flexdaCMA(CMA)
flexdaCMA()所属R语言包：CMA
Flexible Discriminant Analysis
灵活的判别分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This method is experimental.
这种方法是实验。
It is easy to show that, after appropriate scaling of the predictor matrix X, Fisher's Linear Discriminant Analysis is equivalent to Discriminant Analysis in the space of the fitted values from the linear regression of the nlearn x K indicator matrix of the class labels on X. This gives rise to 'nonlinear discrimant analysis' methods that expand X in a suitable, more flexible basis. In order to avoid overfitting, penalization is used. In the implemented version, the linear model is replaced by a generalized additive one, using the package mgcv.
它很容易证明，适当的调整后的预测矩阵X，Fisher线性判别分析判别分析是相当于在空间nlearn x K指标矩阵的线性回归拟合值X类的标签。这引起了“的非线性discrimant分析的方法，扩大X在一个合适的，更灵活的基础。为了避免过拟合，处罚。在实施的版本，线性模型是由广义可加一取代，使用包mgcv。
For S4 method information, s. flexdaCMA-methods.
S4方法信息的。 flexdaCMA-methods。
用法----------Usage----------
flexdaCMA(X, y, f, learnind, comp = 1, plot = FALSE, models=FALSE, ...)
参数----------Arguments----------
参数：X
Gene expression data. Can be one of the following:
基因表达数据。可以是下列之一：
A matrix. Rows correspond to observations, columns to variables.
Amatrix。行对应的观察，列变量。
A data.frame, when f is not missing (s. below).
一个data.frame时f不缺少（S.下面）。
An object of class ExpressionSet.
对象类ExpressionSet。
参数：y
Class labels. Can be one of the following:
类的标签。可以是下列之一：
A numeric vector.
一个numeric向量。
A factor.
Afactor。
A character if X is an ExpressionSet that specifies the phenotype variable.
一个如果character X是一个ExpressionSet指定的表型变量。
missing, if X is a data.frame and a proper formula f is provided.
missing，X是data.frame和适当的公式f提供。
WARNING: The class labels will be re-coded to range from 0 to K-1, where K is the total number of different classes in the learning set.
警告：类标签将被重新编码范围从0K-1，K是在学习集不同类别的总数。
参数：f
A two-sided formula, if X is a data.frame. The left part correspond to class labels, the right to variables.
一个双面的公式，如果X是data.frame。左边部分对应类的标签，对变量的权利。
参数：learnind
An index vector specifying the observations that belong to the learning set. May be missing; in that case, the learning set consists of all observations and predictions are made on the learning set.
索引向量指定属于学习集的意见。可能missing;在这种情况下，学习组学习集的所有意见和预测。
参数：comp
Number of discriminant coordinates (projections) to compute. Default is one, must be smaller than or equal to K-1, where K is the number of classes.
判别坐标数量（预测）计算。默认是，必须小于或等于K-1，其中K是班级数目。
参数：plot
Should the projections onto the space spanned by the optimal projection directions be plotted ? Default is FALSE.
应该被绘制到空间的最优投影方向的跨区预测？默认FALSE。
参数：models
a logical value indicating whether the model object shall be returned
一个逻辑值，该值指示是否应归还模型对象
参数：...
Further arguments passed to the function gam from the package mgcv.
进一步的参数传递给函数gam包mgcv。
值----------Value----------
An object of class cloutput.
对象类cloutput。
注意----------Note----------
Excessive variable selection has usually to performed before flexdaCMA can be applied in the p > n setting. Recall that the original predictor dimension is even enlarged,
过多的变量选择通常之前执行flexdaCMA可以在p > n设置应用。记得原来的预测维度甚至扩大，
作者（S）----------Author(s)----------
Martin Slawski <a href="mailto:ms@cs.uni-sb.de">ms@cs.uni-sb.de</a>
Anne-Laure Boulesteix <a href="mailto:boulesteix@ibe.med.uni-muenchen.de">boulesteix@ibe.med.uni-muenchen.de</a>
参考文献----------References----------
<h3>See Also</h3> <code>compBoostCMA</code>, <code>dldaCMA</code>, <code>ElasticNetCMA</code>, <code>fdaCMA</code>, <code>gbmCMA</code>, <code>knnCMA</code>, <code>ldaCMA</code>, <code>LassoCMA</code>, <code>nnetCMA</code>, <code>pknnCMA</code>, <code>plrCMA</code>, <code>pls_ldaCMA</code>, <code>pls_lrCMA</code>, <code>pls_rfCMA</code>, <code>pnnCMA</code>, <code>qdaCMA</code>, <code>rfCMA</code>,
举例----------Examples----------
### load Golub AML/ALL data[＃负载戈卢布反洗钱/所有数据]
data(golub)
### extract class labels[＃提取类标签]
golubY <- golub[,1]
### extract gene expression from first 5 genes[＃提取从第5个基因的基因表达]
golubX <- as.matrix(golub[,2:6])
### select learningset[＃选择learningset]
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run flexible Discriminant Analysis[＃运行灵活的判别分析]
result <- flexdaCMA(X=golubX, y=golubY, learnind=learnind, comp = 1)
### show results[＃显示结果]
show(result)
ftable(result)
plot(result)
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。
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发表于 2012-2-25 15:20:23
