calibrateMultiscan.matrix(aroma.light)
calibrateMultiscan.matrix()所属R语言包：aroma.light
Weighted affine calibration of a multiple re-scanned channel
重新扫描多通道加权仿射校准
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Weighted affine calibration of a multiple re-scanned channel.
重新扫描多通道的加权仿射校准。
用法----------Usage----------
参数----------Arguments----------
参数：X
An NxK matrix (K>=2) where the columns represent the multiple scans of one channel (a two-color array contains two channels) to be calibrated.
NxKmatrix（钾> = 2），其中列代表一个通道的多个扫描（两色阵列包含两个通道）进行校准。
参数：weights
If NULL, non-weighted normalization is done. If data-point weights are used, this should be a vector of length N of data point weights used when estimating the normalization function.
如果NULL，非加权标准化完成。如果使用的数据点的权重，这应该是一个长度为N的数据点的权重vector估计标准化的功能时使用。
参数：typeOfWeights
A character string specifying the type of weights given in argument weights.
一个character字符串，指定类型参数weights给定的权重。
参数：method
A character string specifying how the estimates are robustified. See *iwpca() for all accepted values.
一个character字符串，指定如何抗差估计。 *iwpca()所有公认的价值观。
参数：constraint
Constraint making the bias parameters identifiable. See *fitIWPCA() for more details.
约束使得偏置参数识别。看到*fitIWPCA()更多细节。
参数：satSignal
Signals equal to or above this threshold is considered saturated signals.
等于或高于此阈值的信号被认为是饱和的信号。
参数：...
Other arguments passed to *fitIWPCA() and in turn *iwpca(), e.g. center (see below).
其他参数传递给*fitIWPCA()和反过来*iwpca()，例如： center（见下文）。
参数：average
A function to calculate the average signals between calibrated scans.
一个function计算平均校准扫描之间的信号。
参数：deviance
A function to calculate the deviance of the signals between calibrated scans.
一个function计算偏差校准扫描之间的信号。
参数：project
If TRUE, the calibrated data points projected onto the diagonal line, otherwise not. Moreover, if TRUE, argument average is ignored.
如果TRUE，投射到对角线的校准数据点，否则不是。此外，如果TRUE，参数average被忽略。
参数：.fitOnly
If TRUE, the data will not be back-transform.
如果TRUE，数据不会回来变换。
Details
详情----------Details----------
Fitting is done by iterated re-weighted principal component analysis (IWPCA).
拟合迭代重新加权主成分分析（IWPCA）。
值----------Value----------
If average is specified or project is TRUE, an Nx1 matrix is returned, otherwise an NxK matrix is returned. If deviance is specified, a deviance Nx1 matrix is returned as attribute deviance. In addition, the fitted model is returned as attribute modelFit.
如果average指定或project是TRUE，NX1matrix返回，否则返回一个NxKmatrix。如果deviance指定，越轨NX1matrix属性deviance返回。此外，拟合模型返回的属性modelFit。
负，不积极，饱和值----------Negative, non-positive, and saturated values----------
Affine multiscan calibration applies also to negative values, which are therefor also calibrated, if they exist.
仿射的MultiScan校准也适用于为负值，为此还校准，如果它们存在。
Saturated signals in any scan are set to NA. Thus, they will not be used to estimate the calibration function, nor will they affect an optional projection.
饱和信号在任何扫描设置NA的。因此，他们不会被用来估计校准功能，也不会影响可选的投影。
遗漏值----------Missing values----------
Only observations (rows) in X that contain all finite values are used in the estimation of the alibration functions. Thus, observations can be excluded by setting them to NA.
唯一的意见（行）X包含所有有限值估计的alibration功能的。因此，观测，可以排除设置NA。
加权标准化----------Weighted normalization----------
Each data point/observation, that is, each row in X, which is a vector of length K, can be assigned a weight in [0,1] specifying how much it should affect the fitting of the calibration function. Weights are given by argument weights, which should be a numeric vector of length N. Regardless of weights, all data points are calibrated based on the fitted calibration function.
每个数据点/观察，也就是说，每行X，这是一个长度为k的向量，可以被分配在[0,1]重量指定多少应该影响拟合校准功能。权重给出参数weights，这应该是一个numericvector长度为N的重量，所有数据点的校准拟合校准功能的基础上。
鲁棒性----------Robustness----------
By default, the model fit of multiscan calibration is done in L_1 (method="L1"). This way, outliers affect the parameter estimates less than ordinary least-square methods.
默认情况下，模型拟合的MultiScan校准完成L_1（method="L1"）。通过这种方式，离群影响参数估计值小于普通最小二乘方法。
When calculating the average calibrated signal from multiple scans, by default the median is used, which further robustify against outliers.
计算平均校准信号从多个扫描时，默认情况下，中位数是用来，进一步robustify了对离群。
For further robustness, downweight outliers such as saturated signals, if possible.
为进一步的鲁棒性，离群downweight如饱和信号，如果可能的话。
Tukey's biweight function is supported, but not used by default because then a "bandwidth" parameter has to selected. This can indeed be done automatically by estimating the standard deviation, for instance using MAD. However, since scanner signals have heteroscedastic noise (standard deviation is approximately proportional to the non-logged signal), Tukey's bandwidth parameter has to be a function of the signal too, cf. loess. We have experimented with this too, but found that it does not significantly improve the robustness compared to L_1. Moreover, using Tukey's biweight as is, that is, assuming homoscedastic noise, seems to introduce a (scale dependent) bias in the estimates of the offset terms.
Tukey的biweight功能的支持，但默认情况下不使用，因为“带宽”参数选定。这确实是可以自动完成的，估计的标准偏差，例如，用疯狂。然而，由于扫描仪信号有异方差的噪声（标准差大约是成正比的非记录的信号），杜克的带宽参数是一个信号的功能太多，比照。 loess。我们已经尝试过，但发现，它并没有显着提高鲁棒性比L_1。此外，使用Tukey的biweight，那就是，假设同方差的噪音，似乎引入偏移方面的估计（规模而定）偏见。
使用已知的/原先估计的偏移----------Using a known/previously estimated offset----------
If the scanner offsets can be assumed to be known, for instance, from prior multiscan analyses on the scanner, then it is possible to fit the scanner model with no (zero) offset by specifying argument center=FALSE. Note that you cannot specify the offset. Instead, subtract it from all signals before calibrating, e.g. Xc <- calibrateMultiscan(X-e, center=FALSE) where e is the scanner offset (a scalar). You can assert that the model is fitted without offset by stopifnot(all(attr(Xc, "modelFit")$adiag == 0)).
如果扫描仪可以假设，例如，被称为偏移，从扫描仪上之前的MultiScan分析，那么它有可能以适应抵消没有指定参数center=FALSE（零）扫描仪型号。请注意，您不能指定偏移。相反，来自所有信号减去前校准，例如Xc <- calibrateMultiscan(X-e, center=FALSE)e是扫描仪的偏移量（纯量）。可以断言，该模型没有安装stopifnot(all(attr(Xc, "modelFit")$adiag == 0))抵销。
作者（S）----------Author(s)----------
Henrik Bengtsson (<a href="http://www.braju.com/R/">http://www.braju.com/R/</a>)
参考文献----------References----------
<br>
参见----------See Also----------
1. Calibration and Normalization. *normalizeAffine(). For more information see matrix.
1. Calibration and Normalization。 *normalizeAffine()。欲了解更多信息，请参阅matrix。
举例----------Examples----------
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。
|
发表于 2012-2-25 12:04:23
