Clest(RSKC)
Clest()所属R语言包：RSKC
An implementation of Clest with robust sparse K-means. CER is used as a similarity measure.
具有强大的稀疏的K-means Clest的实现。 CER是用来作为一个相似性测度。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function Clest performs Clest ( Dudoit and Fridlyand (2002)) with CER as the measure of the agreement between two partitions (in each training set). The following clustering algorithm can be used: K-means, trimmed K-means, sparse K-means and robust sparse K-means.
函数Clest执行Clest（（2002））Dudoit和Fridlyand，CER作为衡量两个分区（每个训练集）之间的协议。下面的聚类算法，可用于：K均值，修剪的K-means，稀疏的K-装置和鲁棒稀疏K-均值。
用法----------Usage----------
Clest(d, maxK, alpha, B = 10, B0 = 20, nstart = 100,
L1 = 12, beta = 0.1, pca = TRUE, silent=FALSE)
参数----------Arguments----------
参数：d
A numerical data matrix (N by p) where N is the number of cases and p is the number of features. The cases are clustered.
数值数据矩阵（Np）其中N是多少的情况下，p是多少功能。的情况下聚集。
参数：maxK
The maximum number of clusters that you suspect.
你怀疑簇的最大数量。
参数：alpha
See RSKC.
见RSKC。
参数：B
The number of times that an observed dataset d is randomly partitioned into a learning set and a training set. Note that each generated reference dataset is partitioned into a learning and a testing set only once to ease the computational cost.
观察到的数据集d被随机分配到一个学习集和训练集的次数。请注意，每个生成的基准数据集被划分成一个学习和测试仅设置一次到缓解的计算成本。
参数：B0
The number of times that the reference dataset is generated.
倍的基准数据集生成的数目。
参数：nstart
The number of random initial sets of cluster centers at Step(a) of robust sparse K-means clustering.
在步骤（a）强大的稀疏的K-means聚类的随机初始的聚类中心的。
参数：L1
See RSKC.
见RSKC。
参数：beta
0 <= beta <= 1: significance level. Clest chooses the number of clusters that returns the strongest significant evidence against the hypothesis H0 : K = 1.
0 <=beta<= 1的显着性水平。 Clest选择的数字聚类返回最强有效的证据对假设H0：K = 1。
参数：pca
Logical, if TRUE, then reference datasets are generated from a PCA reference distribution. If FALSE, then the reference data set is generated from a simple reference distribution.
逻辑，如果TRUE，然后参考的数据集生成从PCA的参考分布。如果FALSE，然后参考的数据集生成一个简单的参考分布。
参数：silent
Logical, if TRUE, then the number of iteration on progress is not printed.
逻辑，如果TRUE，然后迭代的进展的数量不打印。
值----------Value----------
<table summary="R valueblock"> <tr valign="top"><td>K</td> <td> The solution of Clest; the estimated number of clusters. </td></tr>
<table summary="R valueblock"> <tr valign="top"> <TD> K</ TD> <TD>的解决方案Clest聚类的估计数。 </ TD> </ TR>
<tr valign="top"><td>result.table</td> <td> A real matrix (maxK-1 by 4). Each row represents K=2,...,maxK and columns represent the test statistics (=observed CER-reference CER), observed CER, reference CER and P-value. </td></tr>
<tr valign="top"> <TD> result.table </ TD> <td>一个真正的矩阵（maxK-14）。每一行代表K=2，...，maxK和列代表的检验统计量（=观察到的CER基准CER），观察CER，参考CER和P-值。 </ TD> </ TR>
<tr valign="top"><td>referenceCERs</td> <td> A matrix (B0 by maxK-1), containing CERs of testing datasets from generated datasets for each K=2,...,maxK. </td></tr>
<tr valign="top"> <TD> referenceCERs </ TD> <td>一个矩阵（B0maxK-1），包含测试数据集生成的数据集的核证减排量为每个 K=2,...,maxK。 </ TD> </ TR>
<tr valign="top"><td>observedCERs</td> <td> A matrix (B by maxK-1), containing CERs of B testing sets for each K=2,...,maxK. </td></tr>
<tr valign="top"> <TD> observedCERs </ TD> <td>一个矩阵（BmaxK-1），包含核证减排量的B测试集为每个K=2,...,maxK。 </ TD> </ TR>
<tr valign="top"><td>call</td> <td> The matched call.</td></tr>
<tr valign="top"> <TD> call </ TD> <TD>匹配的呼叫。</ TD> </ TR>
</table>
</ TABLE>
（作者）----------Author(s)----------
Yumi Kondo <y.kondo@stat.ubc.ca>
参考文献----------References----------
Yumi Kondo (2011), Robustificaiton of the sparse K-means clustering algorithm, MSc. Thesis, University of British Columbia http://hdl.handle.net/2429/37093
S. Dudoit and J. Fridlyand. A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biology, 3(7), 2002.
实例----------Examples----------
## Not run: [＃不运行：]
# little simulation function [小的仿真功能]
sim <-
function(mu,f){
D<-matrix(rnorm(60*f),60,f)
D[1:20,1:50]<-D[1:20,1:50]+mu
D[21:40,1:50]<-D[21:40,1:50]-mu
return(D)
}
set.seed(1)
d<-sim(1.5,100); # non contaminated dataset with noise variables[非污染与噪声变量的数据集]
# Clest with robust sparse K-means[Clest具有强大的稀疏的K-means]
rsk<-Clest(d,5,alpha=1/20,B=3,B0=10, beta = 0.05, nstart=100,pca=TRUE,L1=3,silent=TRUE);
# Clest with K-means[clest与K-均值]
k<-Clest(d,5,alpha=0,B=3,B0=10, beta = 0.05, nstart=100,pca=TRUE,L1=NULL,silent=TRUE);
## End(Not run)[＃（不执行）]
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。
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发表于 2012-9-28 22:02:22
