R语言 genefilter包 rowpAUCs-methods()函数中文帮助文档(中英文对照)

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rowpAUCs-methods(genefilter)
rowpAUCs-methods()所属R语言包：genefilter

                                        Rowwise ROC and pAUC computation
                                         rowwise中华民国和pAUC计算

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Methods for fast rowwise computation of ROC curves and (partial) area under the curve (pAUC) using the simple classification rule x > theta, where theta is a value in the range of x
ROC曲线和（部分）下曲线（pAUC）的面积rowwise快速计算方法，使用简单的分类规则x > theta，其中theta是一个在x范围内的值


用法----------Usage----------


rowpAUCs(x, fac, p=0.1, flip=TRUE, caseNames=c("1", "2"))



参数----------Arguments----------

参数：x
ExpressionSet or numeric matrix. The matrix must not contain NA values.
ExpressionSet或数字matrix。 matrix不能包含NA值。


参数：fac
A factor or numeric or character that can be coerced to a factor. If x is an ExpressionSet, this may also be a character vector of length 1 with the name of a covariate variable in x. fac must have exactly 2 levels. For better control over the classification, use integer values in 0 and 1, where 1 indicates the "Disease" class in the sense of the Pepe et al paper (see below).
一个factor或numeric或character可以强制factor。如果x是ExpressionSet，这也可能是一个字符vector在x协变量的名称长度1。 fac必须有整整2级。为了更好地控制了分类，用0和1的整数值，其中1表示“病”类中的佩佩等纸感（见下文）。


参数：p
Numeric vector of length 1. Limit in (0,1) to integrate pAUC to.
数字vector长度为1。 （0,1）限制，整合pAUC。


参数：flip
Logical. If TRUE, both classification rules x   > theta and x < theta are tested and the (partial) area under the curve of the better one of the two is returned. This is appropriate for the cases in which the classification is not necessarily linked to higher expression values, but instead it is symmetric and one would assume both over- and under-expressed genes for both classes. You can set flip to FALSE if you only want to screen for genes which discriminate Disease from Control with the x > theta rule.
逻辑。如果TRUE，两个分类规则x   > theta和x < theta测试和两个更好的曲线下的面积（部分）是返回。这是为在该分类没有必然的联系，表达值较高的情况下适当的，但相反，它是对称的，将承担这两个类均超过下表达的基因。您可以设置flipFALSE如果你只是想x > theta规则控制疾病的基因歧视的屏幕。


参数：caseNames
The class names that are used when plotting the data. If fac is the name of the covariate variable in the ExpressionSet the function will use its levels as caseNames.
绘制数据时使用的类的名称。如果fac函数将使用其水平作为ExpressionSet caseNames协变量的名称。


Details

详情----------Details----------

Rowwise calculation of Receiver Operating Characteristic (ROC) curves and the corresponding partial area under the curve (pAUC) for a given data matrix or ExpressionSet. The function is implemented in C and thus reasonably fast and memory efficient. Cutpoints (theta are calculated before the first, in between and after the last data value. By default, both classification rules x > theta and x < theta are tested and the (partial) area under the curve of the better one of the two is returned. This is only valid for symmetric cases, where the classification is independent of the magnitude of x (e.g., both over- and under-expression of different genes in the same class).  For unsymmetric cases in which you expect x to be consistently higher/lower in of of the two classes (e.g. presence or absence of a single biomarker) set flip=FALSE or use the functionality provided in the ROC package. For better control over the classification (i.e., the choice of "Disease" and "Control" class in the sense of the Pepe et al paper), argument fac can be an integer in [0,1] where 1 indicates "Disease" and 0 indicates "Control".
受试者工作特征曲线（ROC）的部分区域相应下曲线（pAUC）的数据矩阵或ExpressionSetrowwise计算。该函数在C和实施因此，合理的速度和内存使用效率。切点（theta计算之前，首先，之间和之后的最后一个数据值。默认情况下，这两个分类规则x > theta和x < theta测试和曲线下面积（部分）两个更好的返回，这是唯一有效的对称情况下，分类x（例如，过，并根据不同的基因表达，在同一类）的大小无关对于非对称的情况下，你期望x是一贯的高/低的两个类（如存在或不存在一个单一的生物标志物的情况下）flip=FALSE或ROC包提供的功能为了更好地控制了分类（即，在选择的佩佩等纸感“疾病”和“控制”类），参数fac可以是一个整数[0,1]其中1表示“疾病”，0表示“控制”。


值----------Value----------

An object of class rowROC with the calculated specificities and sensitivities for each row and the corresponding pAUCs and AUCs values. See rowROC for details.
一个类的对象rowROC为每一行和相应的pAUCs和腰围值与计算的特殊性和敏感性。看到rowROC详情。


方法----------Methods----------

Methods exist for rowPAUCs:
方法存在rowPAUCs：




rowPAUCs signature(x="matrix", fac="factor")
rowPAUCssignature(x="matrix", fac="factor")




rowPAUCs signature(x="matrix", fac="numeric")
rowPAUCssignature(x="matrix", fac="numeric")




rowPAUCs signature(x="ExpressionSet")
rowPAUCssignature(x="ExpressionSet")




rowPAUCs signature(x="ExpressionSet", fac="character")
rowPAUCssignature(x="ExpressionSet", fac="character")


作者（S）----------Author(s)----------


Florian Hahne &lt;fhahne@fhcrc.org&gt;



参考文献----------References----------

Schummer M.: Selecting differentially expressed genes from microarray

参见----------See Also----------

rocdemo.sca,
的<code> rocdemo.sca


举例----------Examples----------


library(Biobase)
data(sample.ExpressionSet)

r1 = rowttests(sample.ExpressionSet, "sex")
r2 = rowpAUCs(sample.ExpressionSet, "sex", p=0.1)

plot(area(r2, total=TRUE), r1$statistic, pch=16)
sel <- which(area(r2, total=TRUE) > 0.7)
plot(r2[sel])

## this compares performance and output of rowpAUCs to function pAUC in[＃比较性能和输出rowpAUCs运作pAUC在]
## package ROC [＃包号]
if(require(ROC)){
  ## performance[＃性能]
  myRule = function(x)
    pAUC(rocdemo.sca(truth = as.integer(sample.ExpressionSet$sex)-1 ,
         data = x, rule = dxrule.sca), t0 = 0.1)
  nGenes = 200
  cat("computation time for ", nGenes, "genes:\n")
  cat("function pAUC: ")
  print(system.time(r3 <- esApply(sample.ExpressionSet[1:nGenes, ], 1, myRule)))
  cat("function rowpAUCs: ")
  print(system.time(r2 <- rowpAUCs(sample.ExpressionSet[1:nGenes, ],
  "sex", p=1)))

  ## compare output[＃比较输出]
  myRule2 = function(x)
   pAUC(rocdemo.sca(truth = as.integer(sample.ExpressionSet$sex)-1 ,
                    data = x, rule = dxrule.sca), t0 = 1)
  r4 <-  esApply(sample.ExpressionSet[1:nGenes, ], 1, myRule2)
  plot(r4,area(r2), xlab="function pAUC", ylab="function rowpAUCs",
  main="pAUCs")

  plot(r4, area(rowpAUCs(sample.ExpressionSet[1:nGenes, ],
  "sex", p=1, flip=FALSE)), xlab="function pAUC", ylab="function rowpAUCs",
  main="pAUCs")

  r4[r4<0.5] <- 1-r4[r4<0.5]
  plot(r4, area(r2), xlab="function pAUC", ylab="function rowpAUCs",
  main="pAUCs")
}

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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