votingLinearPredictor(WGCNA)
votingLinearPredictor()所属R语言包：WGCNA
Voting linear predictor
投票线性预测
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Predictor based on univariate regression on all or selected given features that pools all predictions using weights derived from the univariate linear models.
预测单因素回归对所有或选定的功能的基础上，池预测使用来自单变量线性模型的权重。
用法----------Usage----------
votingLinearPredictor(
x, y, xtest = NULL,
classify = FALSE,
CVfold = 0,
randomSeed = 12345,
assocFnc = "cor", assocOptions = "use = 'p'",
featureWeightPowers = NULL, priorWeights = NULL,
weighByPrediction = 0,
nFeatures.hi = NULL, nFeatures.lo = NULL,
dropUnusedDimensions = TRUE,
verbose = 2, indent = 0)
参数----------Arguments----------
参数：x
Training features (predictive variables). Each column corresponds to a feature and each row to an observation.
培训功能（预测变量）。每一列对应于一个功能和每一行的一个观察。
参数：y
The response variable. Can be a single vector or a matrix with arbitrary many columns. Number of rows (observations) must equal to the number of rows (observations) in x.
响应变量。可以是一个向量或矩阵与任意多个列。的行数（观测值）必须等于（观测值）x中的行数。
参数：xtest
Optional test set data. A matrix of the same number of columns (i.e., features) as x. If test set data are not given, only the prediction on training data will be returned.
选用的测试数据。 A矩阵的列数相同（即功能）x。如果没有给出测试数据集，训练数据的预测将被退回。
参数：classify
Should the response be treated as a categorical variable? Classification really only works with two classes. (The function will run for multiclass problems as well, but the results will be sub-optimal.)
应该被视为分类变量的响应？分类真的只有两班。 （此功能将运行的多类问题为好，但结果将是次优的。）
参数：CVfold
Optional specification of cross-validation fold. If 0 (the default), no cross-validation is performed.
可选规格的交叉验证倍。如果为0（默认值），不进行交叉验证。
参数：randomSeed
Random seed, used for observation selection for cross-validation. If NULL, the random generator is not reset.
随机种子，用于交叉验证观察选择。如果NULL，随机数生成器不复位。
参数：assocFnc
Function to measure association. Usually a measure of correlation, for example Pearson correlation or bicor.
功能来测量协会。通常衡量的相关性，例如，Pearson相关或bicor。
参数：assocOptions
Character string specifying the options to be passed to the association function.
字符串指定要传递给联想功能的选项。
参数：featureWeightPowers
Powers to which to raise the result of assocFnc to obtain weights. Can be a single number or a vector of arbitrary length; the returned value will contain one prediction per power.
权力assocFnc获得权重提高的结果。可以是单数或一个任意长度的向量，每个电源的返回值将包含一个预测。
参数：priorWeights
Prior weights for the features. If given, must be either (1) a vector of the same length as the number of features (columns in x); (2) a matrix of dimensions length(featureWeightPowers)x(number of features); or (3) array of dimensions (number of response variables)xlength(featureWeightPowers)x(number of features).
在此之前的权重为特点。如果给定的，必须是（1）的功能（在x），（2）的矩阵的尺寸的长度（featureWeightPowers）×（功能的数量）或（列数相同的长度的矢量3）数组的维响应变量的数量xlength（featureWeightPowers）×（功能）。
参数：weighByPrediction
(Optional) power to downweigh features that are not well predicted between training and test sets. See details.
（可选）电源，以downweigh的功能没有得到很好的训练和测试集之间的预测。查看详细信息。
参数：nFeatures.hi
Optional restriction of the number of features to use. If given, this many features with the highest association and lowest association (if nFeatures.lo is not given) will be used for prediction.
可使用功能的数量限制。如果给协会的最高和最低协会（nFeatures.lo如果），这许多功能将被用于预测。
参数：nFeatures.lo
Optional restriction of the number of lowest (i.e., most negatively) associated features to use. Only used if nFeatures.hi is also non-NULL.
可选的最低数目的限制（即，最负的）相关联的功能，以使用。仅用于nFeatures.hi如果也非NULL。
参数：dropUnusedDimensions
Logical: should unused dimensions be dropped from the result?
逻辑：从结果中删除未使用的尺寸呢？
参数：verbose
Integer controling how verbose the diagnostic messages should be. Zero means silent.
整数操纵如何详细的诊断消息。零表示沉默。
参数：indent
Indentation for the diagnostic messages. Zero means no indentation, each unit adds two spaces.
缩进的诊断消息。零表示无压痕，每个单元增加两个空格。
Details
详细信息----------Details----------
The predictor calculates the association of each (selected) feature with the response and uses the association to calculate the weight of the feature as sign(association) * (association)^featureWeightPower. Optionally, this weight is multiplied by priorWeights. Further, a feature prediction weight can be used to downweigh features that are not well predicted by other features (see below).
预测器的响应和计算每个（选定的）功能的关联使用的关联，来计算的权重的特征作为sign(association) * (association)^featureWeightPower。可选的，这样的重量乘以priorWeights。另外，一个功能预测重量可以用于downweigh功能没有得到很好的预测的其他功能（见下文）。
For classification, the (continuous) result of the above calculation is turned into ordinal values essentially by rounding.
分类，上述计算的结果（连续）变成序号值基本上是由四舍五入。
If features exhibit non-trivial correlations among themselves (such as, for example, in gene expression data), one can attempt to down-weigh features that do not exhibit the same correlation in the test set. This is done by using essentially the same predictor to predict _features_ from all other features in the test data (using the training data to train the feature predictor). Because test features are known, the prediction accuracy can be evaluated. If a feature is predicted badly (meaning the error in the test set is much larger than the error in the cross-validation prediction in training data), it may mean that its quality in the training or test data is low (for example, due to excessive noise or outliers). Such features can be downweighed using the argument weighByPrediction. The extra factor is min(1, (root mean square prediction error in test set)/(root mean square cross-validation prediction error in the trainig data)^weighByPrediction), that is it is never bigger than 1.
如果功能表现出非平凡的彼此之间的相关性（诸如，例如，在基因表达数据），一个可以尝试下权衡的功能，不表现出的相同的相关性的测试集。这是通过使用本质上是相同的预测器预测_features_在测试数据（使用训练数据来训练特征预测）从所有其他功能。由于测试功能是已知的，可以评估预测精度。如果某个特性预测严重（这意味着在测试集的错误是远远大于在训练数据中的交叉验证预测中的错误），则这可能意味着其在训练或测试数据的质量是低的（例如，由于过度的噪音或异常值）。这些功能可以使用参数weighByPredictiondownweighed。的额外因素是min（1，（均方根在测试集的预测误差）/（根均方预测误差在trainig数据交叉验证）^ weighByPrediction），那就是它永远不会大于1。
值----------Value----------
A list with the following components:
以下组件列表：
参数：predicted
The back-substitution prediction on the training data. Normally an array of dimensions (number of observations) x (number of response variables) x length(featureWeightPowers), but unused are dropped unless dropUnusedDimensions = FALSE.
背面替代训练数据的预测。正常情况下，维数组（若干意见）×（响应变量的数量）×的长度（featureWeightPowers），但尚未使用的都将被丢弃，除非dropUnusedDimensions = FALSE。
参数：weightBase
Absolute value of the associations of each feature with each response.
的关联的每个特征与每个响应的绝对值。
参数：variableImportance
The weight of each feature in the prediction (including the sign).
预测中的每一个功能（包括符号）的重量。
参数：predictedTest
If input xtest is non-NULL, the predicted test response, in format analogous to predicted above.
如果输入xtest非NULL，预计测试响应，格式类似于predicted以上。
参数：CVpredicted
If input CVfold is non-zero, cross-validation prediction on the training data.
如果输入CVfold是非零的，在训练数据上的交叉验证预测。
注意----------Note----------
It makes little practical sense to supply neither xtest nor CVfold since the prediction accuracy on training data will be highly biased.
它没有什么实际意义，提供既不xtest，也不CVfold因为训练数据的预测精度将充满偏见。
（作者）----------Author(s)----------
Peter Langfelder
参见----------See Also----------
bicor for robust correlation that can be used as an association measure
bicor鲁棒的相关性，可以用来作为一个关联措施
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。
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发表于 2012-10-1 22:26:36
