| Title: | Qualitative Network Analysis |
|---|---|
| Description: | Qualitiative analysis of press perturbations of network models. Given a network model represented as a signed directed graphs, this package provide facilities for evaluating the impact of a press perturbation to the system through simulation. |
| Authors: | Simon Wotherspoon [aut], Jessica Melbourne-Thomas [aut], Ben Raymond [aut, cre] |
| Maintainer: | Ben Raymond <[email protected]> |
| License: | GPL-2 |
| Version: | 0.23 |
| Built: | 2024-09-09 04:56:49 UTC |
| Source: | https://github.com/swotherspoon/QPress |
Qualitiative analysis of press perturbations of network models. Given a network model represented as a signed directed graphs, this package provide facilities for evaluating the impact of a press perturbation to the system through simulation.
This package provides facilities for simulating press perturbation scenarios for qualitative network models specified as signed directed graphs (signed digraphs).
Ben Raymond, Jessica Melbourne-Thomas, Simon Wotherspoon
B. Raymond, J. Melbourne-Thomas and S. Wotherspoon
Display adjacency matrix of the directed graph as an image
adjacency.image(edges, required.groups = c(0), cex.axis = 1)adjacency.image(edges, required.groups = c(0), cex.axis = 1)
edges |
an edge list |
required.groups |
which edge groups should be included? |
cex.axis |
character expansion factor for the edge labels |
Display the matrix constructed by adjacency.matrix as an image.
Returns the adjacency matrix for the directed graph.
edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D -> B")) edges <- enforce.limitation(edges) adjacency.image(edges)edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D -> B")) edges <- enforce.limitation(edges) adjacency.image(edges)
Adjacency matrix of the directed graph.
adjacency.matrix(edges, labels = FALSE, required.groups = c(0))adjacency.matrix(edges, labels = FALSE, required.groups = c(0))
edges |
an edge list |
labels |
add row and column labels |
required.groups |
which edge groups should be included? |
This function converts an edge list to an adjacency matrix A,
following the convention that A[i,j] represents the impact of node
j on node i.
Returns the adjacency matrix for the directed graph.
edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D -> B")) edges <- enforce.limitation(edges) adjacency.matrix(edges,labels=TRUE)edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D -> B")) edges <- enforce.limitation(edges) adjacency.matrix(edges,labels=TRUE)
Adjoint matrix and Characteristic Polynomial
adjoint(A) charpoly(A)adjoint(A) charpoly(A)
A |
a square matrix |
These functions compute the adjoint matrix and characteristic polynomial of A by the Fedeew-Leverrier algorithm.
If A has integer elements and the computations are performed with integer arithmetic the result is exact.
adjoint returns the adjoint matrix of A
charpoly returns the coefficients of the characteristic
polynomial of A as a vector.
edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D -> B")) edges <- enforce.limitation(edges) A <- adjacency.matrix(edges,labels=TRUE) adjoint(A)edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D -> B")) edges <- enforce.limitation(edges) A <- adjacency.matrix(edges,labels=TRUE) adjoint(A)
Construct a checkbox widget
checkbox(parent, label, initial = 0) checkcolumn(parent, label, rows, label.rows = TRUE)checkbox(parent, label, initial = 0) checkcolumn(parent, label, rows, label.rows = TRUE)
parent |
the parent window. |
label |
the label for the enclosing frame. |
initial |
the initial state of the checkbox |
rows |
the row labels |
label.rows |
whether to label rows. |
The checkbox function makes a single checkbox widget, while
checkcolumn makes a widget containing a column of
checkboxes.
Returns an object of class checkbox or checkcolumn with elements
window |
the widget |
selected |
function that returns the state of the checkboxes |
state |
the tclVars representing the state of the checkboxes |
Construct an edge selection widget
checkedges(parent, label, rows, edges, group = NULL, label.rows = TRUE)checkedges(parent, label, rows, edges, group = NULL, label.rows = TRUE)
parent |
the parent window |
label |
the label for the enclosing frame |
rows |
the labels for the rows (node names) |
edges |
an nx2 matrix that defines the edges |
group |
a numeric vector that groups edges |
label.rows |
whether to label rows |
Makes a widget consisting of a grid of check buttons that allow the user to select edges of the network.
Returns an object of class checkedges with elements
window |
the widget |
selected |
function that returns the state of the check buttons |
state |
the tclVars representing the state of the check buttons |
Construct functions to generate random community matrices
community.sampler(edges, required.groups = c(0))community.sampler(edges, required.groups = c(0))
edges |
an edge list |
required.groups |
a vector of integers specifying which groups of edges must always occur in the community matrix. |
Given an edge list that specifies a directed graph, this function constructs a list of functions that can be use to generate random community matrices corresponding to that directed graph.
Edges in the edge list that do not fall in a required group are considered uncertain, and may or may not be represented in the community matrix.
Random community matrices are generated in two stages, the first stage
determines which of the uncertain edges will be included or excluded in
subsequent simulations, while the second stage generates random matrices
corresponding to the selected. The select function is a function of
a single argument p that determines which of the uncertain edge pairs
will be included in matrices generated by subsequent calls to
community. This function always selects either neither or both edges
of a pair and every uncertain pair has likelihood p of being
selected. The community function is a function of no arguments that
generates a random community matrix. The weights function is a
function of a single argument W that returns those entries of the
community matrix W that correspond to edges in the edge list.
Returns a list with elements
community() |
a function to generate a random community matrix |
select(p) |
a function that randomly selects which uncertain edges will be retained |
weights(W) |
a function that returns the (non-zero) weights as a vector |
edge.labels |
the labels of the edges |
uncertain.labels |
the labels of the uncertain edges |
set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) s <- community.sampler(edges) ## Force D --> B edge out s$select(0) ## Generate community matrices s$community() s$community() ## Force D --> B edge in s$select(1) ## Generate community matrices s$community() s$community() ## Select the uncertain D --> B edge with prob 0.6 s$select(0.6) ## Generate community matrices s$community() s$community()set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) s <- community.sampler(edges) ## Force D --> B edge out s$select(0) ## Generate community matrices s$community() s$community() ## Force D --> B edge in s$select(1) ## Generate community matrices s$community() s$community() ## Select the uncertain D --> B edge with prob 0.6 s$select(0.6) ## Generate community matrices s$community() s$community()
This is an experimental function! Given a set of system simulation outputs
(from system.simulate), it will drop one or more nodes and their
associated edges, but leave all other elements of the system untouched. Each
set of edge weights in sim is checked for stability after dropping
the specified nodes, and any matrices representing unstable systems are
removed from the returned set.
drop.nodes(sim, to.drop, method = "remove")drop.nodes(sim, to.drop, method = "remove")
sim |
the result from |
to.drop |
the names of the nodes to drop (check
|
method |
either "remove" (the specified nodes will be fully removed from the system) or "zeros" (the specified nodes will be left in the system but all edges from or to these nodes (other than self-interactions) are set to zero). |
As for system.simulate
Enforce self limitation
enforce.limitation(edges)enforce.limitation(edges)
edges |
an edge list |
For stability, the majority of nodes of the directed graph should have a self limiting edge. This function adds a self limiting edge for every node to an existing edge list.
Returns an edge list augmented with self limiting edges.
Write a DOT specification of the model.
grviz.digraph( edges, name = "web", fontsize = 10, node.style = "filled", node.shape = "oval", node.color = "DarkOrange", edge.color = "DarkGrey" )grviz.digraph( edges, name = "web", fontsize = 10, node.style = "filled", node.shape = "oval", node.color = "DarkOrange", edge.color = "DarkGrey" )
edges |
An edge list |
name |
The name of the digraph |
fontsize |
Fontsize for node labels. |
node.style |
The node style. |
node.shape |
The node shape. |
node.color |
The node color. |
edge.color |
The edge color. |
Write a DOT specification of the model in a form suitable for use with
grViz from DiagrammeR.
Returns a string.
Display the impact of a perturbation as a barplot
impact.barplot(sim, epsilon = 1e-05, main = "", cex.axis = 1) impact.barplot0( sim, perturb = 0, monitor = NA, epsilon = 1e-05, main = "", cex.axis = 1 )impact.barplot(sim, epsilon = 1e-05, main = "", cex.axis = 1) impact.barplot0( sim, perturb = 0, monitor = NA, epsilon = 1e-05, main = "", cex.axis = 1 )
sim |
the result from |
epsilon |
outomes below this in absolute magnitude are treated as zero. |
main |
text for plot title |
cex.axis |
character expansion factor for the edge labels |
perturb |
a named vector that indicates which nodes were perturbed and the relative magnitude of the perturbation. |
monitor |
n named vector of signs (-1,0,1) or NA that indicates the outcome of the perturbation. |
This control constructs a barplot that shows the fraction of simulations in which a positive (orange), negative (blue) or zero (off white) outcome occurs at each node following a given perturbation.
The user may specify the perturbation of the nodes, and any outcome known from monitoring the network, and then construct a barplot of the frequency table of outcomes at each node.
impact.barplot0 is a non-interactive variant for
programmatic use.
A Shiny app to display the impact of a perturbation as a barplot
impact.barplot.shiny(sim, epsilon = 1e-05, main = "", cex.axis = 1)impact.barplot.shiny(sim, epsilon = 1e-05, main = "", cex.axis = 1)
sim |
the result from |
epsilon |
outomes below this in absolute magnitude are treated as zero. |
main |
text for plot title |
cex.axis |
character expansion factor for the edge labels |
This control constructs a barplot that shows the fraction of simulations in which a positive (orange), negative (blue) or zero (off white) outcome occurs at each node following a given perturbation.
The user may specify the perturbation of the nodes, and any outcome known from monitoring the network, and then construct a barplot of the frequency table of outcomes at each node.
## Not run: set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) sims <- system.simulate(10, edges) impact.barplot.shiny(sims) ## End(Not run)## Not run: set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) sims <- system.simulate(10, edges) impact.barplot.shiny(sims) ## End(Not run)
Tabulate the impact of every positive perturbation as table.
impact.table(sim, epsilon = 1e-05)impact.table(sim, epsilon = 1e-05)
sim |
the result from |
epsilon |
outomes below this in absolute magnitude are treated as zero. |
Crosstabulate the mean impact (positive or negative) at each node for a positive perturbation of each node. The k-th column corresponds to a perturbation of the k-th node, and shows the mean impact on each node.
The crosstabulation as a matrix
Construct control widget
interactive.selection( action, nodes, edges = NULL, slider = NULL, checkbox = NULL, perturb = TRUE, monitor = TRUE )interactive.selection( action, nodes, edges = NULL, slider = NULL, checkbox = NULL, perturb = TRUE, monitor = TRUE )
action |
function to perform the widgets action |
nodes |
node labels |
edges |
edge labels |
slider |
slider label |
checkbox |
checkbox label |
perturb |
should a node perturbation control be rendered |
monitor |
should a node monitoring control be rendered |
Constructs a toplevel window that allows the allowing the user to interactively select nodes to perturb/monitor, from a subset of models, and then perform a given action.
The action argument must be function of five arguments
perturb the nodes that were perturbed
monitor the outcome of the monitoring
edge the edges to select
check the state of a checkbutton
slider the state of a slider
Read and write Dia representations of models
model.dia(file, labels = NULL) write.dia(edges, file, width = 8, height = 2, self = TRUE)model.dia(file, labels = NULL) write.dia(edges, file, width = 8, height = 2, self = TRUE)
file |
name of the file to read or write |
labels |
the sequence of labels to use for the nodes |
edges |
an edge list |
width |
width of the nodes in Dia |
height |
height of the nodes in Dia |
self |
should self edges be written. |
These functions read and write Dia representions of model toplogies.
These functions should be used with care as no attempt is made to
test for model mis-specification. The model.dia function
only recognizes node shapes "Flowchart - Ellipse", "Flowchart - Box"
and "Flowchart - Terminal", line types "Standard - Arc",
"Standard - ZigZagLine" and "Standard - Line", and arrow types 8,
1 and 5. Other node shapes, line or arrow types will be silently
ignored leading to a mispecified model.
The model.dia function returns an edge list.
Extract labels for the nodes and edges of the directed graph.
node.labels(edges) edge.labels(edges, reverse = FALSE)node.labels(edges) edge.labels(edges, reverse = FALSE)
edges |
an edge list |
reverse |
reverse the direction of edges |
These functions construct meaningful labels for the nodes and edges from an edge list.
Return a vector of node or edge labels
Parse a text representation of (directed) edges, return the index of the directed edge within the edge list.
parse.edge(lines, edges)parse.edge(lines, edges)
lines |
a vector of strings representing directed edges |
edges |
an edge list |
Each directed edge is represented as a string consisting of two node labels separated by an arrow, where the arrow consists of a sequence of dashes "-" followed by one of the character sequences ">","*","<>". The number of dashes used in the arrow is ignored.
the indices of the directed edges within the edge list
## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) parse.edge(c("E->D","D-*E","A-*B"),edges)## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) parse.edge(c("E->D","D-*E","A-*B"),edges)
Construct a function to calculate response to perturbation.
press.impact(edges, perturb, monitor = NULL)press.impact(edges, perturb, monitor = NULL)
edges |
an edge list. |
perturb |
a named vector that indicates which nodes were perturbed and the relative magnitude of the perturbation. |
monitor |
n named vector that indicates the subset of nodes to monitor. |
Given the an edge list that specifies a directed graph, a set of nodes to
perturb and a set of nodes to monitor, press.impact constructs a
function of a single argument W that determines the response of the
monitored nodes to the perturbation for a simulated community matrix
W.
Returns a function that when applied to a community matrix calculates the response to a press perturbation.
set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) s <- community.sampler(edges) s$select(0.5) ## Perturb D, monitor C f <- press.impact(edges,perturb=c(D=1),monitor=c(C=0)) W <- s$community() f(W) W <- s$community() f(W) ## Perturb D, monitor all f <- press.impact(edges,perturb=c(D=1)) W <- s$community() f(W) W <- s$community() f(W)set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) s <- community.sampler(edges) s$select(0.5) ## Perturb D, monitor C f <- press.impact(edges,perturb=c(D=1),monitor=c(C=0)) W <- s$community() f(W) W <- s$community() f(W) ## Perturb D, monitor all f <- press.impact(edges,perturb=c(D=1)) W <- s$community() f(W) W <- s$community() f(W)
Construct a function to test a validation criterion
press.validate(edges, perturb, monitor, epsilon = 1e-05)press.validate(edges, perturb, monitor, epsilon = 1e-05)
edges |
an edge list |
perturb |
a named vector that indicates which nodes were perturbed and the relative magnitude of the perturbation. |
monitor |
n named vector of signs (-1,0,1) that indicates the outcome of the perturbation. |
epsilon |
outomes below this in absolute magnitude are treated as zero. |
Given the an edge list that specifies a directed graph, a set of nodes to
perturb and a set of nodes to monitor, press.validate constructs a
function of a single argument W to test whether the response to
perturbation of the system represented by the community matrix W
matches an observed outcome. The outcome is only specified up to sign (-1,
0 or +1), where outcomes smaller than epsilon are treated as zero.
Returns a function that when applied to a community matrix determines whether the matrix is consistent with the given validation criterion.
set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) s <- community.sampler(edges) s$select(0.5) ## Perturb D, B and C must decrease f <- press.validate(edges,perturb=c(D=1),monitor=c(B=-1,C=-1)) W <- s$community() f(W) W <- s$community() f(W)set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) s <- community.sampler(edges) s$select(0.5) ## Perturb D, B and C must decrease f <- press.validate(edges,perturb=c(D=1),monitor=c(B=-1,C=-1)) W <- s$community() f(W) W <- s$community() f(W)
Construct a grid of radio buttons to select from a range of options that are common to many items.
radiogrid(parent, label, rows, choices, initial = 1, label.rows = TRUE)radiogrid(parent, label, rows, choices, initial = 1, label.rows = TRUE)
parent |
the parent window |
label |
the label for the enclosing frame |
rows |
the labels for the rows/items |
choices |
the labels for the columns/choices |
initial |
the initial selection |
label.rows |
whether to label rows |
Returns an object of class radiogrid with elements
window |
the widget |
selected |
function that returns the state of the radiobuttons |
state |
the tclVars representing the state of the radiobuttons |
Read and write text representations of models
read.digraph(file, labels = NULL) parse.digraph(lines, labels = NULL) deparse.digraph(edges) write.digraph(edges, file = "")read.digraph(file, labels = NULL) parse.digraph(lines, labels = NULL) deparse.digraph(edges) write.digraph(edges, file = "")
file |
the name of the file to read or write |
labels |
the sequence of labels to use for the nodes |
lines |
a string representation of the model |
edges |
an edge list. |
The functions read.digraph and parse.digraph read a model
description from a text file and a string respectively, while
write.digraph writes a text representation of the model to and file.
These functions recognize the following text format. Each line corresponds to an edge, and must consist of two node labels separated by an arrow. An arrow consists of one of the character sequences "<","*","<>" or "" on the left and ">","*","<>" or "" on the right, separated by a sequence of dashes "-". The number of dashes used in the arrow defines the group number of the edge.
The write.digraph function invisibly returns the
text that was written to the file.
The functions read.digraph and parse.digraph return an
edge list - a data frame with columns
From |
a factor indicating the origin of each edge (the node that effects) |
To |
a factor indicating the destination of each edge (the node that is effected) |
Group |
an integer vector that indicates the group each edge belongs to |
Type |
a factor indicating the edge type - "N" (negative) ,"P" (positive),"U" (unknown) or "Z" (zero) |
Pair |
an integer vector that indicates the pairing of directed edges |
Each edge of the text specification is separated into two directed edges, and every row of an edge list corresponds to a single directed edge.
edges <- parse.digraph(c("A <-* B","C *-> A","C <- D", "D -> B","B *--* C","A <--- D")) edges deparse.digraph(edges)edges <- parse.digraph(c("A <-* B","C *-> A","C <- D", "D -> B","B *--* C","A <--- D")) edges deparse.digraph(edges)
Subset an edge list
retain.groups(edges, groups) retain.nodes(edges, nodes)retain.groups(edges, groups) retain.nodes(edges, nodes)
edges |
an edge list |
groups |
the groups to retain in the subset |
nodes |
the nodes to retain in the subset |
These functions extract a subset of an edge list containing only edges in a specified group, or incident with a specified set of nodes.
retain.groups returns an edge list containing only edges from
the specified groups.
retain.nodes returns an edge list containing only edges
incident on the specified nodes.
edges <- parse.digraph(c("A *-> B","B *-> C","C *--> D")) write.digraph(retain.groups(edges,c(0)))edges <- parse.digraph(c("A *-> B","B *-> C","C *--> D")) write.digraph(retain.groups(edges,c(0)))
Classify the sign of the elements of a vector
signum(x, epsilon = 1e-05)signum(x, epsilon = 1e-05)
x |
vector of values to test |
epsilon |
magnitude threshold |
Calculates the sign of the elements of then vector x, except that values less than epsilon in magnitude are rounded down to zero.
Returns a vector with elements +1,0 or -1.
signum(c(-40,-3,-0.1E-8,0,2,5))signum(c(-40,-3,-0.1E-8,0,2,5))
Construct a slider widget.
slider(parent, initial = 1, from = 0, to = 100, orient = "horizontal")slider(parent, initial = 1, from = 0, to = 100, orient = "horizontal")
parent |
the parent window |
initial |
the initial values of the sliders |
from |
minimum slider values |
to |
maximum slider value |
orient |
slider orientation |
The slider function creates a widget containing a single
horizontal slider.
Returns an object of class slider with elements
window |
the widget |
selected |
function that returns the state of the sliders |
state |
the tclVars representing the state of the sliders |
Test community matrix stability
stable.community(W)stable.community(W)
W |
a simulated community matrix |
The system is stable if the eigenvalues of community matrix all have negative real part. This function tests the eigenvalues of a simulated community matrix to determine the stability of the repreeented system.
Returns TRUE if the system is stable, FALSE otherwise.
set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) s <- community.sampler(edges) s$select(0) ## First sample is stable W <- s$community() stable.community(W) ## Second is not W <- s$community() stable.community(W)set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) s <- community.sampler(edges) s$select(0) ## First sample is stable W <- s$community() stable.community(W) ## Second is not W <- s$community() stable.community(W)
Simulate Inverse Community Matrices for a Network
system.simulate( n.sims, edges, required.groups = c(0), sampler = community.sampler(edges, required.groups), validators = NULL )system.simulate( n.sims, edges, required.groups = c(0), sampler = community.sampler(edges, required.groups), validators = NULL )
n.sims |
number of matrices to simulate. |
edges |
an edge list. |
required.groups |
a vector of integers specifying which groups of edges must always occur in the community matrix. |
sampler |
the sampler object used to generate random weights (see
|
validators |
an (optional) list of validation functions generated by
|
Generate sets of edge weights and the inverse community matrices given a directed graph and validation criteria by rejection sampling. Matrices with a pattern of signs consistent with the given model are generated, and only the matrices that correspond to stable equilibria and consistent with the given validation criteria are retained. For matrices retained in the sample, the matrix is inverted, and the inverse community matrix, and the weights that define the community matrix are returned. The function also returns the total number of matrices generated, the number of these that are stable and the number that are ultimately accepted for the sample.
The output of this function may be passed to the interactive exploratory tools.
This function is a simple wrapper for community.sampler,
stable.community and the functions generated by
press.validate.
Returns a list with elements
edges |
The edge list |
A |
A list of inverse community matrices |
w |
A matrix of the corresponding edge weights |
total |
The total number of matrices generated |
stable |
The number of stable matrices generated |
accepted |
The number of matrices accepted for the sample |
set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) sims <- system.simulate(10,edges, validators=list( press.validate(edges, perturb=c(D=1), monitor=c(D=1)), press.validate(edges, perturb=c(D=1), monitor=c(B=-1,C=1))))set.seed(32) ## Sample model edges <- parse.digraph(c( "E *-> D", "D *-> C", "C -> E", "E *-> B", "B *-> A", "A -> E", "D --> B")) edges <- enforce.limitation(edges) sims <- system.simulate(10,edges, validators=list( press.validate(edges, perturb=c(D=1), monitor=c(D=1)), press.validate(edges, perturb=c(D=1), monitor=c(B=-1,C=1))))
Display weights of valid and invalid matrices as a density plots
weight.density(sim, epsilon = 1e-05, main = "") weight.density0( sim, perturb, monitor, edges, smooth = 1, epsilon = 1e-05, main = "" )weight.density(sim, epsilon = 1e-05, main = "") weight.density0( sim, perturb, monitor, edges, smooth = 1, epsilon = 1e-05, main = "" )
sim |
the result from |
epsilon |
outomes below this in absolute magnitude are treated as zero. |
main |
text for plot title |
perturb |
a named vector that indicates which nodes were perturbed and the relative magnitude of the perturbation. |
monitor |
n named vector of signs (-1,0,1) or NA that indicates the outcome of the perturbation. |
edges |
logical vector indicating which edges to plot. |
smooth |
double in the range [0,1] controlling the level of smoothing applied. |
This control constructs density plots that show the distribution of selected edge weights for the cases that meet the selected validation criteria (blue), and those that do not (red), following a given perturbation.
The slider controls the level of smoothing of the densities. Edges are labelled by pairs of integers for compactness, where the integer codes correspond to the ordering of the node labels.
weight.density0 is a non-interactive variant for
programmatic use.
Shiny app to display weights of valid and invalid matrices as a density plots
weight.density.shiny(sim, epsilon = 1e-05, main = "")weight.density.shiny(sim, epsilon = 1e-05, main = "")
sim |
the result from |
epsilon |
outomes below this in absolute magnitude are treated as zero. |
main |
text for plot title |
This control constructs density plots that show the distribution of selected edge weights for the cases that meet the selected validation criteria (blue), and those that do not (red), following a given perturbation.
The slider controls the level of smoothing of the densities. Edges are labelled by pairs of integers for compactness, where the integer codes correspond to the ordering of the node labels.
weight.density0 is a non-interactive variant for
programmatic use.