S3-method predict function to predict the box membership and box vertices
on an independent set, using a cross-validated sbh fitted object.
# S3 method for sbh
predict(object,
newdata,
steps = 1:object$cvfit$cv.nsteps,
na.action = na.omit, ...)Object of class sbh as generated by the main function sbh.
Either a numeric matrix or numeric vector containing the new input data of same dimensionality as
the final sbh object of used covariates. A vector will be transformed to a (\#sample x 1)
matrix.
Integer vector. Vector of peeling steps at which to predict the box memberships
and box vertices. Defaults to all the peeling steps of sbh object object.
A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of incomplete cases.
Further generic arguments passed to the predict function.
List containing the following 2 fields:
Logical matrix of predicted box membership indicator (columns) by peeling steps (rows).
TRUE = in-box, FALSE = out-of-box.
List of size the number of chosen peeling steps where each entry is a numeric matrix of
predicted box vertices: lower and upper bounds (rows) by covariate (columns).
This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. This project was partially funded by the National Institutes of Health NIH - National Cancer Institute (R01-CA160593) to J-E. Dazard and J.S. Rao.
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Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2014). "Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods." In JSM Proceedings, Survival Methods for Risk Estimation/Prediction Section. Boston, MA, USA. American Statistical Association IMS - JSM, p. 3366-3380.
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