plrCMA(CMA)
plrCMA()所属R语言包：CMA
L2 penalized logistic regression
二级处罚logistic回归
译者：生物统计家园网 机器人LoveR
描述----------Description----------
High dimensional logistic regression combined with an L2-type (Ridge-)penalty. Multiclass case is also possible.
L2型（岭）处罚相结合的高维Logistic回归。多用户的情况下也是可能的。
用法----------Usage----------
plrCMA(X, y, f, learnind, lambda = 0.01, scale = TRUE, 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：lambda
Parameter governing the amount of penalization. This hyperparameter should be tuned.
执政的处罚金额的参数。这hyperparameter应该是tuneD。
参数： scale
Scale the predictors as specified by X to have unit variance and zero mean.
X有单位方差和零均值指定规模的预测。
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：...
Currently unused argument.
目前未使用的参数。
值----------Value----------
An object of class cloutput.
对象类cloutput。
作者（S）----------Author(s)----------
Special thanks go to
Ji Zhu (University of Ann Arbor, Michigan)
Trevor Hastie (Stanford University)
who provided the basic code that was then adapted by
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----------
Classification of gene microarrays by penalized logistic regression.
<h3>See Also</h3> <code>compBoostCMA</code>, <code>dldaCMA</code>, <code>ElasticNetCMA</code>, <code>fdaCMA</code>, <code>flexdaCMA</code>, <code>gbmCMA</code>, <code>knnCMA</code>, <code>ldaCMA</code>, <code>LassoCMA</code>, <code>nnetCMA</code>, <code>pknnCMA</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 10 genes[＃提取从第10个基因的基因表达]
golubX <- as.matrix(golub[,-1])
### select learningset[＃选择learningset]
ratio <- 2/3
set.seed(111)
learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
### run penalized logistic regression (no tuning)[＃执行处罚的logistic回归（不调整）]
plrresult <- plrCMA(X=golubX, y=golubY, learnind=learnind)
### show results[＃显示结果]
show(plrresult)
ftable(plrresult)
plot(plrresult)
### multiclass example:[＃多类例如：]
### load Khan data[＃＃负载汗数据]
data(khan)
### extract class labels[＃提取类标签]
khanY <- khan[,1]
### extract gene expression from first 10 genes[＃提取从第10个基因的基因表达]
khanX <- as.matrix(khan[,-1])
### select learningset[＃选择learningset]
set.seed(111)
learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
### run penalized logistic regression (no tuning)[＃执行处罚的logistic回归（不调整）]
plrresult <- plrCMA(X=khanX, y=khanY, learnind=learnind)
### show results[＃显示结果]
show(plrresult)
ftable(plrresult)
plot(plrresult)
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 2012-2-25 15:24:24
