S3generic predict function to predict the box membership and box vertices on an independent set.
1 2 3 4 5 
object 
Object of class 
newdata 
Either a numeric matrix or numeric vector containing the new input data of same dimensionality as
the final 
steps 

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. 
List
containing the following 2 fields:
boxind 

vertices 

Enduser predict function.
"JeanEudes Dazard, Ph.D." jxd101@case.edu
"Michael Choe, M.D." mjc206@case.edu
"Michael LeBlanc, Ph.D." mleblanc@fhcrc.org
"Alberto Santana, MBA." ahs4@case.edu
Maintainer: "JeanEudes Dazard, Ph.D." jxd101@case.edu
Acknowledgments: This project was partially funded by the National Institutes of Health NIH  National Cancer Institute (R01CA160593) to JE. Dazard and J.S. Rao.
Dazard JE., Choe M., LeBlanc M. and Rao J.S. (2015). "Crossvalidation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods." Statistical Analysis and Data Mining (in press).
Dazard JE., Choe M., LeBlanc M. and Rao J.S. (2014). "CrossValidation 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. 33663380.
Dazard JE., 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 JE. and J.S. Rao (2010). "Local Sparse Bump Hunting." J. Comp Graph. Statistics, 19(4):90092.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  #===================================================
# Loading the library and its dependencies
#===================================================
library("PRIMsrc")
#=================================================================================
# Simulated dataset #1 (n=250, p=3)
# Non Replicated Combined CrossValidation (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)

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