S3-generic predict function to predict the box membership and box vertices
on an independent set.
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
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
Integervector. 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
Logicalmatrix 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 numericmatrix of
predicted box vertices: lower and upper bounds (rows) by covariate (columns).
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.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(PRIMsrc)
Loading required package: parallel
Loading required package: survival
Loading required package: Hmisc
Loading required package: lattice
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'Hmisc'
The following objects are masked from 'package:base':
format.pval, round.POSIXt, trunc.POSIXt, units
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-5
Loading required package: MASS
PRIMsrc 0.6.3
Type PRIMsrc.news() to see new features, changes, and bug fixes
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/PRIMsrc/predict.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predict.PRSP
> ### Title: Predict Function
> ### Aliases: predict predict.PRSP
> ### Keywords: Exploratory Survival/Risk Analysis Survival/Risk Estimation &
> ### Prediction Non-Parametric Method Cross-Validation Bump Hunting
> ### Rule-Induction Method
>
> ### ** Examples
>
> #===================================================
> # 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)
Survival dataset provided.
Requested single 5-fold cross-validation without replications
Cross-validation technique: COMBINED
Cross-validation criterion: LRT
Variable pre-selection: TRUE
Computation of permutation p-values: FALSE
Peeling criterion: LRT
Parallelization: FALSE
Pre-selection of covariates and determination of directions of peeling...
Pre-selected covariates:
X1 X2 X3
1 2 3
Directions of peeling at each step of pre-selected covariates:
X1 X2 X3
1 -1 -1
Fitting and cross-validating the Survival Bump Hunting model using the PRSP algorithm ...
replicate : 1
seed : 123
Fold : 1
Fold : 2
Fold : 3
Fold : 4
Fold : 5
Success! 1 (replicated) cross-validation(s) has(ve) completed
Generating cross-validated optimal peeling lengths from all replicates ...
Generating cross-validated box memberships at each step ...
Generating cross-validated box rules for the pre-selected covariates at each step ...
Generating cross-validated modal trace values of covariate usage at each step ...
Covariates used for peeling at each step, based on covariate trace modal values:
X1 X2
1 2
Generating cross-validated box statistics at each step ...
Finished!
>
> 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)
>
>
>
>
>
> dev.off()
null device
1
>