Predict Function

Description

S3-generic predict function to predict the box membership and box vertices on an independent set.

Usage

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  ## 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

Either a numeric matrix or numeric vector containing the new input data of same dimensionality as the final PRSP object of used covariates. 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:

boxind

Logical matrix of predicted box membership indicator (columns) by peeling steps (rows). TRUE = in-box, FALSE = out-of-box.

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).

Note

End-user predict function.

Author(s)

Maintainer: "Jean-Eudes Dazard, Ph.D." jxd101@case.edu

Acknowledgments: 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.

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." Statistical Analysis and Data Mining (in press).

  • 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., 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, (in press).

  • Dazard J-E. and J.S. Rao (2010). "Local Sparse Bump Hunting." J. Comp Graph. Statistics, 19(4):900-92.

Examples

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#===================================================
# Loading the library and its dependencies
#===================================================
library("PRIMsrc")

#=================================================================================
# Simulated dataset #1 (n=250, p=3)
# Non Replicated Combined Cross-Validation (RCCV)
# Peeling criterion = LRT
# Optimization criterion = LRT
#=================================================================================
CVCOMB.synt1 <- sbh(dataset = Synthetic.1, 
                    cvtype = "combined", cvcriterion = "lrt",
                    B = 1, K = 5, 
                    vs = TRUE, cpv = FALSE, 
                    decimals = 2, probval = 0.5, 
                    arg = "beta=0.05,
                           alpha=0.1,
                           minn=10,
                           L=NULL,
                           peelcriterion=\"lr\"",
                    parallel = FALSE, conf = NULL, seed = 123)

n <- 100
p <- length(CVCOMB.synt1$cvfit$cv.used)
x <- matrix(data=runif(n=n*p, min=0, max=1),
            nrow=n, ncol=p, byrow=FALSE,
            dimnames=list(1:n, paste("X", 1:p, sep="")))
CVCOMB.pred <- predict(object=CVCOMB.synt1,
                       newdata=x,
                       steps=CVCOMB.synt1$cvfit$cv.nsteps)