R: Selection probability plot for variable importance from...
selProbPlot
R Documentation
Selection probability plot for variable importance from random forests
Description
Plot, for the top ranked k variables from the original sample, the
probability that each of these variables is included among the top
ranked k genes from the bootstrap samples.
Usage
selProbPlot(object, k = c(20, 100),
color = TRUE,
legend = FALSE,
xlegend = 68,
ylegend = 0.93,
cexlegend = 1.4,
main = NULL,
xlab = "Rank of gene",
ylab = "Selection probability",
pch = 19, ...)
Arguments
object
An object of class varSelRFBoot such as returned by the
varSelRFBoot function.
k
A two-component vector with the k-th upper variables for
which you want the plots.
color
If TRUE a color plot; if FALSE, black and white.
legend
If TRUE, show a legend.
xlegend
The x-coordinate for the legend.
ylegend
The y-coordinate for the legend.
cexlegend
The cex argument for the legend.
main
main for the plot.
xlab
xlab for the plot.
ylab
ylab for the plot.
pch
pch for the plot.
...
Additional arguments to plot.
Details
Pepe et al., 2003 suggested the use of selection probability
plots to evaluate the stability and confidence on our selection of
"relevant genes." This paper also presents several more sophisticated
ideas not implemented here.
Value
Used for its side effects of producing a plot. In a single plot show
the "selection probability plot" for the upper
(largest variable importance) ktth variables. By default, show
the upper 20 and the upper 100
colored blue and red respectively.
Note
This function is in very rudimentary shape and could be used for
more general types of data. I wrote specifically to produce Fig. 4 of
the paper.
Pepe, M. S., Longton, G., Anderson, G. L. & Schummer, M. (2003) Selecting differentially expressed genes from microarray experiments.
Biometrics, 59, 133–142.
Svetnik, V., Liaw, A. , Tong, C & Wang, T. (2004) Application of
Breiman's random forest to modeling structure-activity relationships of
pharmaceutical molecules. Pp. 334-343 in F. Roli, J. Kittler, and T. Windeatt
(eds.). Multiple Classier Systems, Fifth International Workshop, MCS
2004, Proceedings, 9-11 June 2004, Cagliari, Italy. Lecture Notes in
Computer Science, vol. 3077. Berlin: Springer.