Description Usage Arguments Details Value Note Author(s) References See Also Examples
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.
1 2 3 4 5 6 7 8 9 10 
object 
An object of class varSelRFBoot such as returned by the

k 
A twocomponent vector with the kth 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 xcoordinate for the legend. 
ylegend 
The ycoordinate for the legend. 
cexlegend 
The 
main 

xlab 

ylab 

pch 

... 
Additional arguments to plot. 
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.
Used for its side effects of producing a plot. In a single plot show
the "selection probability plot" for the upper
(largest variable importance) kt
th variables. By default, show
the upper 20 and the upper 100
colored blue and red respectively.
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.
Ramon DiazUriarte rdiaz02@gmail.com
Breiman, L. (2001) Random forests. Machine Learning, 45, 5–32.
DiazUriarte, R. , Alvarez de Andres, S. (2005) Variable selection from random forests: application to gene expression data. Tech. report. http://ligarto.org/rdiaz/Papers/rfVS/randomForestVarSel.html
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 structureactivity relationships of pharmaceutical molecules. Pp. 334343 in F. Roli, J. Kittler, and T. Windeatt (eds.). Multiple Classier Systems, Fifth International Workshop, MCS 2004, Proceedings, 911 June 2004, Cagliari, Italy. Lecture Notes in Computer Science, vol. 3077. Berlin: Springer.
randomForest
,
varSelRF
,
varSelRFBoot
,
randomVarImpsRFplot
,
randomVarImpsRF
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## This is a small example, but can take some time.
x < matrix(rnorm(25 * 30), ncol = 30)
x[1:10, 1:2] < x[1:10, 1:2] + 2
cl < factor(c(rep("A", 10), rep("B", 15)))
rf.vs1 < varSelRF(x, cl, ntree = 200, ntreeIterat = 100,
vars.drop.frac = 0.2)
rf.vsb < varSelRFBoot(x, cl,
bootnumber = 10,
usingCluster = FALSE,
srf = rf.vs1)
selProbPlot(rf.vsb, k = c(5, 10), legend = TRUE,
xlegend = 8, ylegend = 0.8)

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