plot.nem {nem} | R Documentation |
plot graph of nested effects model, the marginal likelihood distribution or the posterior position of the effected genes
## S3 method for class 'nem': plot(x, what="graph", remove.singletons=FALSE, PDF=FALSE, filename="nemplot.pdf", thresh=0, transitiveReduction=FALSE, plot.probs=FALSE, SCC=TRUE, D=NULL, draw.lines=FALSE, ...)
x |
nem object to plot |
what |
(i), "graph", (ii) "mLL" = likelihood distribution, (iii) "pos" = posterior position of effected genes |
remove.singletons |
remove unconnected nodes from the graph plot |
PDF |
output as PDF-file |
filename |
filename of PDF-file |
thresh |
if x has a real valued adjacency matrix (weight matrix), don't plot edges with |weight| <= thresh |
transitiveReduction |
plot a transitively reduced graph |
plot.probs |
plot edge weights/probabilities. If regulation directions have been inferred (see infer.edge.type ), upregulated edges are drawn red and downregulated edges blue. Edges, were no clear direction could be inferred, are drawn in black. |
SCC |
plot the strongly connected components graph |
D |
Visualize the nested subset structure of the dataset via plotEffects along with the graph and show the linking of E-genes to S-genes in the dataset. Should only be used for small networks. Default: Just plot the graph |
draw.lines |
If the nested subset structure is shown, should additionally lines connecting S-genes and their associated E-genes be drawn? WARNING: For larger datasets than e.g. 5 S-genes this most probably does not work, because the nested subset structure picture then partially overlaps with the graph picture. Default: Do not draw these lines |
... |
other arguments to be passed to the Rgraphviz plot function or to the graphics 'image' function. |
none
Florian Markowetz <URL: http://genomics.princeton.edu/~florian>, Holger Froehlich <URL: http://www.dkfz.de/mga2/people/froehlich>
nem
, plotEffects
, infer.edge.type