PRIMsrc (version 0.5.8)

plot_boxtrace: Visualization of Covariates Traces

Description

S3 generic function for plotting the cross-validated covariates traces of a PRSP object. Plot the cross-validated modal trace curves of covariate importance and covariate usage of the user-specified covariates among the pre-selected ones at each iteration of the peeling sequence (inner loop of our PRSP algorithm).

Usage

plot_boxtrace(object,
                main = NULL,
                xlab = "Box Mass", 
                ylab = "Covariate Range (centered)",
                toplot = object$used,
                center = TRUE, 
                scale = FALSE, 
                col.cov, 
                lty.cov, 
                lwd.cov, 
                col = 1, 
                lty = 1, 
                lwd = 1, 
                cex = 1, 
                add.legend = FALSE, 
                text.legend = NULL,
                device = NULL, 
                file = "Covariate Trace Plots", 
                path=getwd(), 
                horizontal = FALSE, 
                width = 8.5, 
                height = 8.5, ...)

Arguments

object

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

main

Character vector. Main Title. Defaults to.

xlab

Character vector. X axis label. Defaults to "Box Mass".

ylab

Character vector. Y axis label. Defaults to "Covariate Range (centered)".

toplot

Numeric vector. Which of the pre-selected covariates to plot. Defaults to covariates used for peeling.

center

Logical scalar. Shall the data be centered?. Defaults to TRUE.

scale

Logical scalar. Shall the data be scaled? Defaults to FALSE.

col.cov

Integer vector. Line color for the covariate importance 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 importance 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 importance 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 covariate trace curve. Defaults to 1.

lty

Integer scalar. Line type for the covariate trace curve. Defaults to 1.

lwd

Integer scalar. Line width for the covariate trace curve. 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.

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 Trace 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 trace plots limit the display of traces to those only covariates that are used for peeling. If centered, an horizontal black dotted line about 0 is added to the plot. Due to the variability induced by cross-validation and replication, it is possible that more than one covariate be used for peeling at a given step. So, for simplicity of the trace plots, only the modal or majority vote trace value (over the folds and replications of the cross-validation) is plotted. The top plot shows the overlay of covariate importance curves for each covariate. The bottom plot shows the overlay of covariate usage curves for each covariate. It is a dicretized view of covariate importance. Both point to the magnitude and order with which covariates are used along the peeling sequence.

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_boxtrace(object = CVCOMBREP.synt1,
              main = paste("RCCV trace plots for model #1", sep=""),
              xlab = "Box Mass", ylab = "Covariate Range (centered)",
              toplot = CVCOMBREP.synt1$used,
              center = TRUE, scale = FALSE,
              device = NULL, file = "Covariate Trace Plots", path=getwd(),
              horizontal = FALSE, width = 8.5, height = 8.5)