Q2(pcaMethods)
Q2()所属R语言包：pcaMethods
Cross-validation for PCA
交叉验证PCA
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Internal cross-validation can be used for estimating the level of structure in a data set and to optimise the choice of number of
内部交叉验证可以用于估算数据集的结构水平和优化选择数
用法----------Usage----------
参数----------Arguments----------
参数：object
A pcaRes object (result from previous PCA analysis.)
一个pcaRes对象（从以前的主成分分析的结果。）
参数：originalData
The matrix (or ExpressionSet) that used to obtain the pcaRes object.
矩阵（或ExpressionSet）获得pcaRes对象。
参数：fold
The number of groups to divide the data in.
组数来划分数据英寸
参数：nruncv
The number of times to repeat the whole cross-validation
的次数，重复交叉验证
参数：type
krzanowski or imputation type cross-validation
krzanowski或归集型交叉验证
参数：verbose
boolean If TRUE Q2 outputs a primitive progress bar.
boolean如果真正的Q2输出原始的进度条。
参数：...
Further arguments passed to the pca function called within Q2. </table>
进一步的参数传递给pca功能在第二季度。 </ TABLE>
Details
详情----------Details----------
This method calculates Q^2 for a PCA model. This is the cross-validated version of R^2 and can be interpreted as the ratio of variance that can be predicted independently by the PCA model. Poor (low) Q^2 indicates that the PCA model only describes noise and that the model is unrelated to the true data
这种方法计算Q^2为PCA模型。这是R^2交叉验证的版本，并可以作为独立的PCA模型可以预测的方差比解释。差（低）Q^2的表明PCA模型只描述了噪声，该模型是无关的真实数据
for the matrix x which has n rows and k columns. For a given number of PC's x is estimated as \hat{x} = TP' (T are scores and P are loadings). Although this defines the leave-one-out cross-validation this is not what is performed if fold is less than the number of rows and/or columns. In 'impute' type CV, diagonal rows of elements in the matrix are deleted and the re-estimated. In 'krzanowski' type CV, rows are sequentially left out to build fold PCA models which give the loadings. Then, columns are sequentially left out to build fold models for scores. By combining scores and loadings from different models, we can estimate completely left out values. The two types may seem similar but can give very different results, krzanowski typically yields more stable and reliable result for estimating data structure whereas impute is better for evaluating missing value imputation performance. Note that since Krzanowski CV operates on a reduced matrix, it is not possible estimate Q2 for all components and the result vector may therefore be shorter than
矩阵xn行k列。对于PC的x的人数估计为\hat{x} = TP'（T是分数和P是载荷）。虽然这个定义交叉验证留一出，这是没有什么是执行倍，如果是小于行和/或列数。在“归罪于”型CV，对角线矩阵中的元素的行被删除，并重新估计。在krzanowski型CV，行的顺序离开构建倍PCA的模式，给负荷。然后，列顺序离开了分数倍的模型建立。相结合，从不同车型的得分和负载，我们可以估算值完全排除在外。两种可能看起来类似，但可以给非常不同的结果，krzanowski通常产生更稳定和更可靠的估算数据结构，而归罪于评估缺失值插补性能更好的结果。请注意，减少矩阵因为Krzanowski简历工作，这是无法估计第二季度的所有组件和结果的向量，因此可能是少于
值----------Value----------
A matrix or vector with Q^2 estimates.
矩阵或向量Q^2估计的。
作者（S）----------Author(s)----------
Henning Redestig
参考文献----------References----------
举例----------Examples----------
x <- iris[,1:4]
pcIr <- pca(x, nPcs=3)
q2 <- Q2(pcIr, x)
barplot(q2, main="Krzanowski CV", xlab="Number of PCs", ylab=expression(Q^2))
pcIr <- pca(x, nPcs=3, method="nipals")
q2 <- Q2(pcIr, x, type="impute")
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 2012-2-26 10:52:33
