Visualization of Covariates Traces

Description

Function for plotting the cross-validated covariates traces of a PRSP object. Plot the cross-validated modal trace curves of covariate importance and covariate usage of the pre-selected covariates specified by user at each iteration of the peeling sequence (inner loop of our PRSP algorithm).

Usage

  plot_boxtrace(object,
                main = NULL,
                xlab = "Box Mass", 
                ylab = "Covariate Range (centered)",
                toplot = object$cvfit$cv.used,
                center = TRUE, 
                scale = FALSE, 
                col.cov, 
                lty.cov, 
                lwd.cov, 
                col = 1, 
                lty = 1, 
                lwd = 1, 
                cex = 1, 
                add.legend = FALSE, 
                text.legend = NULL,
                device = NULL, 
                file = "Covariate Trace Plots", 
                path=getwd(), 
                horizontal = FALSE, 
                width = 8.5, 
                height = 8.5, ...)

Arguments

NULL

Details

The trace plots limit the display of traces to those only covariates that are used for peeling. If centered, an horizontal black dotted line about 0 is added to the plot.

Due to the variability induced by cross-validation and replication, it is possible that more than one covariate be used for peeling at a given step. So, for simplicity of the trace plots, only the modal or majority vote trace value (over the folds and replications of the cross-validation) is plotted.

The top plot shows the overlay of covariate importance curves for each covariate. The bottom plot shows the overlay of covariate usage curves for each covariate. It is a dicretized view of covariate importance.

Both point to the magnitude and order with which covariates are used along the peeling sequence.

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_boxtrace(object = CVCOMB.synt1,
              main = paste("Cross-validated trace plots for model #1", sep=""),
              xlab = "Box Mass", ylab = "Covariate Range (centered)",
              toplot = CVCOMB.synt1$cvfit$cv.used,
              center = TRUE, scale = FALSE,
              device = NULL, file = "Covariate Trace Plots", path=getwd(),
              horizontal = FALSE, width = 8.5, height = 8.5)

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_boxtrace.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot_boxtrace
> ### Title: Visualization of Covariates Traces
> ### Aliases: plot_boxtrace
> ### 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_boxtrace(object = CVCOMB.synt1,
+               main = paste("Cross-validated trace plots for model #1", sep=""),
+               xlab = "Box Mass", ylab = "Covariate Range (centered)",
+               toplot = CVCOMB.synt1$cvfit$cv.used,
+               center = TRUE, scale = FALSE,
+               device = NULL, file = "Covariate Trace Plots", path=getwd(),
+               horizontal = FALSE, width = 8.5, height = 8.5)
Device:  2 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>