find.interaction {randomSurvivalForest} | R Documentation |
Test for pairwise interactions between predictors by comparing pairwise importance values to additive individual importance values.
find.interaction(x, sort = TRUE, predictorNames = NULL, n.pred = NULL, n.rep = 1, ...)
x |
An object of class (rsf, grow) . |
sort |
Should predictors be sorted by importance values (only
applies if importance values are available and
predictorNames =NULL)? Default is TRUE. |
predictorNames |
Character vector of predictor names. Only these predictors will be considered. Default is all. |
n.pred |
Number of predictors to be plotted (only applies when
predictorNames =NULL). Default is all. |
n.rep |
Number of Monte Carlo replicates. |
... |
Further arguments passed to or from other methods. |
Pairwise interactions between the first n.pred
predictors
are investigated (if sort
=TRUE analysis is based on the
first n.pred
as ordered by importance values). Predictors
are paired and then removed from the model. The increase in error
rate is computed and is defined to be the 'Paired' importance value
of the two predictors. The increase in error rate if each
predictor is removed individually is also computed and the sum of
these two values yields an 'Additive' importance value. The
difference between the 'Paired' and 'Additive' values can be used
to identify potential interactions. If the difference is positive,
and relatively large, the two predictors may have a tree-type
interaction.
Computations can be fairly heavy if the data is large, thus users
should consider setting n.pred
to a fairly small value to
guage computational times.
If n.rep
is greater than 1, the analysis is replicated
n.rep
times. Results reported are averaged values in this
case.
Invisibly, the interaction table.
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.
rsf
,
predict.rsf
.
data(veteran, package = "randomSurvivalForest") v.out <- rsf(Survrsf(time,status)~., veteran, ntree = 1000) find.interaction(v.out, n.pred = 3, n.rep=5)