Visualization of Survival Distributions

Description

Function for plotting the cross-validated survival distributions of a PRSP object. Plot the cross-validated Kaplan-Meir estimates of survival distributions for the highest risk (inbox) versus lower-risk (outbox) groups of samples at each iteration of the peeling sequence (inner loop of our PRSP algorithm).

Usage

  plot_boxkm(object,
             main = NULL, 
             xlab = "Time", 
             ylab = "Probability",
             precision = 1e-3, 
             mark = 3, 
             col = 2, 
             cex = 1,
             steps = 1:object$cvfit$cv.nsteps,
             nr = 3, 
             nc = 4,
             device = NULL, 
             file = "Survival Plots", 
             path=getwd(), 
             horizontal = TRUE, 
             width = 11, 
             height = 8.5, ...)

Arguments

Details

Some of the plotting parameters are further defined in the function plot.survfit (R package survival). Step #0 always corresponds to the situation where the starting box covers the entire test-set data before peeling. Cross-validated LRT, LHR of inbox samples and log-rank p-values of separation are shown at the bottom of the plot with the corresponding peeling step. P-values are lower-bounded by the precision limit given by 1/A, where A is the number of permutations.

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.

See Also

• plot.survfit (R package survival)

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_boxkm(object = CVCOMB.synt1,
           main = paste("Cross-validated probability curves for model #1", sep=""),
           xlab = "Time", ylab = "Probability",
           device = NULL, file = "Survival Plots", path=getwd(),
           horizontal = TRUE, width = 11, 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_boxkm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot_boxkm
> ### Title: Visualization of Survival Distributions
> ### Aliases: plot_boxkm
> ### 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_boxkm(object = CVCOMB.synt1,
+            main = paste("Cross-validated probability curves for model #1", sep=""),
+            xlab = "Time", ylab = "Probability",
+            device = NULL, file = "Survival Plots", path=getwd(),
+            horizontal = TRUE, width = 11, height = 8.5)
Device:  2 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>