plot_traj: Visualization of Peeling Trajectories/Profiles

In PRIMsrc: PRIM Survival Regression Classification

Description

Function for plotting the cross-validated peeling trajectories/profiles of a sbh object. Applies to the pre-selected covariates specified by user and all other statistical quantities of interest at each iteration of the peeling sequence (inner loop of our PRSP or PRGSP algorithm).

Usage

  plot_traj(object,
            main = NULL, 
            toplot = object$cvfit$cv.used,
            col.cov, 
            lty.cov, 
            lwd.cov,
            col = 1, 
            lty = 1, 
            lwd = 0.5, 
            cex = 0.5, 
            add.caption = FALSE, 
            text.caption = NULL, 
            nr = NULL, 
            nc = NULL,
            device = NULL, 
            file = "Trajectory Plots", 
            path = getwd(), 
            horizontal = FALSE, 
            width = 8.5, 
            height = 11, ...)

Arguments

object	Object of class sbh as generated by the main function sbh.
main	Character vector. Main Title. Defaults to NULL.
toplot	Numeric vector. Which of the pre-selected covariates to plot (in reference to the original index of covariates). Defaults to covariates used for peeling.
col.cov	Integer vector. Line color for the covariate trajectory curve of each selected covariate. Defaults to vector of colors of length the number of selected covariates. The vector is reused cyclically if it is shorter than the number of selected covariates.
lty.cov	Integer vector. Line type for the covariate trajectory curve of each selected covariate. Defaults to vector of 1's of length the number of selected covariates. The vector is reused cyclically if it is shorter than the number of selected covariates.
lwd.cov	Integer vector. Line width for the covariate trajectory curve of each selected covariate. Defaults to vector of 1's of length the number of selected covariates. The vector is reused cyclically if it is shorter than the number of selected covariates.
col	Integer scalar. Line color for the trajectory curve of each statistical quantity of interest. Defaults to 1.
lty	Integer scalar. Line type for the trajectory curve of each statistical quantity of interest. Defaults to 1.
lwd	Numeric scalar. Line width for the trajectory curve of each statistical quantity of interest. Defaults to 0.5.
cex	Numeric scalar. Symbol expansion used for titles, captions, and axis labels. Defaults to 0.5.
add.caption	Logical scalar. Should the caption be plotted? Defaults to FALSE.
text.caption	Character vector of caption content. Defaults to NULL.
nr	Integer scalar of the number of rows in the plot. If NULL, defaults to 3.
nc	Integer scalar of the number of columns in the plot. If NULL, defaults to 3.
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 "Trajectory Plots".
path	Absolute path (without final (back)slash separator). Defaults to working directory path.
horizontal	Logical scalar. Orientation of the printed image. Defaults to FALSE, that is potrait orientation.
width	Numeric scalar. Width of the graphics region in inches. Defaults to 8.5.
height	Numeric scalar. Height of the graphics region in inches. Defaults to 11.
...	Generic arguments passed to other plotting functions.

Details

The plot limits the display of trajectories to those only covariates that are used for peeling.

The plot includes box descriptive statistics (such as support), survival endpoint statistics (such as Maximum Event-Free Time (MEFT), Minimum Event-Free Probability (MEVP), LHR, LRT) and prediction performance (such as CER).

Value

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

Acknowledgments

This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. 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.

Note

End-user plotting function.

Author(s)

• "Jean-Eudes Dazard, Ph.D." [e-mail]

• "Michael Choe, M.D." [e-mail]

• "Michael LeBlanc, Ph.D." [e-mail]

• "Alberto Santana, MBA." [e-mail]

• "J. Sunil Rao, Ph.D." [e-mail]

Maintainer: "Jean-Eudes Dazard, Ph.D." [e-mail]

References

• Dazard J-E. and Rao J.S. (2018). "Variable Selection Strategies for High-Dimensional Survival Bump Hunting using Recursive Peeling Methods." (in prep).

• Rao J.S., Huilin Y. and Dazard J-E. (2018). "Disparity Subtyping: Bringing Precision Medicine Closer to Disparity Science." (in prep).

• Diaz-Pachon D.A., Saenz J.P., Dazard J-E. and Rao J.S. (2018). "Mode Hunting through Active Information." (in press).

• Diaz-Pachon D.A., Dazard J-E. and Rao J.S. (2017). "Unsupervised Bump Hunting Using Principal Components." In: Ahmed SE, editor. Big and Complex Data Analysis: Methodologies and Applications. Contributions to Statistics, vol. Edited Refereed Volume. Springer International Publishing, Cham Switzerland, p. 325-345.

• Yi C. and Huang J. (2017). "Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression." J. Comp Graph. Statistics, 26(3):547-557.

• Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2016). "Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods." Statistical Analysis and Data Mining, 9(1):12-42.

• 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, p. 650-664.

• 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. and J.S. Rao (2010). "Local Sparse Bump Hunting." J. Comp Graph. Statistics, 19(4):900-92.

PRIMsrc documentation built on May 2, 2019, 11:45 a.m.