S3-method plot function for two-dimensional visualization of scatter of data points
and cross-validated encapsulating box of a sbh
object for the highest risk (inbox) versus
lower-risk (outbox) groups (PRSP), and between the two specified fixed groups (PRGSP),
if this option is used. The scatter plot is done for a given peeling step (or number of steps)
of the peeling sequence (inner loop of our PRSP or PRGSP) 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),
steps = 1:x$cvfit$cv.nsteps,
pch = 16,
cex = 0.5,
col = c(1,2),
boxes = TRUE,
asp = NA,
col.box = rep(2,length(steps)),
lty.box = rep(2,length(steps)),
lwd.box = rep(1,length(steps)),
add.caption.box = boxes,
text.caption.box = paste("Step: ", steps, sep=""),
pch.group = c(1,1),
cex.group = c(1,1),
col.group = c(3,4),
add.caption.group = ifelse(test = x$cvarg$peelcriterion == "grp",
yes = TRUE,
no = FALSE),
text.caption.group = levels(x$groups),
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 in which the scatter plot is to be plotted. See details.
Defaults to first two dimensions c(1,2)
.
Integer
vector
. Vector of peeling steps at which to plot the
inbox samples and box vertices.
Defaults to all the peeling steps of sbh
object x
.
Integer
scalar specifying the symbol for the outbox and inbox
data points (Defaults to 16 for both).
Numeric
scalar specifying the symbol expansion for the outbox
and inbox data points (Defaults to 0.5 for both).
Integer
vector
specifying the symbol color for the outbox
and inbox data points (Defaults to "black" and "red", respectively).
Logical
scalar. Shall the encapsulating box(es) be plotted as well?
Default to TRUE
.
Numeric
scalar giving the \(y\)/\(x\) aspect ratio.
Default to asp=NA
i.e. a regular plot. See details.
Integer
vector
of line color of box vertices for each step.
Defaults to vector of 2's (red) 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 caption be plotted?
Defaults to boxes
value.
Character
vector
of caption content.
Defaults to paste("Step: ", steps, sep="")
.
Integer
vector
specifying the symbol for the two groups
data points (Defaults to 0.5 for both).
Numeric
vector
specifying the symbol expansion for the two groups
data points (Defaults to 0.5 for both).
Integer
vector
specifying the symbol color for the two groups
data points (Defaults to "green" and "blue").
Logical
scalar. Shall the caption be plotted?
Defaults to TRUE
or FALSE
,
depending on x$cvarg$peelcriterion
.
Character
vector
of caption content.
Defaults to levels(x$groups)
.
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.
Use graphical parameter asp=1
for a plotting a proportional scatter plot on the graphical device
with geometrically equal scales on the \(x\) and \(y\) axes. In that case, it produces a proportional
scatter plot where distances between points are represented accurately on screen. The window is set up
so that one data unit in the \(x\) direction is equal in length to one data unit in the \(y\) direction.
The two dimensions (proj
) of the projection plane in which the scatter plot is to be plotted,
must be a subset (in the large sense) of the used (selected) covariates of sbh
object x
.
If the number of used covariates in the sbh
object is zero, the scatterplot will not be plotted.
If the number of used covariates is one, the scatterplot will be plotted using the specified
covariate and an arbitrary dimension, both specified by the user.
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.