Learn R Programming
PRIMsrc (version 0.6.0)

predict.PRSP: Predict Function

Description

S3-generic predict function to predict the box membership and box vertices on an independent set from a PRSP object trained by a SBH model.

Usage

## S3 method for class 'PRSP':
predict(object, newdata, steps, na.action = na.omit, ...)

Arguments

object
Object of class PRSP as generated by the main function sbh.
newdata
An object containing the new input data: either a numeric matrix or numeric vector. A vector will be transformed to a (#sample x 1) matrix.
steps
Integer vector. Vector of peeling steps at which to predict the box memberships and box vertices. Defaults to the last peeling step only.
na.action
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.

Value

  • List containing the following 2 fields:
  • boxindLogical matrix of predicted box membership indicator (columns) by peeling steps (rows). TRUE = in-box, FALSE = out-of-box.
  • verticesList 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).

References

  • Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2015). "Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods." (Submitted).
  • 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.