makeFdrPlot(rtfbs)
makeFdrPlot()所属R语言包：rtfbs
Plot FDR
图FDR
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Plot one or more false discovery rate data.frame(s). False discovery rate data.frame(s) are created using calc.fdr. This plot enables the user to get an idea of an appropreate FDR threshold to use for determining likely binding sites.
绘制一个或多个虚假的发现率数据框（S）。虚假的发现率数据框（S）是使用calc.fdr的。此图使用户能够获得一个想法，一个appropreate FDR阈值用于确定可能的结合位点。
用法----------Usage----------
makeFdrPlot(x, xlim = NULL, ylim = NULL, col = "black",
lty = 1, ...)
参数----------Arguments----------
参数：x
data.frame created by calc.fdr, or list of such data frames
数据框创建的calc.fdr，这样的数据框或列表
参数：xlim
If given, specifies the x coordinate boundaries. Otherwise these are taken by the observed range of scores
如果给出，指定的x坐标的边界。否则，这些被观察到的分数范围
参数：ylim
If given, specifies the y coordinate boundaries. Otherwise these are taken by the observed range of FDR values.
如果给出，指定边界的y坐标。否则，这些FDR值的范围内所观察到的。
参数：col
The color to plot (see par() for description). Will be recycled to the number of data.frames in x.
绘制的颜色（见par()的描述）。将被再循环到在x的数目data.frames。
参数：lty
The line type (see par() for description). Will be recycled to the number of data.frames in x.
该生产线的类型（见par()的描述）。将被再循环到在x的数目data.frames。
参数：...
Additional arguments to be passed to plot()
额外的参数被传递到绘制（）
实例----------Examples----------
require("rtfbs")
exampleArchive <- system.file("extdata", "NRSF.zip", package="rtfbs")
seqFile <- "input.fas"
unzip(exampleArchive, seqFile)
# Read in FASTA file "input.fas" from the examples into an [阅读在FASTA的文件“input.fas”中的例子成]
# MS (multiple sequences) object[多个序列（MS）对象]
ms <- read.ms(seqFile);
pwmFile <- "pwm.meme"
unzip(exampleArchive, pwmFile)
# Read in Position Weight Matrix (PWM) from MEME file from[阅读中的位置权重矩阵（PWM）MEME文件]
# the examples into a Matrix object[到Matrix对象的例子]
pwm <- read.pwm(pwmFile)
# Build a 3rd order Markov Model to represent the sequences[建立一个3阶Markov模型来表示序列]
# in the MS object "ms". The Model will be a list of[在MS对象“MS”。该模型将一个列表]
# matrices corrisponding in size to the order of the [矩阵的大小的顺序corrisponding]
# Markov Model[马尔可夫模型]
mm <- build.mm(ms, 3);
# Match the PWM against the sequences provided to find[对找到的序列匹配PWM]
# possible transcription factor binding sites. A [可能的转录因子结合位点。一]
# Features object is returned, containing the location[返回对象特点的，包含位置]
# of each possible binding site and an associated score.[每个可能的结合位点和一个相关的得分。]
# Sites with a negative score are not returned unless [除非网站不会返回一个负的成绩]
# we set threshold=-Inf as a parameter.[我们作为一个参数设置的阈值=-INF。]
cs <- score.ms(ms, pwm, mm)
# Generate a sequence 1000 bases long using the supplied[术语使用提供的1000个碱基生成一个序列]
# Markov Model and random numbers[马尔可夫模型和随机数]
v <- simulate.ms(mm, 10000)
# Match the PWM against the sequences provided to find[对找到的序列匹配PWM]
# possible transcription factor binding sites. A [可能的转录因子结合位点。一]
# Features object is returned, containing the location[返回对象特点的，包含位置]
# of each possible binding site and an associated score.[每个可能的结合位点和一个相关的得分。]
# Sites with a negative score are not returned unless [除非网站不会返回一个负的成绩]
# we set threshold=-Inf as a parameter. Any identified[我们作为一个参数设置的阈值=-INF。任何确定的]
# binding sites from simulated data are false positives[从模拟数据的结合位点是误报]
# and used to calculate False Discovery Rate[并用于计算假发现率]
xs <- score.ms(v, pwm, mm)
# Calculate the False Discovery Rate for each possible[每一个可能的假发现率计算]
# binding site in the Features object CS. Return[结合位点的特点反对CS。返回]
# a mapping between each binding site score and the[每个结合位点的得分和之间的映射]
# associated FDR.[相关的FDR。]
fdr <- calc.fdr(ms, cs, v, xs)
# Plot the False Discovery Rate v.s. score for one or[绘制假发现率V.S.的得分为一个或]
# more groups. To plot multiple FDR/Score mapping [更多的组。要绘制多个FDR /分数映射]
# data frames on a single plot, simply supply a list[在同一张图上的数据框，只需提供一个列表]
# of FDR/Score data.frames [FDR /分数data.frames]
makeFdrPlot(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 2012-9-28 22:46:02
