plot nested effect model

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Description

plot graph of nested effects model, the marginal likelihood distribution or the posterior position of the effected genes

Usage

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	## 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, palette="BlueRed",...)

Arguments

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

palette

color palette to use: either 'BlueRed' (default) or 'Grey'

...

other arguments to be passed to the Rgraphviz plot function or to the graphics 'image' function.

Value

none

Author(s)

Florian Markowetz, Holger Froehlich

See Also

nem, plotEffects, infer.edge.type

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