predict.sbh: Predict Function

In PRIMsrc: PRIM Survival Regression Classification

Description

S3-method predict function to predict the box membership and box vertices on an independent set, using a cross-validated sbh fitted object.

Usage

  ## S3 method for class 'sbh'
predict(object, 
        newdata, 
        steps = 1:object$cvfit$cv.nsteps, 
        na.action = na.omit, ...)

Arguments

object	Object of class sbh as generated by the main function sbh.
newdata	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.
steps	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.
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.

Details

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.

Value

List containing the following 5 fields:

boxind	Logical matrix of predicted box membership indicator (columns) by peeling steps (rows). TRUE = inbox, FALSE = outbox.
vertices	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).
rules	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).
sign	numeric vector in {-1,+1} of directions of peeling for all used (selected) covariates.
used	numeric vector of covariates used (selected) for peeling, indexed in reference to original index.

Acknowledgments

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.

Note

End-user predict function.

Author(s)

• "Jean-Eudes Dazard, Ph.D." [e-mail]

• "Michael Choe, M.D." [e-mail]

• "Michael LeBlanc, Ph.D." [e-mail]

• "Alberto Santana, MBA." [e-mail]

• "J. Sunil Rao, Ph.D." [e-mail]

Maintainer: "Jean-Eudes Dazard, Ph.D." [e-mail]

References

• Dazard J-E. and Rao J.S. (2018). "Variable Selection Strategies for High-Dimensional Survival Bump Hunting using Recursive Peeling Methods." (in prep).

• Rao J.S., Huilin Y. and Dazard J-E. (2018). "Disparity Subtyping: Bringing Precision Medicine Closer to Disparity Science." (in prep).

• Diaz-Pachon D.A., Saenz J.P., Dazard J-E. and Rao J.S. (2018). "Mode Hunting through Active Information." (in press).

• Diaz-Pachon D.A., Dazard J-E. and Rao J.S. (2017). "Unsupervised Bump Hunting Using Principal Components." In: Ahmed SE, editor. Big and Complex Data Analysis: Methodologies and Applications. Contributions to Statistics, vol. Edited Refereed Volume. Springer International Publishing, Cham Switzerland, p. 325-345.

• Yi C. and Huang J. (2017). "Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression." J. Comp Graph. Statistics, 26(3):547-557.

• Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2016). "Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods." Statistical Analysis and Data Mining, 9(1):12-42.

• Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2015). "R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification." In JSM Proceedings, Statistical Programmers and Analysts Section. Seattle, WA, USA. American Statistical Association IMS - JSM, p. 650-664.

• 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.

PRIMsrc documentation built on May 2, 2019, 11:45 a.m.