Description Usage Arguments Details Value Acknowledgments Note Author(s) References
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ## S3 method for class '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, ...)
|
x |
Object of class |
main |
|
proj |
|
steps |
|
pch |
|
cex |
|
col |
|
boxes |
|
asp |
|
col.box |
|
lty.box |
|
lwd.box |
|
add.caption.box |
|
text.caption.box |
|
pch.group |
|
cex.group |
|
col.group |
|
add.caption.group |
|
text.caption.group |
|
device |
Graphic display device in { |
file |
File name for output graphic. Defaults to "Scatter Plot". |
path |
Absolute path (without final (back)slash separator). Defaults to working directory path. |
horizontal |
|
width |
|
height |
|
... |
Generic arguments passed to other plotting functions. |
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.
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.
End-user plotting function.
"Jean-Eudes Dazard, Ph.D." jean-eudes.dazard@case.edu
"Michael Choe, M.D." mjc206@case.edu
"Michael LeBlanc, Ph.D." mleblanc@fhcrc.org
"Alberto Santana, MBA." ahs4@case.edu
"J. Sunil Rao, Ph.D." Rao@biostat.med.miami.edu
Maintainer: "Jean-Eudes Dazard, Ph.D." jean-eudes.dazard@case.edu
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.
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