PRIMsrc (version 0.6.0)

plot_profile: Visualization for Model Selection/Validation

Description

Function for plotting the cross-validated profiles of a PRSP object. It uses the user's choice of statistics among the Log Hazard Ratio (LHR), Log-Rank Test (LRT) or Concordance Error Rate (CER) as a function of the model tuning parameter, that is, the optimal number of peeling steps of the peeling sequence (inner loop of our PRSP algorithm).

Usage

plot_profile(object,
               main = NULL, 
               xlab = "Peeling Steps", 
               ylab = "Mean Profiles",
               add.sd = TRUE, 
               add.legend = TRUE, 
               add.profiles = TRUE,
               pch = 20, 
               col = 1, 
               lty = 1, 
               lwd = 2, 
               cex = 2,
               device = NULL, 
               file = "Profile Plot", 
               path=getwd(), 
               horizontal = FALSE, 
               width = 8.5, 
               height = 5.0, ...)

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 "Peeling Steps".

ylab

Character vector. Y axis label. Defaults to "Mean Profiles".

add.sd

Logical scalar. Shall the standard error bars be plotted? Defaults to TRUE.

add.legend

Logical scalar. Shall the legend be plotted? Defaults to TRUE.

add.profiles

Logical scalar. Shall the individual profiles (for all replicates) be plotted? Defaults to TRUE.

pch

Integer scalar of symbol number for all the profiles. Defaults to 20.

col

Integer scalar of line color of the mean profile. Defaults to 1.

lty

Integer scalar of line type of the mean profile. Defaults to 1.

lwd

Integer scalar of line width of the mean profile. Defaults to 2.

cex

Integer scalar of symbol expansion for all the profiles. Defaults to 2.

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 "Profile Plot".

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 5.

...

Generic arguments passed to other plotting functions.

Value

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

Details

Model validation is done by applying the optimization criterion on the user's choice of specific statistic. The goal is to find the optimal value of the K-fold cross-validated number of steps by maximization of LHR or LRT, or minimization of CER. Currently, this done internally for visualization purposes, but it will ultimately offer the option to do be interactive with the end-user as well for parameter choosing/model selection.

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_profile(object = CVCOMBREP.synt1, 
             main = "RCCV tuning profiles for model #1",
             xlab = "Peeling Steps", ylab = "Mean Profiles",
             pch=20, col="black", lty=1, lwd=2, cex=2,
             add.sd = TRUE, add.legend = TRUE, add.profiles = TRUE,
             device = NULL, file = "Profile Plot", path=getwd(),
             horizontal = FALSE, width = 8.5, height = 5)