writeReport(CellNOptR)
writeReport()所属R语言包:CellNOptR
Write a report of a CellNOptR analysis
写一个一个CellNOptR分析报告
译者:生物统计家园网 机器人LoveR
描述----------Description----------
This function writes a short report of a CellNOptR analysis in an html page, that is linked to the various graphs produced
这个函数写在简短的报告,HTML页一个CellNOptR分析的,链接到所产生的各种图表
用法----------Usage----------
writeReport(ModelOriginal, ModelOpt, optimResT1, optimResT2, CNOlist, directory, namesFiles = list(dataPlot = NA, evolFit1 = NA, evolFit2 = NA, SimResults2 = NA, SimResults1 = NA, Scaffold = NA, tscaffold = NA, wscaffold = NA, PKN = NA, wPKN = NA, nPKN = NA), namesData = list(CNOlist=NA, Model=NA),resE)
参数----------Arguments----------
参数:ModelOriginal
the original previous knowledge network (i.e. model that you loaded) in a model list format
原来以前的知识网络模型中的列表格式(即模型,你装)
参数:ModelOpt
the model that was actually used for optimisation (i.e. the scaffold network, after compression and expansion) in a model list format
实际上被用于优化(即脚手架网络,压缩和膨胀后)模型在模型列表格式
参数:optimResT1
the results of the optimisation at t1, as output by gabinaryT1
由gabinaryT1在T1输出的优化结果,
参数:optimResT2
the results of the optimisation at t2, as output by gabinaryT2. Always set to NA here since the t2 optimisation is not implemented in this version
在T2优化的结果,输出由gabinaryT2。始终设置为NA这里没有在这个版本中实现自T2优化
参数:CNOlist
a CNOlist
1 CNOlist
参数:directory
the name of a new directory that will be created, where your results will be moved
将创建一个新的目录名称,您的结果将被移到
参数:namesFiles
a list of the names of the files that should have been created. Depending on whether a t2 optimisation was performed or not, all or some of the following fields are expected: dataPlot,evolFit1,evolFit2,SimResults2,SimResults1,Scaffold,tscaffold,wscaffold,PKN,wPKN,nPKN
应该已创建的文件的名称列表。根据是否T2进行优化或没有,全部或部分以下领域预计:dataPlot,evolFit1,evolFit2 SimResults2,SimResults1,脚手架tscaffold,wscaffold,PKN,wPKN,nPKN
参数:namesData
a list with fields $CNOlist and $Model that contain strings that are meaningful identifiers of your data and previous knowledge network (for your own record)
与字段列表$CNOlist和$Model包含字符串,是有意义的数据和以前的知识网络标识符(自己的纪录)
参数:resE
a vector with named entries t1, t2 t1andt2, as produced by the function ResidualError, that contains the residual error associated with the discretisation of the data
项名为T1,T2 t1andt2ResidualError,包含残差数据离散化的功能,向量
Details
详情----------Details----------
Future versions of this function might directly write and compile a tex file.
此功能的未来版本可能会直接写和编译一个tex文件。
值----------Value----------
This function produces a directory and moves all the files of namesFiles to it, then it creates an html report that contains infos about the optimisation process.
该函数产生一个目录和它移动所有的namesFiles的文件,然后它创建一个HTML报告,其中包含关于优化过程中的infos。
作者(S)----------Author(s)----------
C.Terfve
参见----------See Also----------
writeNetwork, writeScaffold
writeNetwork,writeScaffold
举例----------Examples----------
tmpdir<-tempdir()
setwd(tmpdir)
#load data[数据加载]
data(CNOlistToy,package="CellNOptR")
data(ToyModel,package="CellNOptR")
#pre-process model (partial)[预过程模型(部分)]
indicesToy<-indexFinder(CNOlistToy,ToyModel,verbose=TRUE)
ToyNCNOcutComp<-compressModel(ToyModel,indicesToy)
indicesToyNCNOcutComp<-indexFinder(CNOlistToy,ToyNCNOcutComp)
ToyNCNOcutCompExp<-expandGates(ToyNCNOcutComp)
#optimise[优化]
ToyFields4Sim<-prep4Sim(ToyNCNOcutCompExp)
initBstring<-rep(1,length(ToyNCNOcutCompExp$reacID))
ToyT1opt<-gaBinaryT1(
CNOlist=CNOlistToy,
Model=ToyNCNOcutCompExp,
SimList=ToyFields4Sim,
indexList=indicesToyNCNOcutComp,
initBstring=initBstring,
maxGens=2,
PopSize=5,
verbose=TRUE)
#write report[写报告]
namesFilesToy<-list(
dataPlot=NA,
evolFit1=NA,
evolFit2=NA,
SimResults1=NA,
SimResults2=NA,
Scaffold=NA,
ScaffoldDot=NA,
tscaffold=NA,
wscaffold=NA,
PKN=NA,
PKNdot=NA,
wPKN=NA,
nPKN=NA)
writeReport(
ModelOriginal=ToyModel,
ModelOpt=ToyNCNOcutCompExp,
optimResT1=ToyT1opt,
optimResT2=NA,
CNOlist=CNOlistToy,
directory="testToy",
namesFiles=namesFilesToy,
namesData=list(CNOlist="Toy",Model="ToyModel"),
resE=NA)
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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