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.
A numeric matrix containing the new input data of same format as input data object$X.
If not a matrix, newdata will be coerced to a 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 5 fields:
Logical matrix of predicted box membership indicator (columns) by peeling steps (rows).
TRUE = inbox, FALSE = outbox.
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).
List of size the number of chosen peeling steps, where each entry is a numeric matrix of
decision rules on the covariates (columns) for all peeling steps (rows).
numeric vector in {-1,+1} of directions of peeling for all used (selected) covariates.
numeric vector of covariates used (selected) for peeling, indexed in reference to original index.
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.
Only the used covariates of the final sbh object will be retained for the covariates of newdata.
So, the used covariates of sbh object must be equal or a subset of the
the covariates of newdata.
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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.
Dazard J-E. and J.S. Rao (2010). "Local Sparse Bump Hunting." J. Comp Graph. Statistics, 19(4):900-92.