2D Visualization of Data Scatter and Box Vertices

Description

S3-generic plotting function for two-dimensional visualization of original data as well as predicted data scatter with cross-validated box vertices of a PRSP object. The scatter plot is for a given peeling step of the peeling sequence and in a given plane of the used covariates of the PRSP object, both specified by the user.

Usage

  ## S3 method for class 'PRSP'
plot(x,
                      main = NULL,
                      proj = c(1,2), 
                      splom = TRUE, 
                      boxes = FALSE,
                      steps = x$cvfit$cv.nsteps,
                      pch = 16, 
                      cex = 0.5, 
                      col = 2:(length(steps)+1), 
                      col.box = 2:(length(steps)+1), 
                      lty.box = rep(2,length(steps)), 
                      lwd.box = rep(1,length(steps)),
                      add.legend = TRUE, 
                      device = NULL, 
                      file = "Scatter Plot", 
                      path=getwd(), 
                      horizontal = FALSE, 
                      width = 5, 
                      height = 5, ...)

Arguments

Details

The scatterplot is drawn on a graphical device with geometrically equal scales on the X and Y axes.

Value

Invisible. None. Displays the plot(s) on the specified device.

Note

End-user plotting function.

Author(s)

• "Jean-Eudes 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: "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

#===================================================
# 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)

plot(x = CVCOMB.synt1,
     main = paste("Scatter plot for model #1", sep=""),
     proj = c(1,2), splom = TRUE, boxes = TRUE,
     steps = CVCOMB.synt1$cvfit$cv.nsteps,
     pch = 16, cex = 0.5, col = 2,
     col.box = 2, lty.box = 2, lwd.box = 1,
     add.legend = TRUE, device = NULL)

Results

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/plot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot.PRSP
> ### Title: 2D Visualization of Data Scatter and Box Vertices
> ### Aliases: plot plot.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!
> 
> plot(x = CVCOMB.synt1,
+      main = paste("Scatter plot for model #1", sep=""),
+      proj = c(1,2), splom = TRUE, boxes = TRUE,
+      steps = CVCOMB.synt1$cvfit$cv.nsteps,
+      pch = 16, cex = 0.5, col = 2,
+      col.box = 2, lty.box = 2, lwd.box = 1,
+      add.legend = TRUE, device = NULL)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>