plot_traj: Visualization of Peeling Trajectories/Profiles

In jedazard/PRIMsrc: PRIM Survival Regression Classification

Visualization of Peeling Trajectories/Profiles

Description

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

Usage

  plot_traj(object,
            main = "Trajectory Plots", 
            toplot = object$cvfit$cv.used,
            range = NULL,
            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 "Trajectory Plots".
toplot	Numeric vector. Pre-selected covariates to plot (in reference to the original index of covariates). Defaults to covariates used for peeling.
range	List. User-specified ranges of survival output statistics according to the peeling criterion (peelcriterion) that is used, so that only relevant entries are to be entered. See details. Defaults to NULL, i.e. the full range of each statistic is going to be used.
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 shows peeling trajectories of some box descriptive summary statistics and survival output statistics as a function of box support (i.e. peeling steps). It plots peeling trajectories of those only covariates that are used for peeling. It also plots according to the peeling criterion (peelcriterion) that is used so that only relevant outputs are plotted. These outputs are: Size (remaining sample size n in the box), Maximum Event-Free Time (MEFT), Minimum Event-Free Probability (MEFP), Log-Hazard Ratio (LHR), Log-Rank Test (LRT), and Concordance Error Rate (CER) if peelcriterion in {"lhr", "lrt", "chs"}, or Group Log-Hazard Ratio (GLHR), and Group Concordance Error Rate (GCER) if peelcriterion = "grp".

The range list includes user-specified ranges of Log-Hazard Ratio (LHR), Log-Rank Test (LRT), and Concordance Error Rate (CER) if peelcriterion in {"lhr", "lrt", "chs"}, or Group Log-Hazard Ratio (GLHR), and Group Concordance Error Rate (GCER) if peelcriterion = "grp". In the former case, the list should be of the form range = list("lhr"=..., "lrt"=..., "cer"=...) In the latter case, the list should be of the form range = list("glhr"=..., "gcer"=...).

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. (2021a). "Variable Selection Strategies for High-Dimensional Recursive Peeling-Based Survival Bump Hunting Models." (in prep).

• Dazard J-E. and Rao J.S. (2021b). "Group Bump Hunting by Recursive Peeling-Based Methods: Application to Survival/Risk Predictive Models." (in prep).

• Dazard J-E., Choe M., Pawitan Y., and Rao J.S. (2021c). "Identification and Characterization of Informative Prognostic Subgroups by Survival Bump Hunting." (in prep).

• Rao J.S., Huilin Y., and Dazard J-E. (2020). "Disparity Subtyping: Bringing Precision Medicine Closer to Disparity Science." Cancer Epidemiology Biomarkers & Prevention, 29(6 Suppl):C018.

• 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.

jedazard/PRIMsrc documentation built on July 16, 2022, 10:56 p.m.