overlap.plot(Mfuzz)
overlap.plot()所属R语言包:Mfuzz
Visualisation of cluster overlap and global clustering structure
聚类重叠的可视化和全球的聚类结构
译者:生物统计家园网 机器人LoveR
描述----------Description----------
This function visualises the cluster overlap
此功能形象化的聚类重叠
用法----------Usage----------
overlap.plot(cl,overlap,thres=0.1,scale=TRUE,magni=30,P=NULL)
参数----------Arguments----------
参数:cl
object of class “flclust”
对象类“flclust”
参数:overlap
matrix of cluster overlap produced by overlap
overlap生产聚类重叠矩阵
参数:thres
threshold for visualisation. Cluster overlaps below the threshold will not be visualised.
可视化的阈值。不会被可视化低于阈值聚类重叠。
参数:scale
Scale parameter for principal component analysis by prcomp
prcomp主成分分析的尺度参数
参数:magni
Factor for increase the line width for cluster overlap.
因素增加聚类重叠的线条宽度。
参数:P
Projection matrix produced by principal component analysis.
投影矩阵产生的主成分分析。
值----------Value----------
A plot is genererated based on a prinicpal component analysis of the cluster centers. The overlap is visualised by lines with variable width indicating the strength of the overlap. Additonally, the matrix of principal components is returned. This matrix can be re-used for other projections to compare the overlap and global cluster structure of different
有一个图是genererated的聚类中心prinicpal成分分析的基础上。重叠可视化可变宽度的指示线重叠的力量。 additonally返回,主成分矩阵。这个矩阵可以重新用于其他预测比较重叠和全球不同的聚类结构
作者(S)----------Author(s)----------
Matthias E. Futschik (<a href="http://itb.biologie.hu-berlin.de/~futschik">http://itb.biologie.hu-berlin.de/~futschik</a>)
参见----------See Also----------
prcomp
prcomp
举例----------Examples----------
if (interactive()){
data(yeast)
# Data pre-processing[数据预处理]
yeastF <- filter.NA(yeast)
yeastF <- fill.NA(yeastF)
yeastF <- standardise(yeastF)
# Soft clustering[软聚类]
cl <- mfuzz(yeastF,c=20,m=1.25)
X11();mfuzz.plot(yeastF,cl=cl,mfrow=c(4,5))
O <- overlap(cl)
X11()tmp <- overlap.plot(cl,over=O,thres=0.05)
# Alternative clustering [替代聚类]
cl <- mfuzz(yeastF,c=10,m=1.25)
X11();mfuzz.plot(yeastF,cl=cl,mfrow=c(3,4))
O <- overlap(cl)
X11();overlap.plot(cl,over=O,P=Ptmp,thres=0.05)
# visualisation based on principal compents from previous projection[可视化的基础上从先前预测的主要compents]
}
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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