fitIWPCA.matrix(aroma.light)
fitIWPCA.matrix()所属R语言包：aroma.light
Robust fit of linear subspace through multidimensional data
线性子空间，通过的鲁棒合适的多维数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Robust fit of linear subspace through multidimensional data.
通过多维数据的线性子空间的鲁棒适合。
用法----------Usage----------
参数----------Arguments----------
参数：X
NxK matrix where N is the number of observations and K is the number of dimensions (channels).
NxKmatrix其中N是一些意见和K的维数（频道）。
参数：constraint
A character string or a numeric value. If character it specifies which additional contraint to be used to specify the offset parameters along the fitted line; If "diagonal", the offset vector will be a point on the line that is closest to the diagonal line (1,...,1). With this constraint, all bias parameters are identifiable. If "baseline" (requires argument baselineChannel), the estimates are such that of the bias and scale parameters of the baseline channel is 0 and 1, respectively. With this constraint, all bias parameters are identifiable. If "max", the offset vector will the point on the line that is as "great" as possible, but still such that each of its components is less than the corresponding minimal signal. This will guarantee that no negative signals are created in the backward transformation. If numeric value, the offset vector will the point on the line such that after applying the backward transformation there are constraint*N. Note that constraint==0 corresponds approximately to constraint=="max". With the latter two constraints, the bias parameters are only identifiable modulo the fitted line.
一个character字符串或一个numeric值。如果character它指定额外的约束实现用于指定偏移参数拟合线沿线的"diagonal"如果，偏移的向量将是一个点线最接近的对角线（1 ，...，1）。有了这方面的限制，所有的偏置参数识别。如果"baseline"（需要参数baselineChannel），估计是基准通道的偏见和尺度参数是0和1，分别。有了这方面的限制，所有的偏置参数识别。如果"max"，偏移向量点就行了“伟大”，尽可能，但还是这样，每个组件是小于相应的最小信号。这将保证在落后的转型创造没有负面信号。如果numeric值，偏移向量点就行了，这样，应用落后的转型后，有constraint*N。请注意，constraint==0大约相当于constraint=="max"。与后者的两个限制，偏置参数是唯一的识别模的拟合线。
参数：baselineChannel
Index of channel toward which all other channels are conform. This argument is required if constraint=="baseline". This argument is optional if constraint=="diagonal" and then the scale factor of the baseline channel will be one. The estimate of the bias parameters is not affected in this case. Defaults to one, if missing.
指数渠道向所有其他渠道符合。此参数是必需的，如果constraint=="baseline"。此参数是可选的，如果constraint=="diagonal"然后基准通道的规模因素将是一个。在这种情况下，偏置参数的估计不会受到影响。一个默认值，如果缺少。
参数：...
Additional arguments accepted by *iwpca(). For instance, a N vector of weights for each observation may be given, otherwise they get the same weight.
*iwpca()接受额外的参数。例如，一个Nvector可给予每个观测值的权重，否则他们得到同样的重量。
参数：aShift, Xmin
For internal use only.
仅供内部使用。
Details
详情----------Details----------
This method uses re-weighted principal component analysis (IWPCA) to fit a the nodel y_n = a + bx_n + eps_n where y_n, a, b, and eps_n are vector of the K and x_n is a scalar.
这种方法使用适合的nodely_n = a + bx_n + eps_n其中y_n，a，b，eps_n向量重新加权主成分分析（IWPCA） K和x_n是一个标量。
The algorithm is: For iteration i: 1) Fit a line L through the data close using weighted PCA with weights \{w_n\}. Let r_n = \{r_{n,1},...,r_{n,K}\} be the K principal components. 2) Update the weights as w_n <- 1 / ∑_{2}^{K} (r_{n,k} + ε_r) where we have used the residuals of all but the first principal component. 3) Find the point a on L that is closest to the line D=(1,1,...,1). Similarily, denote the point on D that is closest to L by t=a*(1,1,...,1).
该算法是：我对于迭代：1）适合行L通过接近使用权重加权PCA的数据\{w_n\}。让r_n = \{r_{n,1},...,r_{n,K}\}是K主要组成部分。 2）更新作为权w_n <- 1 / ∑_{2}^{K} (r_{n,k} + ε_r)我们已经使用了所有的残留物，但在第一主成分。 3）寻找上L行D=(1,1,...,1)最接近点。同样的，表示在D点是最接近Lt=a*(1,1,...,1)。
值----------Value----------
Returns a list that contains estimated parameters and algorithm details;
返回list，包含估计的参数和算法的细节;
参数：a
A double vector (a[1],...,a[K])with offset parameter estimates. It is made identifiable according to argument constraint.
一个doublevector(a[1],...,a[K])offset参数估计。它可辨认根据参数constraint。
参数：b
A double vector (b[1],...,b[K])with scale parameter estimates. It is made identifiable by constraining b[baselineChannel] == 1. These estimates are idependent of argument constraint.
一个doublevector(b[1],...,b[K])尺度参数估计。这是做识别由制约b[baselineChannel] == 1。这些估计是参数constraintidependent。
参数：adiag
If identifiability constraint "diagonal", a double vector (adiag[1],...,adiag[K]), where adiag[1] = adiag[2] = ... adiag[K], specifying the point on the diagonal line that is closest to the fitted line, otherwise the zero vector.
如果辨识约束"diagonal"，doublevector(adiag[1],...,adiag[K])，其中adiag[1] = adiag[2] = ... adiag[K]，最接近的拟合线的对角线上的指定点，否则零向量。
参数：eigen
A KxK matrix with columns of eigenvectors.
一个KxKmatrix与特征向量的列。
参数：converged
TRUE if the algorithm converged, otherwise FALSE.
TRUE如果算法收敛，否则FALSE。
参数：nbrOfIterations
The number of iterations for the algorithm to converge, or zero if it did not converge.
迭代算法收敛，或零，如果它没有收敛。
参数：t0
Internal parameter estimates, which contains no more information than the above listed elements.
内部参数的估计，其中包含不超过上述所列元素的信息。
参数：t
Always NULL.
总是NULL。
作者（S）----------Author(s)----------
Henrik Bengtsson (<a href="http://www.braju.com/R/">http://www.braju.com/R/</a>)
参见----------See Also----------
This is an internal method used by the *calibrateMultiscan() and *normalizeAffine() methods. Internally the function *iwpca() is used to fit a line through the data cloud and the function distanceBetweenLines() to find the closest point to the diagonal (1,1,...,1).
这是一个内部的*calibrateMultiscan()和*normalizeAffine()方法使用的方法。内部的功能*iwpca()用于适合通过云的数据和功能distanceBetweenLines（）找到最接近的点对角线（1,1，...，1）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。
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 2012-2-25 12:04:46
