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

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  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

object

Object of class PRSP as generated by the main function sbh.

main

Character vector. Main Title. Defaults to NULL.

xlab

Character vector. X axis label. Defaults to "Time".

ylab

Character vector. Y axis label. Defaults to "Probability".

precision

Precision of cross-validated log-rank p-values of separation between two survival curves. Defaults to 1e-3.

mark

Integer scalar of mark parameter, which will be used to label the inbox and out-of-box curves. Defaults to 3.

col

Integer scalar specifying the color of the inbox curve. Defaults to 2.

cex

Numeric scalar specifying the size of the marks. Defaults to 1.

steps

Integer vector. Vector of peeling steps at which to plot the survival curves. Defaults to all the peeling steps of PRSP object object.

nr

Integer scalar of the number of rows in the plot. Defaults to 3.

nc

Integer scalar of the number of columns in the plot. Defaults to 4.

device

Graphic display device in {NULL, "PS", "PDF"}. Defaults to NULL (standard output screen). Currently implemented graphic display devices are "PS" (Postscript) or "PDF" (Portable Document Format).

file

File name for output graphic. Defaults to "Survival Plots".

path

Absolute path (without final (back)slash separator). Defaults to the working directory path.

horizontal

Logical scalar. Orientation of the printed image. Defaults to TRUE, that is potrait orientation.

width

Numeric scalar. Width of the graphics region in inches. Defaults to 11.

height

Numeric scalar. Height of the graphics region in inches. Defaults to 8.5.

...

Generic arguments passed to other plotting functions, including plot.survfit (R package survival).

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)

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

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