ppca(pcaMethods)
ppca()所属R语言包：pcaMethods
Probabilistic PCA
概率主成分分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Implementation of probabilistic PCA (PPCA). PPCA allows to perform PCA on incomplete data and may be used for missing value estimation. This script was implemented after the Matlab version provided by Jakob Verbeek ( see http://lear.inrialpes.fr/~verbeek/) and the draft “EM
实施概率PCA侵权的。申诉机关允许执行PCA不完整的数据，可用于缺失值估计。此脚本后实施，由雅各布·维贝克提供的MATLAB版本（见http://lear.inrialpes.fr/~维贝克/）草案“的EM
用法----------Usage----------
ppca(Matrix, nPcs=2, seed=NA, threshold=1e-05, ...)
参数----------Arguments----------
参数：Matrix
matrix – Data containing the variables in columns and observations in rows. The data may contain missing values, denoted as NA.
matrix - 包含列和行的观测变量的数据。数据可能包含缺失值，记为NA。
参数：nPcs
numeric – Number of components to estimate. The preciseness of the missing value estimation depends on the number of components, which should resemble the internal structure of the data.
numeric - 组件的数量估计。严谨的缺失值估计取决于元件的数量，应该像数据的内部结构。
参数：seed
numeric Set the seed for the random number generator. PPCA creates fills the initial loading matrix with random numbers chosen from a normal distribution. Thus results may vary slightly. Set the seed for exact reproduction of your results.
numeric设置为随机数发生器的种子。申诉机关创建填充选择从正态分布的随机数的初始载荷矩阵。因此，结果可能会略有不同。设置真实再现你的结果的种子。
参数：threshold
Convergence threshold.
收敛阈值。
参数：...
Reserved for future use. Currently no further parameters are used. </table>
保留供将来使用。目前还没有进一步的参数。 </ TABLE>
Details
详情----------Details----------
Probabilistic PCA combines an EM approach for PCA with a probabilistic model. The EM approach is based on the assumption that the latent variables as well as the noise are normal distributed.
概率主成分分析相结合的EM的PCA方法与概率模型。 EM方法的基础上，潜变量以及噪音是正常的分布假设。
In standard PCA data which is far from the training set but close to the principal subspace may have the same reconstruction error. PPCA defines a likelihood function such that the likelihood for data far from the training set is much lower, even if they are close to the principal subspace. This allows to improve the estimation accuracy.
在PCA的标准数据，这是远从训练集，但接近的主要子空间可能具有相同的重建误差。申诉机关定义了一个似然函数，远离训练集数据的可能性要低得多，即使他们是接近的主要子空间。这可以提高估计精度。
A method called kEstimate is provided to estimate the optimal number of components via cross validation. In general few components are sufficient for reasonable estimation accuracy. See also the package documentation for further discussion on what kind of data PCA-based missing value estimation is advisable.
一个名为kEstimate的方法提供通过交叉验证组件的最佳估计。几个组件，一般是充分合理的估计精度。也看到包的文档作进一步讨论在什么样的数据基于PCA缺失值估计是最好的。
Complexity:<br> Runtime is linear in the number of data, number of data dimensions and number of principal components.
复杂性：参考运行时间是线性的数据，数据维数和主要部件的数量。
Convergence: The threshold indicating convergence was changed from 1e-3 in 1.2.x to 1e-5 in the current version leading to more stable results. For reproducability you can set the seed (parameter seed) of the random number generator. If used for missing value estimation, results may be checked by simply running the algorithm several times with changing seed, if the estimated
收敛：表明收敛的阈值从1.2.x的1E-3 1E-5在当前的版本更稳定的结果。为reproducability，你可以设置随机数发生器的种子（种子参数）。如果缺失值估计，结果可能会被检查，只需运行算法改变种子数次，如果估计
值----------Value----------
Standard PCA result object used by all PCA-based methods of this package. Contains scores, loadings, data mean and
使用这个包的所有基于PCA方法的标准PCA结果对象。包含分数，载荷，数据的意思，
注意----------Note----------
Requires MASS. It is not recommended to use this
需要MASS。这是不建议使用此
作者（S）----------Author(s)----------
Wolfram Stacklies
参见----------See Also----------
bpca, svdImpute, prcomp,
svdImpute，prcomp的<code> BPCA
举例----------Examples----------
data(metaboliteData)
## Perform probabilistic PCA using the 3 largest components[＃执行概率PCA的使用3个最大的组成部分]
result <- pca(t(metaboliteData), method="ppca", nPcs=3, seed=123)
## Get the estimated complete observations[＃获取估计完整的意见。]
cObs <- completeObs(result)
## Plot the scores[＃绘制的分数]
plotPcs(result, type = "scores")
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-26 10:52:04
