extractBic(fabia)
extractBic()所属R语言包：fabia
Extraction of Biclusters
提取的Biclusters
译者：生物统计家园网 机器人LoveR
描述----------Description----------
extractBic: R implementation of extractBic.
extractBic：R的extractBic实施。
用法----------Usage----------
extractBic(fact,thresZ=0.5,thresL=NULL)
参数----------Arguments----------
参数：fact
object of the class Factorization.
对象类Factorization。
参数：thresZ
threshold for sample belonging to bicluster; default 0.5.
属于样品到bicluster，默认0.5的阈值。
参数：thresL
threshold for loading belonging to bicluster (if not given it is estimated).
装载属于bicluster（如果没有给出估计）的阈值。
Details
详情----------Details----------
Essentially the model is the sum of outer products of vectors:
模型本质上是向量外产品的总和：
where the number of summands p is the number of biclusters. The matrix factorization is
加数p是的biclusters数量。矩阵分解
Here λ_i are from R^n, z_i from R^l, L from R^{n \times p}, Z from R^{p \times l}, and X, U from R^{n \times l}.
这里λ_iR^n，z_iR^l，LR^{n \times p}，ZR^{p \times l} X，UR^{n \times l}。
U is the Gaussian noise with a diagonal covariance matrix which entries are given by Psi.
U 是一个对角线协方差矩阵项Psi的高斯噪声。
The Z is locally approximated by a Gaussian with inverse variance given by lapla.
Z 本地近似高斯与lapla逆差额。
Using these values we can computer for each j the variance z_i given x_j. Here
每个j方差 z_i给 x_j使用这些值，我们可以电脑。这里
This variance can be used to determine the information content of a bicluster.
这种差异可以用来确定一个bicluster的信息内容。
The λ_i and z_i are used to extract the bicluster i, where a threshold determines which observations and which samples belong the the bicluster.
λ_i 和z_i 用于提取biclusteri，阈值确定的意见和样品所属的bicluster，。
In bic the biclusters are extracted according to the largest absolute values of the component i, i.e. the largest values of λ_i and the largest values of z_i . The factors z_i are normalized to variance 1.
在bic根据组件的最大绝对值提取的biclusters i“即λ_i 和z_i 的最大价值最大的值。因素 z_i归方差为1的。
The components of bic are binp, bixv, bixn, biypv, and biypn.
组件的bic是binp，bixv，bixn，biypv和biypn。
binp give the size of the bicluster: number observations and number samples. bixv gives the values of the extracted observations that have absolute values above a threshold. They are sorted. bixn gives the extracted observation names (e.g. gene names). biypv gives the values of the extracted samples that have absolute values above a threshold. They are sorted. biypn gives the names of the extracted samples (e.g. sample names).
binp给的bicluster的大小：数字观测和数量的样品。 bixv提取的意见，即有一个阈值以上的绝对值的值。它们进行排序。 bixn给提取的观察名称（如基因名称）。 biypv给提取的样品有超过阈值的绝对值的值。它们进行排序。 biypn给提取的样品（如样品名称）的名称。
In bicopp the opposite clusters to the biclusters are given. Opposite means that the negative pattern is present.
在bicopp的biclusters的对面聚类。对面的负面模式是目前。
The components of opposite clusters bicopp are binn, bixv, bixn, biypnv, and biynn.
相反聚类的组成部分bicopp是binn，bixv，bixn，biypnv，biynn。
binp give the size of the opposite bicluster: number observations and number samples. bixv gives the values of the extracted observations that have absolute values above a threshold. They are sorted. bixn gives the extracted observation names (e.g. gene names). biynv gives the values of the opposite extracted samples that have absolute values above a threshold. They are sorted. biynn gives the names of the opposite extracted samples (e.g. sample names).
binp给对面bicluster的规模数量的意见和数量样本。 bixv提取的意见，即有一个阈值以上的绝对值的值。它们进行排序。 bixn给提取的观察名称（如基因名称）。 biynv给对面提取样品，高于阈值的绝对值的值。它们进行排序。 biynn给对面提取样品（如样品名称）的名称。
That means the samples are divided into two groups where one group shows large positive values and the other group has negative values with large absolute values. That means a observation pattern can be switched on or switched off relative to the average value.
这意味着样本分为两组，其中一组显示了很大的正面的价值观和另一组有绝对值大的负值。这意味着一个观察模式，可以开启或关闭相对平均值。
numn gives the indices of bic with components: numng = bix and numnp = biypn.
numn给人指数bic组件：numng=bix和numnp=biypn。
numn gives the indices of bicopp with components: numng = bix and numnn = biynn.
numn给人指数bicopp组件：numng=bix和numnn=biynn。
Implementation in R.
R中的实现
值----------Value----------
参数：bic
extracted biclusters.
提取biclusters。
参数：numn
indexes for the extracted biclusters.
指标提取biclusters。
参数：bicopp
extracted opposite biclusters.
提取对面biclusters。
参数：numnopp
indexes for the extracted opposite biclusters.
指标提取对面biclusters。
参数：X
scaled and centered data matrix.
缩放中心的数据矩阵。
参数：np
number of biclusters.
对biclusters。
作者（S）----------Author(s)----------
Sepp Hochreiter
参见----------See Also----------
fabia, fabias, fabiap, fabi, fabiasp, mfsc, nmfdiv, nmfeu, nmfsc, plot, extractPlot, extractBic, plotBicluster, Factorization, projFuncPos, projFunc, estimateMode, makeFabiaData, makeFabiaDataBlocks, makeFabiaDataPos, makeFabiaDataBlocksPos, matrixImagePlot, summary, show, showSelected, fabiaDemo, fabiaVersion
fabia，fabias，fabiap，fabi，fabiasp，mfsc，nmfdiv，nmfeu，nmfsc，plot，extractPlot，extractBic，plotBicluster，Factorization，projFuncPos，projFunc，estimateMode ，makeFabiaData，makeFabiaDataBlocks，makeFabiaDataPos，makeFabiaDataBlocksPos，matrixImagePlot，summary，show，showSelected fabiaDemo，fabiaVersion
举例----------Examples----------
#---------------[---------------]
# TEST[试验]
#---------------[---------------]
dat <- makeFabiaDataBlocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)
X <- dat[[1]]
Y <- dat[[2]]
resEx <- fabia(X,3,0.01,20)
rEx <- extractBic(resEx)
rEx$bic[1,]
rEx$bic[2,]
rEx$bic[3,]
## Not run: [＃无法运行：]
#---------------[---------------]
# DEMO1[DEMO1]
#---------------[---------------]
dat <- makeFabiaDataBlocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)
X <- dat[[1]]
Y <- dat[[2]]
resToy <- fabia(X,13,0.01,200)
rToy <- extractBic(resToy)
resToy@avini
rToy$bic[1,]
rToy$bic[2,]
rToy$bic[3,]
#---------------[---------------]
# DEMO2[DEMO2]
#---------------[---------------]
avail <- require(fabiaData)
if (!avail) {
message("")
message("")
message("#####################################################")[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃ ＃＃“）]
message("Package 'fabiaData' is not available: please install.")
message("#####################################################")[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃ ＃＃“）]
} else {
data(Breast_A)
X <- as.matrix(XBreast)
resBreast <- fabia(X,5,0.1,200)
rBreast <- extractBic(resBreast)
resBreast@avini
rBreast$bic[1,]
rBreast$bic[2,]
rBreast$bic[3,]
}
## End(Not run)[＃结束（不运行）]
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。
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发表于 2012-2-25 17:28:39
