File:Simmelian-backbone-config.png

2013-04-18T22:26:39Z

Nick:

.dl

2011-08-17T11:47:42Z

Nick: Created page with "Descriptions of the UCINET file format (.dl) can be found on the web, e.g., [http://www.analytictech.com/networks/dataentry.htm here]."

Descriptions of the UCINET file format (.dl) can be found on the web, e.g., [http://www.analytictech.com/networks/dataentry.htm here].

Supported network formats

2011-08-17T11:41:59Z

Nick:

[[.graphml|GraphML files (.graphml)]]

[[adjacency matrix|adjacency matrix files (.txt or .csv)]]

[[link list|link list files (.txt or .csv)]]

[[.dl|UCINET .dl files (.dl)]]

Pajek graph files (.net)

edge list files (.el)

Siena files (.dat)

[[RSiena session file|RSiena session files]] (.txt or .csv or .dat or .prn)

Installation (tutorial)

2011-04-12T09:34:28Z

Nick: heap size

This [[Trails|trail]] explains how to download, install, and run visone and its various extensions.
To use the core visone program you'll need only to read the first section ''Installing visone'' and, potentially, the second section on the installation of the java runtime environment. The other sections explain how to install and run several extension packages that can be used in connection with visone.

== Installing visone ==

Installing visone is very simple. You can start it either from you local hard disk or use visone webstart. To download visone and start it from your '''local hard disk''' do the following two steps:

# Go to the visone download page [http://visone.info/doku.php?id=download:index] and save the latest current version (e.g., visone.2.6.3.jar) on your hard disk, for instance, on your desktop.
# Execute the downloaded file (for instance, by double-clicking on it). The visone window opens and visone is ready for use!
(For larger projects, you might want to allocate more memory for the JVM [http://de.wikipedia.org/wiki/Java_Virtual_Machine] by executing the downloaded file from a command prompt: "java -Xmx256m -jar visone-2.6.3.jar" ...256m increases the default java heap size (128 MB) to 256 MB.)

Even simpler is starting the program via '''visone webstart'''. To do this go to the visone Website [http://visone.info] and click on the link ''webstart visone'' in the green download box on the left. Tell your browser to execute the file ''visone.jnlp''.
(note: visone webstart uses a maximum heap size of 256 MB by default)

== Troubleshooting (I): installing the java runtime environment (JRE) ==

If the visone window does not open and if you get a message similar to "cannot open file: visone-2.6.3.jar", then it is most likely that the Java Runtime Environment (JRE) is not installed on your computer. In this case do the following steps.

# Go to the Oracle Webpage [http://www.oracle.com/technetwork/java/javase/downloads/index.html].
# Search in the list for Java SE Runtime Environment (JRE) (version 6 or higher) and click on download. Follow the instructions.
# Once you have installed the JRE, do as described in the previous section.

== Installing the R connection ==

=== Rserve installation===
We assume that you have an [http://www.r-project.org R] installation running (install the latest version fitting to your system from the link).
For a connection between visone and R we additionally need the RServe packages.
You can install this package directly from R:
* start an R console (the R GUI under windows, or type "R" on a terminal in Linux/MacOS)
* in the R console run the command: <code>install.packages("Rserve", dependencies=T)</code>
* make sure the installation completed successfully
* you can close R (i.e. by running the command <code>q()</code>, no need to save anything)
Now, your R should be ready for visone.

The following subsections explain how to set the R connection properties in the [[option dialog]] (menu '''file, options''').

===basic connection properties===
Here you tell visone where your R installation resides.
On some systems this is not problematic, sometimes you have to give the exact place where R is installed.
If you try this on a Linux or MacOS, try not to touch anything.
If this does not work for you, configure the following options:

* "use R in system path" - use this option if you have a regular R installation on a Linux or MacOS and the binary is in the path
* "path to R/Rserve dir" - if the first option does not work for you (or you want to start a specific R installation) give the path to the executable here (the directory where the executable "RServe.exe" (Windows) or "R" (all other) file is located)
* "use Rserve" - if you are on a windows system, R needs to be started using a specific command, in this case check this box and give the path to the "Rserve.exe" to the option above
Sometimes it is necessary to copy the file "Rserve.exe" from the library section (i.e. <code>C:\Users\Peter\Documents\R</code>) to the bin section (i.e. <code>C:\Program Files\R\R-2.12.2\bin\i386</code>) of your R installation.

===library configuration===
In some cases R has a problem to find its libraries when started from visone. If you experience problems with library installation, i.e. visone connects, but cannot install
libraries or send graphs to R, you can configure the location of your libraries here.
This is also an option to have a unique place where all the visone installed R libraries will reside.
The option is
* "library path" - paste the path were your libraries are installed or should be installed. Use "NULL" for default (system) path.

===start your own R===
If you're planning to start R on a remote machine or the other options do not work for you, you can run your own instance of R and have visone connect to it:
* "don't start R, but use" - check this if you would like visone to use an R instance started by you
* "server or ip" - give the name of the server or its ip adress
* "port" - the port visone should connect to
In case you have trouble connecting, you can always start your own R-session.
To do this, start the usual R console, make sure your completed the RServe installation (see above) and run:

<code>
library(Rserve)
</code><br>
<code>
Rserve()
</code>

Starting visone on a computer prepared in this way should give you a proper connection to R.

Learn more about R - especially RSiena and some more on the connection options in a
[http://visone.info/lib/exe/fetch.php?media=docs:visonesiena_man.pdf short guide on how to use Rsiena from within visone].

== KNIME installation ==

Get a copy of Knime from [http://www.knime.org http://www.knime.org] and install it.

Now we need to install the "bison-plugin", go to "Help->Install new software"
add "http://tech.knime.org/update/bison" as update site and install the BisoNet plugin, especially "visone Integration" is needed.

After this installation you should find a number of new nodes in the "BisoNet"-category which can be used to create, manipulate or analyze networks.
In the subcategory "visone" you find the "Input Connector"-Node which is used to receive networks from visone and the "Output-Connector"-Node, used to send networks
to visone.

For a test, keep your Knime running, add an "Input Connector"-Node and start visone. See further details in the page [[Knime menu]].

R console (tutorial)

2011-04-06T15:29:58Z

Nick: /* Plotting to a PDF file */

This [[Trails|trail]] illustrates how to send network data from visone to R and back. [http://www.r-project.org/ The R project] for statistical computing offers a rich set of methods for data analysis and modeling which becomes accessible from visone through the R console. We assume that you have installed the R connection as it is explained in the [[Installation_(trail)#Installing_the_R_connection|installation trail]]. This trail assumes that you have basic understanding about how to work with visone as it is, for instance, explained in the trail on [[Visualization_and_analysis_(trail)|visualization and analysis]]. You do ''not'' need to have any previous knowledge about R to follow this trail; nevertheless, to exploit the full potential offered by R you could consult documentation and tutorials linked from the [http://www.r-project.org/ R-project page].

To follow the steps illustrated in this trail you should download the network file ''Egonet.graphml'' which is linked from and explained in the page [[Egoredes_(data)]]. Further you should remove all ties that are not rated as ''very likely'' in the same manner as it is explained in the last section of the [[Visualization_and_analysis_(trail)#Selecting_elements_dependent_on_attribute_values|visualization and analysis trail]].

==Sending networks from visone to R==

To send the network from visone to R open the [[R console tab]], choose a name in the textfield '''r name''' (you might just accept visone's suggestion for this name, which should be ''egonet''), and click on the '''send''' button. After clicking on '''send''' visone starts the Rserve connection and opens the R-console. If this does not work you should check the settings of the [[Option_dialog#R_-_connection|R connection options]] accessible via the '''file, options''' menu. If it works you should get a message like
ready to serve
visone: sendActiveNet egonet
done
visone: lsIGraph
"egonet"
in the message field of the R console.

[[File:R_console_ls.png]]

You can list all variables that are in the R workspace by typing
ls()
in the input field at the bottom of the R console and pressing the Enter-Key (currently there is one object called ''egonet'').
As we'll see soon, ''egonet'' is an object of class ''igraph'' which is an R package obtainable from a [http://cran.r-project.org/ CRAN Website] (this site also gives you access to R tutorials and documentation). The igraph package is documented in more detail on [http://igraph.sourceforge.net/ http://igraph.sourceforge.net/].

==Getting basic statistics about an igraph object==

The class of the object ''egonet'' in the R workshop is printed when executing the command
class(egonet)
As we can see the class of ''egonet'' is ''igraph''. The igraph documentation linked above gives a complete list of all methods available for this class. In the following we describe how to inspect what is encoded in the given object and how to get simple summary statistics.
Executing the command
summary(egonet)
outputs basic information such as the number of vertices and edges, the names of vertex and edge attributes, as well as a list of all edges.

[[File:R_console_summary.png]]

=== Exploring attributes ===

The names of all vertex or edge attributes are printed with
list.vertex.attributes(egonet)
list.edge.attributes(egonet)
To see the values of the vertex or edge attributes one needs to understand the concept of ''vertex iterators'' and ''edge iterators'' in igraph. The vertex iterator of graph ''egonet'' is returned by typing the command
V(egonet)
When executing this you see just the list of vertex ''name''s which are here the numbers from 1 to 45. To obtain the values of an attribute (e.g., ''Afrm'') type
V(egonet)$Afrm
which returns the vector of countries of origin of the various actors.

[[File:R_console_afrm.png]]

Useful summary statistics include information about how many actors originate from the various countries. However, typing the
command
summary(V(egonet)$Afrm)
just outputs information about the class and size of the list of countries of origin:
Length Class Mode
45 character character
which is not very informative. To obtain the list of unique values for the ''Afrm'' attribute, you can type
unique(V(egonet)$Afrm)
which returns the names of five different countries. To count the number of actors in each of the countries, it is most convenient to convert the vector of character strings into a ''factor'' and save this factor in a new variable (e.g., called ''from'') by typing the command
from <- as.factor(V(egonet)$Afrm)
Finally the command
summary(from)
returns the list of unique values along with the number of actors in each of the classes. In our example this is
Colombia Dominican Republic Puerto Rico Spain United States
2 14 5 1 23

=== Indexing of vertex and edge iterators ===

[[File:R_console_iterator.png|400px|thumb|right]] Vertex iterators can be restricted to subsets by specifying a logical vector (or a command that produces one) in square brackets after the iterator. For instance, the command
V(egonet)[2:5]
returns the values 3 to 6. This seeming contradiction is explained by the fact that counting of indices in vertex or edge iterators starts at zero; thus, the name of the vertex at position 0 is 1, the name of the vertex at position 2 is 3, and so on.
The result of such a restriction operation on an vertex iterator is itself a vertex iterator and, thus, provides access to vertex attributes. For instance, typing
V(egonet)[2:5]$Afrm
returns the countries of origin of actors indexed by 2 to 5 (i.e., named 3 to 6). The command
V(egonet)[Acit == "new york"]$Afrm
gives you the countries of origin of all actors whose attribute ''Acit'' (encoding the city of residence) equals ''new york'', and so on.

An edge iterator is returned via the command
E(egonet)
and offers access to edge attributes similar as for vertices.

Vertex iterators and edge iterators can be indexed by more complex conditions. Actually, any logical vector whose length equals the number of vertices (respectively edges) can be used as an argument in the square brackets following vertex iterators (respectively edge iterators). The following lines illustrate such indexing tasks. To select all actors whose origin is in the US and save this iterator in a variable ''actors.from.usa'' type
actors.from.usa <- V(egonet)[Afrm == "United States"]
All edges connecting two actors from the US are obtained by
edges.within.usa <- E(egonet)[actors.from.usa %--% actors.from.usa]
The command ''%--%'' is a special command used in edge iterators between two vertex iterators; it selects all edges connecting vertices from the two specified subsets (which might be identical, as in the example above).
Edges with at least one actor from the US are selected by
edges.incident.usa <- E(egonet)[adj(actors.from.usa)]

To select all edges within any of the classes defined by Afrm we first construct a logical vector for edges that is true if and only if the ''Afrm'' attribute of the two connected vertices is identical and then use it as an argument in ''E(egonet)[...]''. Therefore type
el <- get.edgelist(egonet) +1
within.class.edges <- V(egonet)[el[,1]]$Afrm == V(egonet)[el[,2]]$Afrm
E(egonet)[within.class.edges]
The variable ''el'' is just a matrix with two columns containing the vertex ids of adjacent vertices. The ''+1'' in the first line is necessary because ids start with zero.

Vertex and edge iterators can be restricted in various other ways; see the [http://igraph.sourceforge.net/ igraph documentation for details].

== Analyzing distributions of centralities in igraph ==

The igraph package offers methods to compute various established centrality measures (refer to the [http://igraph.sourceforge.net/ igraph documentation] for a complete list of available methods). While many of these could also be directly computed in visone without the detour via the R console, R directly offers statistical descriptions and analysis of the computed values. This is demonstrated in the following. The vertex degrees are returned by the command
degree(egonet)
Let's save this vector in a variable ''d'' by typing
d <- degree(egonet)
Mean, standard deviation, and summary statistics (including min, max, quartiles, and median) are computed by
mean(d)
sd(d)
summary(d)

To display these (or other) statistics separately for each class of actors defined by the country of origin (or any other attribute), execute the command
tapply(d, from, summary)
The three arguments of ''tapply'' have the following meaning: ''d'' is the vector of values to which the function should be applied, ''from'' is the factor whose unique values determine the different classes, and ''summary'' is the function to be computed (instead of ''summary'', you could also type ''mean'', ''sd'', and so on).

[[File:R_console_tapply.png]]

== Loading networks from R into visone ==

[[File:R_console_load.png|200px|thumb|right]] As networks can be sent from visone to R, you can also load objects of class ''igraph'' into visone. Loading the current R object ''egonet'' would be of no use since this network is already in visone and has not been modified. Loading networks from R into visone is useful when some values that have been computed in R and attached to the igraph object should be accessible as vertex or edge attributes in visone. This offers numerous possibilities to transform attributes, as it will be demonstrated in the following.

First let's copy ''egonet'' into a new igraph object named ''g'' by executing
g <- egonet
(This rather serves to demonstrate how new variables for igraph objects can be loaded into visone; we could also have attached the new data directly to ''egonet''.) To attach a new attribute named ''Degree'' that encodes the previously computed node degrees type
V(g)$Degree <- d
Degrees could have been computed in visone as well. However, R offers methods to transform such values that are not implemented directly in visone. For instance, the variable ''DegreeCentered'' computed and attached via
V(g)$DegreeCentered <- d-mean(d)
encodes the differences between the individual degrees and the mean (so that nodes with relatively small degrees get negative values and nodes with relatively high degrees get positive values). Likewise
V(g)$LogDegree <- log(d)
attaches the logarithmized degrees to the graph (this transformation is quite useful in networks with skewed degree distributions, e.g., [[Random_networks_generation#preferential|preferential attachment]] graphs). Note that while visone directly offers some possibilities to [[Managing_attributes_(trail)#Transforming_attributes|transform attributes]], logarithmic transformations are not implemented; in contrast, R as a programming language, imposes no such restrictions.

Finally, loading the network along with all old and new attributes into visone can be done via the [[R console tab]] as explained in the following. Click once on the '''refresh''' button to show all R variables of class igraph (note that this shows the variable ''g'' but not the vector of degrees ''d''). Selecting ''g'' and pushing the '''load''' button opens a new network tab with a network called ''g''. You can inspect the newly attached attributes via the [[attribute manager]].

== File import and export in R ==

=== Saving and loading R objects ===

Variables in the R workspace can be saved to and read from the disk. If you just want to save the igraph objects (the network together with all attributes) you could as well load them into visone and save them as [[GraphML]]. However, your R workspace might contain other variables that are not of class ''igraph''. To save objects it is convenient to set the working directory. The path to the current working directory can be obtained by
getwd()
which currently outputs in many cases the directory where the ''Rserve.exe'' file is located (see the [[Option_dialog#R_-_connection| R connection settings]]). To set it to a different directory type
setwd("<path_to_directory>")
(Chose a directory to which you have write-access.) Saving an R object (for instance, the network ''egonet'') can be done by
save(egonet, file="egonet.rda")
This creates a file ''egonet.rda'' in the specified working directory. (Here, ''egonet.rda'' is an arbitray filename that you can chose as you wish. ) All objects in the workspace can be saved by
save.image(file="myWorkspace.rda")
Conversely, to read objects from a file in the current working directory type
load(file="egonet.rda")
(This makes sense if you have closed the R connection and you want to recover the object ''egonet''.) R objects that have been saved to disk can, of course, also been imported into any other R environment (see [http://www.r-project.org/ http://www.r-project.org/] for more information).

=== Plotting to a PDF file ===

To plot a diagram, such as the degrees of a network, to a PDF file type
pdf("myDiagram.pdf")
plot(d, type="b")
dev.off()
This writes a PDF file ''myDiagram.pdf'' to the current working directory; this file can be viewed, e.g., with Adobe's Acrobat Reader, printed, or used as a figure in some other document. The R plot command is a very general and powerful tool to create statistical graphics; for more information see the documentation linked from the [http://www.r-project.org/ R Project Website].

=== Executing R code from a file ===

Especially if you are working on a larger project it is convenient not just to save the data but also the R code to recompute some of the results or to modify some analyses. R code can be executed from text-files by the ''source'' command. For instance,
source("myRCode.R")
executes all commands from the file ''myRCode.R'' located in the current working directory. Note that this file must be a '''plain text file''' (see [http://en.wikipedia.org/wiki/Text_editor http://en.wikipedia.org/wiki/Text_editor] for explanation) containing commands like the examples provided in typewriter font in the boxes on this page. Conversely,
sink("myROutput.txt")
directs the R output to a file ''myROutput.txt'' in the current working directory. This is convenient when executing R commands that produce complex output.

== Fitting exponential random graph models (ERGMs) ==

Since networks can be sent from visone to R you get the possibility to use many powerful contributed R packages for network analysis and modeling. In this section we illustrate the use of some methods provided by the '''statnet''' package written by Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris; see the [http://statnetproject.org/ statnet project Website] for additional information, including documentation and tutorials. In particular, we are going to demonstrate how to fit an exponential random graph model (ERGM) to a network sent from visone to R. ERGMs are sophisticated statistical network models that can deal with complex dependencies among observations, including homophily, reciprocity, preferential attachment, and triangular closure. Note that ERGMs are mainly applied to time-independent networks; an option for modeling network dynamics is the [[RSiena|'''RSiena''']] package that can be used from within visone as it is illustrated in the [[RSiena_(trail)| RSiena trail]].

=== Installing new packages ===

To estimate an ERGM we need to install the R packages '''network''', '''sna''', and '''ergm''', all of which are part of the statnet package. To install these packages separately type (after appropriate replacement of <code><path to R library dir></code> and <code><url of R mirror site></code>)
install.packages("network", lib = "<path to R library dir>", repos = "<url of R mirror site>")
library(network, lib.loc = "<path to R library dir>")

install.packages("sna", lib = "<path to R library dir>", repos = "<url of R mirror site>")
library(sna, lib.loc = "<path to R library dir>")

install.packages("ergm", lib = "<path to R library dir>", repos = "<url of R mirror site>")
library(ergm, lib.loc = "<path to R library dir>")

For the R library you can set the same path as in the [[Option_dialog#R_-_connection| R connection settings]]; a list of mirror sites can be found at [http://cran.r-project.org/mirrors.html http://cran.r-project.org/mirrors.html] (copy the URL of any mirror site, including the http:// prefix, and use it in quotes as a value for the '''repos''' argument in the commands above).

Since <code>igraph</code> and <code>network</code> are both packages for networks, they define some methods with identical names. This can lead to problems when a method from one package is executed on an object from the other. A workaround for such problems would be to save the objects that you want to work with, close the R connection, open it again, install only the package that you need, and reload the data objects.

=== Converting an igraph to a network object ===

The <code>ergm</code> package works on network objects from the package <code>network</code>. Thus, the current <code>igraph</code> object first has to be converted into this class. This can be done in two steps: getting the adjacency matrix for the ''egonet'' object by
adj <- get.adjacency(egonet)
and then creating a network from this adjacency matrix by executing the following line (note that the <code>directed</code> attribute must be set to <code>FALSE</code>, otherwise the network will be directed)
net <- as.network(adj, directed=FALSE)
Executing
summary(net)
shows that the ties have been correctly converted from the <code>igraph</code> package to <code>network</code> but, so far, no vertex or edge attributes are attached to the <code>net</code> object. This can be done by, e.g.,
set.vertex.attribute(net,"From", V(egonet)$Afrm)
set.vertex.attribute(net,"City", V(egonet)$Acit)

Conversely, to create an igraph from an adjacency matrix use the command <code>graph.adjacency(adj, mode="undirected")</code> (we don't need this at the moment).

=== Computing observed statistics ===

The '''ergm''' package enables the computation of the observed values of various network statistics, such as number of triangles, number of k-stars, and many more. To get these values call the <code>summary</code> method on an ERGM formula object. For instance,
summary(net ~ triangle + kstar(2) + nodematch("City"))
returns the number of triangles in the network, the number of 2-stars, and the number of edges connecting actors that live in the same city. In our example, this is
triangle kstar2 nodematch.City
543 1857 143
To see other statistics that can be used in an ERGM formula see the section on <code>ergm.terms</code> in the statnet documentation linked from [http://statnetproject.org http://statnetproject.org].

=== Estimating ERGMs ===

Exponential random graph models (ERGMs) assign a network <math>G</math> a probability of the form

<math>P(G)=\frac{1}{\kappa(\theta)}\exp\left(\sum_{i=1}^k\theta_i\cdot s_i(G)\right)</math>, where
* the <math>s_i</math> are functions mapping from the set of networks (the ''population'' of the random graph model) to the real numbers; the <math>s_i</math> are called '''(network) statistics''' and are typically chosen by the researcher based on theory
* <math>\theta=(\theta_1,\dots,\theta_k)</math> is a vector of free '''parameters''' associated with statistics; the parameters are typically estimated via maximum likelihood estimation (MLE) given an observed network
* <math>\kappa(\theta)</math> is a normalization constant for the random graph model

The statistics are typically (functions of) counts of small subgraphs such as edges, stars, triangles, or edges connecting actors that have specific attribute values. The interpretation of the estimated parameters is as follwing. If, for instance, the parameter associated with the triangle count statistic is (significantly) positive, then networks with more triangles have higher probability - assuming that all other statistics remain constant. This would demonstrate a tendency for transitive closure as described in the saying ''the friend of a friend is a friend''. The specification, estimation, and interpretation of ERGMs is quite involved and cannot be sufficiently treated in this page; for more information we refer to the statnet tutorial linked from [http://statnetproject.org http://statnetproject.org]. In the following, we illustrate merely ''how'' ERGMs can be specified and estimated in statnet.

Computing maximum likelihood estimates for ERGM parameters can be done by calling the <code>ergm</code> on an ERGM formula. For instance,
model <- ergm(net ~ edges + nodematch("From") + nodematch("City") + gwesp(0.1, fixed=TRUE))
estimates the parameters of a model with four statistics: number of edges, numbers of edges connecting actors with the same country of origin, respectively the same city of residence, and the so-called ''geometrically weighted edgewise shared partners'' statistic. The latter has a similar interpretation as the triangle statistic but is less likely to lead to degenerate models; see the statnet documentation for details. Calling
summary(model)
prints basic information about the model, including estimated parameters and standard errors. In our example, we get (note that the results might change from call to call, since the estimation algorithm itself is probabilistic)
==========================
Summary of model fit
==========================
Formula: net ~ edges + nodematch("From") + nodematch("City") + gwesp(0.1, fixed = TRUE)
Newton-Raphson iterations: 6
MCMC sample of size 10000
Monte Carlo MLE Results:
Estimate Std. Error MCMC s.e. p-value
edges -4.1438 3.8443 0.193 0.281
nodematch.From 0.8223 0.1459 0.006 <1e-04 ***
nodematch.City 0.8212 0.1553 0.002 <1e-04 ***
gwesp.fixed.0.1 3.0107 3.4763 0.175 0.387
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Null Deviance: 1372.43 on 990 degrees of freedom
Residual Deviance: 484.89 on 986 degrees of freedom
Deviance: 887.54 on 4 degrees of freedom
AIC: 492.89 BIC: 512.48
From these results we can conclude that ties are more likely between actors that stem from the same countries of origin (significantly positive estimate for the parameter associated with the <code>nodematch.From</code> statistic) and ties are more likely between actors that live in the same city (<code>nodematch.City</code>). We could not find evidence for transitive closure - controlling for country of origin and city of residence - since the parameter associated with <code>gwesp.fixed.0.1</code> is not significant.

== References ==

Gábor Csárdi and Tamás Nepusz. The '''igraph''' library. [http://igraph.sourceforge.net/ http://igraph.sourceforge.net/].

Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, and Martina Morris (2003). '''statnet''': Software tools for the Statistical Modeling of Network Data. URL [http://statnetproject.org http://statnetproject.org].

The R project for statistical computing [http://www.r-project.org/ http://www.r-project.org/]

File:Zoom 100.png

2011-03-28T12:13:40Z

Nick: uploaded a new version of "File:Zoom 100.png"

visone icon zoom 100

File:Zoom 100.png

2011-03-28T12:12:26Z

Nick: uploaded a new version of "File:Zoom 100.png"

visone icon zoom 100

File:Knime on.gif

2011-03-28T12:09:52Z

Nick:

2010-12-16T16:59:30Z

Nick:

The R console tab enables to send network data from visone to R (using the igraph package) and vice versa.
Refer to [[option_dialog#R_-_connection|visone's option dialog]] to get more information on how to set up the visone R connection.

== tab ==

* '''r name''': defines the name of the igraph network variable in R that the visone network is assigned to (by default this is the name that was assigned to the network in visone...)
* '''send active net''': '''send''' the network from the active visone tab to R, creating an igraph network variable with the speficied r name (this will open the console, if it has not been shown yet)
* '''show r varibles''': '''refresh''' the overview of network objects that are available in R (shown below the refresh button)
* '''load selected net''': '''load''' the network object that is selected in the overview from R to a new visone tab (the name of the network will be the name of the R object; the asterisk reminds you, that it has not been saved yet); alternatively you can double-click on the according network object in the overview

* show console: this button is used to (re)show the console without sending a network

== console ==

There are three tabs displaying different messsages regarding the R connection:
* '''visone''': all messages that originate from visone
* '''user''': all messages that originate from user input in the console
* '''all''': both, user and visone messages

At the bottom of the console you find the
* '''input''' field that can be used to invoke any R commands
* '''close''' button that closes the R console window (but not the connection to R)

R console tab

2010-12-16T16:57:51Z

Nick:

The R console tab enables to send network data from visone to R (using the igraph package) and vice versa.
Refer to [[option_dialog#R_-_connection|visone's option dialog]] to get more information on how to set up the visone R connection.

== tab ==

* '''r name''': defines the name of the igraph network variable in R that the visone network is assigned to (by default this is the name that was assigned to the network in visone...)
* '''send active net''': '''send''' the network from the active visone tab to R, creating an igraph network variable with the speficied r name (this will open the console, if it has not been shown yet)
* '''show r varibles''': '''refresh''' the overview of network objects that are available in R (shown below the refresh button)
* '''load selected net''': '''load''' the network object that is selected in the overview from R to a new visone tab (the name of the network will be the name of the R object; the asterisk reminds you, that it has not been saved yet); alternatively you can double-click on the according network object in the overview

* show console: this button is used to (re)show the console without sending a network

== console ==

There are three tabs displaying different messsages regarding the R connection:
* '''visone''': all messages that originate from visone
* '''user''': all messages that originate from user input in the console
* '''all''': both, user and visone messages

At the bottom of the console you find:
* '''input''' field: can be used to invoke any R commands
* '''close''' button: closes the R console (but not the connection to R)