z.effects(pint)
z.effects()所属R语言包：pint
The model parameters z and W
模型参数Z和W
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Contribution of each sample to a dependency model, and contribution of each variable.
每个样品的贡献，以一个依赖模型，每个变量的贡献。
用法----------Usage----------
z.effects(model, X, Y = NULL)
W.effects(model, X, Y = NULL)
参数----------Arguments----------
参数：model
The fitted dependency model.
拟合依赖模型。
参数：X, Y
Data sets used in fitting the dependency modeling functions (screen.cgh.mrna or link{fit.dependency.model}). Note: Arguments must be given in the same order as in fit.dependency.model or screen.cgh.mrna. Only X is needed for dependency model for one data set.
使用数据集（screen.cgh.mrna或link{fit.dependency.model}）在装配依赖建模功能。注：参数必须在同一顺序作为fit.dependency.model或screen.cgh.mrna。只有X需要依赖一个数据集的模型。
Details
详情----------Details----------
z.effects gives the contribution of each sample to the dependency score. This is approximated by projecting original data to first principal component of Wz. This is possible only when the data window is smaller than half the number of samples.
z.effects给每个样品的贡献依赖得分。这是近似原始数据投影到第一主成分Wz。这是可能的，只有当数据窗口小于样本数的一半。
W.effects gives the contribution of each variable to the observed dependency. This is approximated with the loadings of the first principal component of Wz
W.effects给每个变量的贡献，以观察依赖。这是近似负荷的第一Wz的主要组成部分，在
Original data can be retrieved by locating the row in X (or Y) which has the same variable (gene) name than model.
原始数据可以被检索定位X（Y），其中有比model的名称相同的变量（基因）的行。
值----------Value----------
z.effects gives a projection vector over the samples and W.effects gives a projection vector over the variables.
z.effects给投影向量在样品和W.effects提供了变量的投影向量。
作者（S）----------Author(s)----------
Olli-Pekka Huovilainen <a href="mailto huovila@gmail.com">ohuovila@gmail.com</a> and Leo Lahti <a href="mailto:leo.lahti@iki.fi">leo.lahti@iki.fi</a>
参考文献----------References----------
Lahti et al., 2009 Proc. MLSP'09 IEEE International Workshop on Machine Learning for Signal Processing, See http://www.cis.hut.fi/lmlahti/publications/mlsp09_preprint.pdf
Bach Francis R. and Jordan Michael I. 2005 Technical Report 688. Department of Statistics, University of California, Berkley. http://www.di.ens.fr/~fbach/probacca.pdf
Tipping Michael E. and Bishop Christopher M. 1999. Journal of the Royal Statistical Society, Series B, 61, Part 3, pp. 611–622. http://research.microsoft.com/en-us/um/people/cmbishop/downloads/Bishop-PPCA-JRSS.pdf
参见----------See Also----------
DependencyModel-class, screen.cgh.mrna
DependencyModel-class，screen.cgh.mrna
举例----------Examples----------
data(chromosome17)
window <- fixed.window(geneExp, geneCopyNum, 150, 10)
## pSimCCA model around one gene[＃pSimCCA围绕一个基因模型]
depmodel <- fit.dependency.model(window$X, window$Y)
# Conversion from DependencyModel to GeneDependencyModel so that gene name and location can be stored[因此该基因的名称和位置可以存储的转换从DependencyModel到GeneDependencyModel]
depmodel <- as(depmodel,"GeneDependencyModel")
setGeneName(depmodel) <- window$geneName
setLoc(depmodel) <- window$loc
barplot(z.effects(depmodel, geneExp, geneCopyNum))
## Plot the contribution of each genes to the model. Only the X component is plotted[＃绘制到模型中的每一个基因的贡献。只有X组件被绘制]
## here since Wx = Wy (in SimCCA) [＃在这里，因为WX = WY（在SimCCA）]
barplot(W.effects(depmodel, geneExp, geneCopyNum)$X)
## plot.DpenendencyModel shows also sample and variable effects[也＃plot.DpenendencyModel显示样本和变量的影响]
plot(depmodel,geneExp,geneCopyNum)
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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 2012-2-26 11:10:02
