted against the value of a predictor.
Ensemble mortality values (vertical axis) should be interpreted in
terms of total number of deaths. For example, if individual
i
has a mortality value of 100, then if all individuals had
the same predictor as i
, there would be 100 deaths in the
dataset (on average). Note that if freq
=FALSE, then
mortality values are divided by the sample size and the y-axis
records relative frequencies (i.e. the expected fraction of deaths
in the sample).
Default is to create marginal plots. Thus, each point represents
the estimated mortality of an individual i
against the value
for i
's predictor. For continuous predictors, points are
colored so that blue corresponds to events, whereas black points
represent censored observations.
Partial plots are created when partial
=TRUE. In this
case, the mortality value being plotted for a predictor X
evaluated at X=x is
tilde{f}(x) = frac{1}{n} sum_{i=1}^n hat{f}(x, x_{i,O}),
where x_{i,O} represents the value for all other predictors
other than X for individual i and hat{f} is the
ensemble mortality predictor. Generating partial plots can be very
slow. Choosing a small value for n.pts
can speed up
computational times as this restricts the number of distinct x
values used in computing tilde{f}.
For continuous predictors, red points are used to indicate partial values and dashed red lines represent a lowess smoothed error bar of +/- two standard errors. Black dashed line is the lowess estimate of the partial values. For discrete predictors, partial values are indicated using boxplots with whiskers extending out approximately two standard errors from the mean. Standard errors are meant only to be a guide and should be interpreted with caution.
Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu
H. Ishwaran and Udaya B. Kogalur (2006). Random Survival Forests. Cleveland Clinic Technical Report.
J.H. Friedman (2001). Greedy function approximation: a gradient boosting machine, Ann. of Stat., 5:1189-1232.
A. Liaw and M. Wiener (2002). Classification and regression by randomForest, R News, 2:18-22.
rsf
,
predict.rsf
.
data(veteran, package = "randomSurvivalForest") v.out <- rsf(Survrsf(time,status)~., veteran, forest = TRUE, ntree = 1000) plot.variable(v.out, plots.per.page = 3) plot.variable(v.out, plots.per.page = 2, predictorNames = c("trt", "karno", "age")) plot.variable(v.out, freq = TRUE, partial = TRUE, plots.per.page = 2, n.pred=3)