fdr.int(OLIN)
fdr.int()所属R语言包：OLIN
Assessment of the significance of intensity-dependent bias
评估依赖强度偏见的意义
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function assesses the significance of intensity-dependent bias by an one-sided random permutation test. The observed average values of logged fold-changes within an intensity neighbourhood are compared to an empirical distribution generated by random permutation. The significance is given by the false discovery rate.
此功能评估依赖强度偏见，片面的随机排列试验的意义。记录内强度邻里倍变化的观测平均值相比，经验分布产生随机置换。的意义是虚假的发现率。
用法----------Usage----------
fdr.int(A,M,delta=50,N=100,av="median")
参数----------Arguments----------
参数：A
vector of average logged spot intensity
平均记录现场强度矢量
参数：M
vector of logged fold changes
向量记录的fold change
参数：delta
integer determining the size of the neighbourhood. The actual window size is (2 * delta+1).
整数确定附近的大小。实际的窗口大小是（2 * delta+1）。“
参数：N
number of random permutations performed for generation of empirical distribution
一代的经验分布进行随机排列的号码
参数：av
averaging of M within neighbourhood by mean or median (default)
平均M内邻里均值或中位数（默认）
Details
详情----------Details----------
The function fdr.int assesses significance of intensity-dependent bias using a one-sided random permutation test. The null hypothesis states the independence of A and M. To test if M depends on A, spots are ordered with respect to A. This defines a neighbourhood of spots with similar A for each spot. Next, a test statistic is defined by calculating the median or mean of M within a symmetrical spot's intensity neighbourhood of chosen size (2 *delta+1). An empirical distribution of the test statistic is produced by calculating for N random intensity orders of spots. Comparing this empirical distribution of median/mean of \code{M} with the observed distribution of median/mean of \code{M}, the independence of M and A is assessed. If M is independent of A, the empirical distribution of median/mean of \code{M} can be expected to be distributed around its mean value. The false discovery rate (FDR) is used to assess the significance of observing positive deviations of median/mean of \code{M}. It indicates the expected proportion of false positives among rejected null hypotheses. It is defined as FDR=q*T/s, where q is the fraction of median/mean of \code{M} larger than chosen threshold c for the empirical distribution, s is the number of neighbourhoods with (median/mean of \code{M})> c for the distribution derived from the original data and T is the total number of neighbourhoods in the original data. Varying threshold c determines the FDR for each spot neighbourhood. FDRs equal zero are set to FDR=1/T*N for computational reasons, as log10(FDR) is plotted by sigint.plot. Correspondingly, the significance of observing negative deviations of median/mean of \code{M} can be determined. If the neighbourhood
功能fdr.int评估依赖强度偏置使用一种片面的随机排列试验的意义。空假说和研究要测试的独立性，如果M取决于A，点到A排列，这定义了一个类似的每一个点附近的斑点。接下来，定义一个测试统计计算中位数或平均M在一个对称的点的大小选择强度居委会（2 *delta+1）。一个检验统计量的经验分布计算N点随机强度订单生产。这median/mean of \code{M}与median/mean of \code{M}的观测分布，独立性M和A评估的经验分布的比较。如果M的A是独立的，经验分布median/mean of \code{M}可以预计将围绕其平均值的分布。虚假的发现率（FDR）是用来评估观察median/mean of \code{M}的正偏差的意义。它表示拒绝虚无假设之间的误报预期的比例。它被定义为FDR=q*T/s，其中q是的median/mean of \code{M}比选择的经验分布阈值C，大的一小部分s是(median/mean of \code{M})> c分布的居民区来自原始数据和T街区是在原始数据的总数。不同的阈值Ç决定为每个点附近的FDR。 FDRs等于零计算的原因，FDR=1/T*N为log10(FDR)sigint.plot绘制。相应的，意义观察负偏差median/mean of \code{M}的，可确定。如果邻里
值----------Value----------
A list of vector containing the false discovery rates for positive (FDRp) and negative (FDRn) deviations of
为假阳性发现率（FDRp）和阴性（FDRn）偏差的向量列表，其中包含
注意----------Note----------
The same functionality but with our input and output formats is offered by fdr.int
提供相同的功能，但我们的输入和输出格式fdr.int
作者（S）----------Author(s)----------
Matthias E. Futschik (<a href="http://itb.biologie.hu-berlin.de/~futschik">http://itb.biologie.hu-berlin.de/~futschik</a>)
参见----------See Also----------
fdr.int2,p.int, fdr.spatial, sigint.plot
fdr.int2，p.int，fdr.spatial，sigint.plot
举例----------Examples----------
# To run these examples, delete the comment signs (#) in front of the commands.[要运行这些例子，在前面的命令中删除注释符号（＃）。]
#[]
# LOADING DATA NOT-NORMALISED[加载数据没有，正规化]
# data(sw)[数据（SW）]
# CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS[作者：现货，邻里意义的计算]
# For this example, N was chosen rather small. For "real" analysis, it should be larger.[对于这个例子，N的选择相当小。 “真实”的分析，它应该更大。]
# FDR <- fdr.int(maA(sw)[,1],maM(sw)[,1],delta=50,N=10,av="median")[FDR（MAA（SW）[1]，MAM（SW）[1]，δ= 50，N = 10，AV =“中位数”）< - fdr.int]
# VISUALISATION OF RESULTS[结果的可视化]
# sigint.plot(maA(sw)[,1],maM(sw)[,1],FDR$FDRp,FDR$FDRn,c(-5,-5))[（SW）sigint.plot（MAA [1]，MAM（SW）[1]，FDR$ FDRp，“FDR$ FDRn，C（-5，-5））]
# LOADING NORMALISED DATA[装载正规化的资料]
# data(sw.olin)[数据（sw.olin）]
# CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS [作者：现货，邻里意义的计算]
# FDR <- fdr.int(maA(sw.olin)[,1],maM(sw.olin)[,1],delta=50,N=10,av="median")[FDR（MAA（sw.olin）[1] MAM（sw.olin）的[1]，δ= 50，N = 10，AV =“中位数”）< - fdr.int]
# VISUALISATION OF RESULTS[结果的可视化]
# sigint.plot(maA(sw.olin)[,1],maM(sw.olin)[,1],FDR$FDRp,FDR$FDRn,c(-5,-5))[（sw.olin）sigint.plot（MAA [1] MAM（sw.olin）的[1]，FDR$ FDRp，“FDR$ FDRn，C（-5，-5））]
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 08:19:36
