S3-method plot function for two-dimensional visualization of original data as well as
predicted data scatter with cross-validated box vertices of a sbh object.
The scatter plot is for a given peeling step of the peeling sequence and in a given plane of
the used covariates of the sbh object, both specified by the user.
# S3 method for sbh
plot(x,
main = NULL,
proj = c(1,2),
splom = TRUE,
boxes = FALSE,
steps = 1:x$cvfit$cv.nsteps,
pch = 16,
cex = 0.5,
col = 2:(length(steps)+1),
col.box = 2:(length(steps)+1),
lty.box = rep(2,length(steps)),
lwd.box = rep(1,length(steps)),
add.legend = TRUE,
device = NULL,
file = "Scatter Plot",
path = getwd(),
horizontal = FALSE,
width = 5,
height = 5, ...)Object of class sbh as generated by the main function sbh.
Character vector. Main Title. Defaults to NULL.
Integer vector of length two, specifying the two dimensions of the projection plane of
of the used covariates of the sbh object. Defaults to first two dimensions: {1,2}.
Logical scalar. Shall the scatter plot of points inside the box(es) be plotted? Default to TRUE.
Logical scalar. Shall the box vertices be plotted or just the scatter of points? Default to FALSE.
Integer vector. Vector of peeling steps at which to plot the in-box samples and box vertices.
Defaults to all the peeling steps of sbh object x.
Integer scalar of symbol number for the scatter plot. Defaults to 16.
Integer scalar of symbol expansion. Defaults to 0.5.
Integer vector specifying the symbol color for each step.
Defaults to vector of colors of length the number of steps.
The vector is reused cyclically if it is shorter than the number of steps.
Integer vector of line color of box vertices for each step.
Defaults to vector of colors of length the number of steps.
The vector is reused cyclically if it is shorter than the number of steps.
Integer vector of line type of box vertices for each step.
Defaults to vector of 2's of length the number of steps.
The vector is reused cyclically if it is shorter than the number of steps.
Integer vector of line width of box vertices for each step.
Defaults to vector of 1's of length the number of steps.
The vector is reused cyclically if it is shorter than the number of steps.
Logical scalar. Shall the legend of steps numbers be plotted? Defaults to TRUE.
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 name for output graphic. Defaults to "Scatter Plot".
Absolute path (without final (back)slash separator). Defaults to working directory path.
Logical scalar. Orientation of the printed image. Defaults to FALSE, that is potrait orientation.
Numeric scalar. Width of the graphics region in inches. Defaults to 5.
Numeric scalar. Height of the graphics region in inches. Defaults to 5.
Generic arguments passed to other plotting functions.
Invisible. None. Displays the plot(s) on the specified device.
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.
The scatterplot is drawn only if the number of used covariates of the sbh object is greater than two.
It is drawn on a graphical device with geometrically equal scales on the \(x\) and \(y\) axes.
Dazard J-E. and Rao J.S. (2017). "Variable Selection Strategies for High-Dimensional Survival Bump Hunting using Recursive Peeling Methods." (in prep).
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.
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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.