selProbPlot(varSelRF)
selProbPlot()所属R语言包：varSelRF
Selection probability plot for variable importance from random forests
选择变量随机森林的重要性概率图
译者：生物统计家园网 机器人LoveR
描述----------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.
图，为顶级排名k原始样品的变量，每个变量的概率居前排名k的bootstrap样本的基因。
用法----------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, ...)
参数----------Arguments----------
参数：object
An object of class varSelRFBoot such as returned by the varSelRFBoot function.
一个对象的类varSelRFBoot返回的varSelRFBoot功能。
参数：k
A two-component vector with the k-th upper variables for which you want the plots.
双组分矢量k个变量，你要上的图。
参数：color
If TRUE a color plot; if FALSE, black and white.
如果真正的彩色图，如果为FALSE，黑色和白色。
参数：legend
If TRUE, show a legend.
如果是TRUE，显示一个传奇。
参数：xlegend
The x-coordinate for the legend.
的x坐标的传说。
参数：ylegend
The y-coordinate for the legend.
y坐标的传说。
参数：cexlegend
The cex argument for the legend.
cex参数的传说。
参数：main
main for the plot.
main的图。
参数：xlab
xlab for the plot.
xlab的图。
参数：ylab
ylab for the plot.
ylab的图。
参数：pch
pch for the plot.
pch的图。
参数：...
Additional arguments to plot.
其他参数图。
Details
详细信息----------Details----------
<CITE>Pepe et al., 2003</CITE> 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.
<CITE>佩佩等人，2003 </ CITE>建议使用的选择概率图来评估我们的选择“相关的基因的稳定性和信心。”本文还提出了一些更复杂的想法没有实现。
值----------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.
用于其副作用生产的图。在同一张图中，显示“选择概率图”上（最大变量的重要性）kt的个变量。缺省情况下，显示的上部20和上部100色的蓝色和红色。
注意----------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
这个函数是在非常简陋的形状，可用于更一般的类型的数据。我写了专门生产\ 4。
（作者）----------Author(s)----------
Ramon Diaz-Uriarte <a href="mailto:rdiaz02@gmail.com">rdiaz02@gmail.com</a>
参考文献----------References----------
Breiman, L. (2001) Random forests. Machine Learning, 45, 5–32.
Diaz-Uriarte, 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 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.
参见----------See Also----------
randomForest, varSelRF, varSelRFBoot, randomVarImpsRFplot, randomVarImpsRF
randomForest，varSelRF，varSelRFBoot，randomVarImpsRFplot，randomVarImpsRF
实例----------Examples----------
## This is a small example, but can take some time.[＃这是一个小例子，但可能需要一些时间。]
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)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 14:26:44
