RS.generator(stepwiseCM)
RS.generator()所属R语言包：stepwiseCM
A function to generate reclassification score.
函数生成改叙得分。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A function to calculates the reclassification score (RS) using both clinical and molecular data.
一个函数计算得分（RS）的临床和分子数据的重新分类。
用法----------Usage----------
RS.generator(pred.cli, pred.gen, train.label, prox.gen, prox.cli,
type = c("rank", "proximity", "both"))
参数----------Arguments----------
参数：pred.cli
A vector containing the predicted labels of training set from clinical data.
一个向量，训练的预测从临床数据设置的标签。
参数：pred.gen
A vector containing the predicted labels of training set from genomic data.
一个向量，训练的预测从基因组数据设置标签。
参数：train.label
A vector of the class labels (0 or 1) of the training set. NOTE: response values should be numerical not factor.
一类标签的训练集（0或1）的向量。注：响应值应该是数值不是因素。
参数：prox.gen
A square matrix, size of ncol(train.gen), contains the proximity values between training set in the genomic data space.
一个方阵，NCOL（train.gen）的大小，包含接近值之间在基因组数据的空间设置的培训。
参数：prox.cli
A matrix, size of ncol(test.cli) by ncol(train.cli), contains the proximity values between the test set and the training set in the clinical data space.
矩阵，由NCOL（train.cli）（test.cli）NCOL大小，包含接近值之间的测试集和训练中的临床数据空间设置。
参数：type
Which values are used to construct the reclassification score (RS)? There are three options available: proximity, rank and both. If set to proximity, RS will be calculated directly from the proximity value. If set to rank, RS calculate from the rank of proximity values (more robust). If set to both, both of them will be calculated. Default is rank.
用来构造重新定级评分（RS）的值？有三种选择：接近，排名都。如果设置为接近，RS将被计算直接从邻近的价值。如果设置排名，遥感计算从排名接近值（更强大）。如果设置两个，他们都将被计算。默认是排名。
Details
详情----------Details----------
For each test sample, RS is calculated using the given classification results from clinical and genomic data. Algorithm project each test sample onto the clinical space check its neighborhood, also tries to gain some information about this test samples "pseudo" neighbors in the genomic space by the indirect mapping. If algorithm finds that the location of this test samples in the clinical space are more "safe" (more neighbors are correctly classified) and the location in the genomic space is surrounded by wrongly classified samples, then it will give this test sample high RS score and vice versa. After obtaining the RS, user can order them in descending order and pass the top ranked certain portion (decided by user) of samples to genomic data to classify.
对于每个测试样品，RS的计算方法，从临床和基因组数据的使用给定的分类结果。算法的项目，每个检查到临床的空间试验样品及其邻近区域，还试图获得有关此测试的样本由基因组的间接映射空间中的“伪”邻居的一些信息。如果算法认为，本次测试样品在临床上的空间位置更“安全”（更多的邻居是正确归类）和基因组空间的位置，周围被错划的样品，然后它会给此测试样品高RS得分和反之亦然。获得RS后，用户可以责令降序，并通过一流的一定比例（由用户决定）样品基因组数据进行分类。
值----------Value----------
If the "type" set to "rank" ("proximity"), then RS will be a vector of RS calculated from the ranking (proximity) approach , otherwise RS will be a matrix of RS, with two columns and size of rows equal number of test samples, calculated using the both approaches.
如果在“类型”设置为“排名”（“近水楼台”），那么RS将是一个从排名（接近）的方法计算出的RS向量，否则RS将是一个矩阵的RS，与两列和大小行同等数量的测试样品，使用这两种方法计算。
作者（S）----------Author(s)----------
Askar Obulkasim
Maintainer: Askar Obulkasim <askar.wubulikasimu@vumc.nl>
举例----------Examples----------
data(CNS)
train.cli <- t(CNS$cli[1:40,])
test.cli <- t(CNS$cli[41:60,])
train.gen <- CNS$mrna[,1:40]
train.label <- CNS$class[1:40]
pred.cli <- Classifier(train = train.cli, train.label = train.label, type = "GLM_L1",
CVtype = "k-fold", outerkfold = 2, innerkfold = 2)
pred.gen <- Classifier(train = train.gen, train.label = train.label, type = "GLM_L1",
CVtype = "k-fold", outerkfold = 2, innerkfold = 2)
prox <- Proximity(train.cli, train.label, test.cli, train.gen, N = 2)
RS.generator(pred.cli$P.train, pred.gen$P.train, train.label, prox$Prox.gen,
prox$Prox.cli, type = "both")
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 15:13:26
