fdr.spatial(OLIN)
fdr.spatial()所属R语言包：OLIN
Assessment of the significance of spatial bias
评估空间偏见的意义
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function assesses the significance of spatial bias by a one-sided random permutation test. This is achieved by comparing the observed average values of logged fold-changes within a spot's spatial neighbourhood with an empirical distribution generated by random permutation. The significance of spatial bias is given
此功能评估空间偏见的一种片面的随机排列试验的意义。这是通过比较观察到的平均值记录倍的变化，产生随机置换的经验分布在一个点的空间邻里。空间偏见的意义
用法----------Usage----------
fdr.spatial(X,delta=2,N=100,av="median",edgeNA=FALSE)
参数----------Arguments----------
参数：X
matrix of logged fold changes. For alternative input format, see fdr.spatial2.
矩阵记录倍的变化。替代的输入格式，请参阅fdr.spatial2。
参数：delta
integer determining the size of spot neighbourhoods ((2*delta+1)x(2*delta+1)).
整数确定现货街区的大小（(2*delta+1)x(2*delta+1)）。
参数：N
number of random permutations performed for generation of empirical background distribution
数代的经验背景分布进行随机排列
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）
参数：edgeNA
treatment of edges of array: For edgeNA=TRUE, the significance of a neighbourhood (defined by a sliding window) is set to NA, if the neighbourhood extends over the edges of the matrix.
阵列的边缘处理：edgeNA=TRUE，设置为NA个居委会（滑动窗口定义）的意义，如果邻里矩阵的边缘延伸。
Details
详情----------Details----------
The function fdr.spatial assesses the significance of spatial bias using a one-sided random permutation test. The null hypothesis states random spotting i.e. the independence of log ratio M and spot location. First, a neighbourhood of a spot is defined by a two dimensional square window of chosen size ((2*delta+1)x(2*delta+1)). Next, a test statistic is defined by calculating the median or mean of M within a symmetrical spot's neighbourhood. An empirical distribution of median/mean of \code{M} is generated based N random permutations of the spot locations on the array. The randomisation and calculation of median/mean of \code{M} is repeated N times. 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 spot location can be assessed. If M is independent of spot's location, the empirical distribution can be expected to be distributed around its mean value. To assess the significance of observing positive deviations of median/mean of \code{M}, the false discovery rate (FDR) is used. It indicates the expected proportion of false discoveries 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 on the array. FDRs equal zero are set to FDR=1/T*N. Varying threshold c determines the FDR for each spot neighbourhood. Correspondingly, the significance
功能fdr.spatial评估的意义空间的偏见，用一种片面的随机排列试验。空假说，即独立的log比M点位置的随机发现。首先，一个一个点的附近，是指由一个二维选择大小的正方形窗口（（2 *Delta+1）×（2 *Delta+1））。接下来，定义一个测试统计计算中位数或平均M在一个对称的点的附近。 median/mean of \code{M}经验分布生成基于N随机排列阵列上的点的位置。随机和计算median/mean of \code{M}的的重复N倍。 median/mean of \code{M}与median/mean of \code{M}的观测分布，M点位置的独立性，可以评估的经验分布的比较。 M如果是独立点的位置，可以预期的经验分布，周围分布及其均值。评估median/mean of \code{M}，错误发现率（FDR）是用来观察正偏差的意义。它表示拒绝虚无假设中的虚假发现预期的比例。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是阵列上的社区总数。 FDRs等于零设置FDR=1/T*N的。不同的阈值Ç决定为每个点附近的FDR。相应地，意义
值----------Value----------
A list of matrices 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）偏差的矩阵列表
注意----------Note----------
The same functionality but with our input and output formats is offered by fdr.spatial
提供相同的功能，但我们的输入和输出格式fdr.spatial
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----------
p.spatial, fdr.int, sigxy.plot, fdr.spatial2
p.spatial，fdr.int，sigxy.plot，fdr.spatial2
举例----------Examples----------
# To run these examples, delete the comment signs before the commands.[要运行这些例子，删除命令之前的评论迹象。]
#[]
# LOADING DATA[加载数据]
# data(sw)[数据（SW）]
# M <- v2m(maM(sw)[,1],Ngc=maNgc(sw),Ngr=maNgr(sw),[v2m M“ - （MAM（SW）[1]，NGC = maNgc（SW），NGR = maNgr（SW），]
# Nsc=maNsc(sw),Nsr=maNsr(sw),main="MXY plot of SW-array 1")[NSC = maNsc（SW），NSR = maNsr（SW），主要=“SW阵列1 MXY图”）]
#[]
# CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS[作者：现货，邻里意义的计算]
# For this illustration, 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.spatial(M,delta=2,N=10,av="median",edgeNA=TRUE)[< - fdr.spatialFDR（男，Delta= 2，N = 10，AV =“中间”，edgeNA = TRUE时）]
# sigxy.plot(FDR$FDRp,FDR$FDRn,color.lim=c(-5,5),main="FDR")[sigxy.plot（FDR$ FDRp，FDR$ FDRn，color.lim = C（-5,5），主要=“FDR”）]
#[]
# LOADING NORMALISED DATA[装载正规化的资料]
# data(sw.olin)[数据（sw.olin）]
# M<- v2m(maM(sw.olin)[,1],Ngc=maNgc(sw.olin),Ngr=maNgr(sw.olin),[M“ - v2m（MAM（sw.olin）的[1]，NGC = maNgc（sw.olin），NGR = maNgr（sw.olin），]
# Nsc=maNsc(sw.olin),Nsr=maNsr(sw.olin),main="MXY plot of SW-array 1")[的NSC = maNsc（sw.olin）（NSR = maNsr sw.olin），主要=“SW阵列1 MXY图”）]
#[]
# CALCULATION OF SIGNIFICANCE OF SPOT NEIGHBOURHOODS[作者：现货，邻里意义的计算]
# FDR <- fdr.spatial(M,delta=2,N=10,av="median",edgeNA=TRUE)[< - fdr.spatialFDR（男，Delta= 2，N = 10，AV =“中间”，edgeNA = TRUE时）]
# VISUALISATION OF RESULTS[结果的可视化]
# sigxy.plot(FDR$FDRp,FDR$FDRn,color.lim=c(-5,5),main="FDR")[sigxy.plot（FDR$ FDRp，FDR$ FDRn，color.lim = C（-5,5），主要=“FDR”）]
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 08:19:54
