PRIMsrc (version 0.6.0)

plot_boxtraj: Visualization of Peeling Trajectories/Profiles

Description

Function for plotting the cross-validated peeling trajectories/profiles of a PRSP 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 algorithm).

Usage

plot_boxtraj(object,
               main = NULL, 
               xlab = "Box Mass", 
               ylab = "Covariate Range",
               toplot = object$used,
               col.cov, 
               lty.cov, 
               lwd.cov,
               col = 1, 
               lty = 1, 
               lwd = 1, 
               cex = 1, 
               add.legend = FALSE, 
               text.legend = NULL, 
               nr = NULL, 
               nc = NULL,
               device = NULL, 
               file = "Covariate Trajectory Plots", 
               path=getwd(), 
               horizontal = FALSE, 
               width = 8.5, 
               height = 11.5, ...)

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 "Box Mass".

ylab

Character vector. Y axis label. Defaults to "Covariate Range".

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 t

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 n

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

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

Integer scalar. Line width for the trajectory curve of each statistical quantity of interest. Defaults to 1.

cex

Integer scalar. Symbol expansion used for titles, legends, and axis labels. Defaults to 1.

add.legend

Logical scalar. Should the legend be added to the current open graphics device? Defaults to FALSE.

text.legend

Character vector of legend 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 "Covariate 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 8.5.

...

Generic arguments passed to other plotting functions.

Value

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

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).

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." (Submitted).
• 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.

Examples

#===================================================
# Loading the library and its dependencies
#===================================================
library("PRIMsrc")

#=================================================================================
# Simulated dataset #1 (n=250, p=3)
# Replicated Combined Cross-Validation (RCCV)
# Peeling criterion = LRT
# Optimization criterion = LRT
#=================================================================================
CVCOMBREP.synt1 <- sbh(dataset = Synthetic.1, 
                       cvtype = "combined", cvcriterion = "lrt",
                       B = 1, K = 5, 
                       vs = TRUE, cpv = FALSE, probval = 0.5, 
                       arg = "beta=0.05,
                              alpha=0.1,
                              minn=10,
                              L=NULL,
                              peelcriterion="lr"",
                       parallel = FALSE, conf = NULL, seed = 123)

plot_boxtraj(object = CVCOMBREP.synt1,
             main = paste("RCCV peeling trajectories for model #1", sep=""),
             xlab = "Box Mass", ylab = "Covariate Range",
             toplot = CVCOMBREP.synt1$used,
             device = NULL, file = "Covariate Trajectory Plots", path=getwd(),
             horizontal = FALSE, width = 8.5, height = 8.5)