find.interaction {randomSurvivalForest}R Documentation

Find Interactions Between Pairs of Variables

Description

Test for pairwise interactions between variables by comparing pairwise importance values to additive individual importance values.

Usage

    find.interaction(x,
                  sort = TRUE,
                  predictorNames = NULL,
                  n.pred = NULL,
                  n.rep = 1,
                  ...)

Arguments

x An object of class (rsf, grow).
sort Should variables be sorted by importance values (only applies if importance values are available and predictorNames=NULL)? Default is TRUE.
predictorNames Character vector of variable names. Only these variables will be considered. Default is all.
n.pred Number of variables 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.

Details

Pairwise interactions between the first n.pred variables are investigated (if sort=TRUE analysis is based on the first n.pred as ordered by importance values). Variables 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 variables. The increase in error rate if each variable is removed individually is also computed and the sum of these two values yields an 'Additive' importance value. Large positive or negative differences between 'Paired' and 'Additive' values indicate potentially interesting associations.

Computations can be fairly heavy if the data is large, thus users should consider setting n.pred to a fairly small value to gauge computational times.

If n.rep is greater than 1, the analysis is replicated n.rep times. Results reported are averaged values in this case.

Value

Invisibly, the interaction table.

Author(s)

Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu

References

H. Ishwaran (2007). Variable importance in binary trees. Cleveland Clinic Technical Report.

See Also

rsf, predict.rsf.

Examples

  data(veteran, package = "randomSurvivalForest") 
  v.out <- rsf(Survrsf(time,status)~., veteran, ntree = 1000)
  find.interaction(v.out, n.pred = 2, n.rep=1)

[Package randomSurvivalForest version 3.0.1 Index]