predict.sbh: Predict Function

In jedazard/PRIMsrc: PRIM Survival Regression Classification

Predict Function

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, 
        groups = NULL,
        na.action = na.omit, ...)

Arguments

object	Object of class sbh as generated by the main function sbh.
newdata	A numeric matrix of same p input covariates as input data object$X. If newdata is a numeric data.frame, it is acceptable and will be coerced to a numeric matrix. Discrete nominal covariates will be treated as ordinal variables. NA missing values are not allowed. See details.
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.
groups	Same as sbh input: character or numeric vector, or factor of group membership indicator variable, automatically converted into a factor. It is of length the sample size, where entries take on group levels. Only two groups are allowed at this point. Defaults to NULL. See sbh for details.
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.

Matrix newdata is usually an independent dataset drawn from the same population as the input X dataset. It can also come from a split input dataset into two sets, usually for validation purposes.

Value

Object of class sbh (Patient Recursive Survival Peeling), where the cvfit list contains the usual values of sbh object.

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. (2021a). "Variable Selection Strategies for High-Dimensional Recursive Peeling-Based Survival Bump Hunting Models." (in prep).

• Dazard J-E. and Rao J.S. (2021b). "Group Bump Hunting by Recursive Peeling-Based Methods: Application to Survival/Risk Predictive Models." (in prep).

• Dazard J-E., Choe M., Pawitan Y., and Rao J.S. (2021c). "Identification and Characterization of Informative Prognostic Subgroups by Survival Bump Hunting." (in prep).

• Rao J.S., Huilin Y., and Dazard J-E. (2020). "Disparity Subtyping: Bringing Precision Medicine Closer to Disparity Science." Cancer Epidemiology Biomarkers & Prevention, 29(6 Suppl):C018.

• 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.

jedazard/PRIMsrc documentation built on July 16, 2022, 10:56 p.m.