R语言 CellNOptR包 writeDot()函数中文帮助文档(中英文对照)

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writeDot(CellNOptR)
writeDot()所属R语言包：CellNOptR

                                         Write a model, and attached features, to a dot file
                                         写一个模型，并附加功能，到一个点文件

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This function writes a model to a Graphviz dot file with encoded features such as edge weight and nodes status (see details).
此功能写入模型到Graphviz的dot文件编码的功能，如边的权重和节点的状态（见详情）。


用法----------Usage----------


writeDot(dotNodes,dotMatrix,Model,fileName)



参数----------Arguments----------

参数：dotNodes
internal variables created by writeNetwork or writeScaffold; dotNodes is a matrix with 2 columns: the first has the node names,and the second the attributes (signal, stimulated, inhibited, compressed, nano). A node can appear twice in this matrix if it belongs to more of one of the above categories; a node could also not appear here if it is is none of these categories  
创建由writeNetwork或writeScaffold内部变量; dotNodes是一个2列的矩阵：第一节点名称，第二属性（信号，刺激，抑制，压缩，纳米）。节点可以在这个矩阵中出现两次，如果它属于上述类别之一;一个节点，也不会出现在这里，如果它是没有这些类别


参数：dotMatrix
internal variables created by writeNetwork or writeScaffold; dotMatrix is a matrix with 4 or 5 columns, and a row for each reaction:the first column holds the name of the input node, the second column holds the sign of the reaction (-1 if negative, 1 if positive), the third column holds the name of the output node, the fourth column holds the time stamp (0,1,2), an optional 5th column holds the weights of the edges  
内部变量创建由writeNetwork或writeScaffold dotMatrix是4或5列的矩阵，一排每个反应：第一列拥有输入节点的名称，第二列持有的反应的迹象（-1如果是负数，如果是正1），第三列持有输出节点的名称，第四列持有时间戳记（0,1,2），可选的第5列包含的边缘重量


参数：Model
A model to be plotted, if used inside writeNetwork then this should be the previous knowledge network (ModelOriginal), if inside writeScaffold then this should be the scaffold (ModelComprExpanded)  
一个模型来策划，如果里面writeNetwork的话，这应该是以前的知识网络（ModelOriginal），如果里面writeScaffold然后这应该是支架（ModelComprExpanded）


参数：fileName
a name for the file  
文件名


Details

详情----------Details----------

This function is not to be used on its own, it should be used internally to writeNetwork or writeScaffold. For the colouring of the nodes, nodes that are both stimulated and inhibited or any other combination, only one colour per category is used, and the following order of priority for the colours is used: signals prime over inhibited nodes which primes over stimulated nodes which primes over non-controllable/non-observable nodes, which primes over compressed.  Nodes that are neither of those have a black contour, stimulated nodes are green, inhibited are red, measure are blue, compressed and non-controllable/non-observable nodes are black and dashed.  Edges are coloured according to time stamp in the optimal model (green=t1, blue=t1 and/or t2, grey=neither); on the scaffold, the strokes of the edges reflects the weights in the models within reltol (i.e. for each edge, the weight is the frequency with which it appeared among the models within the relative tolerance boundaries around the best solution).
此功能不被单独使用，它应该被用来，内部writeNetwork或writeScaffold。黄金超过抑制为节点，节点是刺激和抑制或任何其他的组合，每个类别中，只有一个颜色，颜色按下列顺序优先用于着色：信号刺激节点，节点素数超过non-controllable/non-observable节点，通过压缩素数的素数。节点是那些既不是有一个黑色的轮廓，刺激的节点是绿色的，抑制的是红色的，措施是蓝色，压缩和non-controllable/non-observable的节点是黑色和破灭。根据优化模型中的时间戳记（边色绿= T1，蓝色= t1和/或T2，灰色=既不）;在断头台上，边招体现每个内RELTOL（即模型中的权重边缘，重量与它周围最好的解决方案相对宽容的界限）内的车型之间出现的频率。


值----------Value----------

This function does not have any output, it just writes a dot file in your working directory.
此功能不会有任何输出，它只是一个点文件写在你的工作目录。


作者（S）----------Author(s)----------



C. Terfve




参考文献----------References----------



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[预过程模型]

indicesToy<-indexFinder(CNOlistToy,ToyModel,verbose=TRUE)
ToyNCNOindices<-findNONC(ToyModel,indicesToy,verbose=TRUE)
ToyNCNOcut<-cutNONC(ToyModel,ToyNCNOindices)
indicesToyNCNOcut<-indexFinder(CNOlistToy,ToyNCNOcut)
ToyNCNOcutComp<-compressModel(ToyNCNOcut,indicesToyNCNOcut)
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 network[写网络]

writeNetwork(
        ModelOriginal=ToyModel,
        ModelComprExpanded=ToyNCNOcutCompExp,
        optimResT1=ToyT1opt,
        optimResT2=NA,
        CNOlist=CNOlistToy)

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


注：
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