Description Usage Arguments Details Value See Also Examples
A set of edits can be represented as a graph where every vertex is
an edit. Two vertices are connected if they have at least one variable
in vars
in common.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  adjacency(E, nodetype = c("all", "rules", "vars"), rules = rownames(E),
vars = getVars(E), ...)
## S3 method for class 'editmatrix'
adjacency(E, nodetype = c("all", "rules", "vars"),
rules = rownames(E), vars = getVars(E), ...)
## S3 method for class 'editarray'
adjacency(E, nodetype = c("all", "rules", "vars"),
rules = rownames(E), vars = getVars(E), ...)
## S3 method for class 'editset'
adjacency(E, nodetype = c("all", "rules", "vars"),
rules = c(rownames(E$num), rownames(E$mixcat)), vars = getVars(E), ...)
## S3 method for class 'editmatrix'
as.igraph(x, nodetype = c("all", "rules", "vars"),
rules = editnames(x), vars = getVars(x), weighted = TRUE, ...)
## S3 method for class 'editarray'
as.igraph(x, nodetype = c("all", "rules", "vars"),
rules = editnames(x), vars = getVars(x), weighted = TRUE, ...)
## S3 method for class 'editset'
as.igraph(x, nodetype = c("all", "rules", "vars"),
rules = editnames(x), vars = getVars(x), weighted = TRUE, ...)

E 

nodetype 
adjacency between rules, vars or both? 
rules 
selection of edits 
vars 
selection of variables 
... 
arguments to be passed to or from other methods 
x 
An object of class 
weighted 
see 
adjacency
returns the adjacency matrix. The elements of the matrix
count the number of variables shared by the edits indicated in the row and
column names. The adjacency matrix can be converted to an igraph object with
graph.adjacency
from the igraph
package.
as.igraph
converts a set of edits to an igraph
object directly.
the adjacency matrix of edits in E
with resect to
the variables in vars
plot.editmatrix
, plot.editarray
, plot.editset
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94  ## Examples with linear (in)equality edits
# load predefined edits from package
data(edits)
edits
# convert to editmatrix
E < editmatrix(edits)
## Not run:
# (Note to reader: the Not run directive only prevents the examle commands from
# running when package is built)
# Total edit graph
plot(E)
# Graph with dependent edits
plot(E, nodetype="rules")
# Graph with dependent variables
plot(E, nodetype="vars")
# Total edit graph, but with curved lines (option from igraph package)
plot(E, edge.curved=TRUE)
# graph, plotting just the connections caused by variable 't'
plot(E,vars='t')
## End(Not run)
# here's an example with a broken record.
r < c(ct = 100, ch = 30, cp = 70, p=30,t=130 )
violatedEdits(E,r)
errorLocalizer(E,r)$searchBest()$adapt
# we color the violated edits and the variables that have to be adapted
## Not run
set.seed(1) # (for reprodicibility)
plot(E,
adapt=errorLocalizer(E,r)$searchBest()$adapt,
violated=violatedEdits(E,r))
## End(Not run)
# extract total graph (as igraph object)
as.igraph(E)
# extract graph with edges related to variable 't' and 'ch'
as.igraph(E,vars=c('t','ch'))
# extract total adjacency matrix
adjacency(E)
# extract adjacency matrix related to variables t and 'ch'
adjacency(E,vars=c('t','ch'))
## Examples with categorical edits
# generate an editarray:
E < editarray(expression(
age %in% c('<15','1665','>65'),
employment %in% c('unemployed','employed','retired'),
salary %in% c('none','low','medium','high'),
if (age == '<15') employment=='unemployed',
if (salary != 'none') employment != 'unemployed',
if (employment == 'unemployed') salary == 'none'))
## Not run:
# plot total edit graph
plot(E)
# plot with a different layout
plot(E,layout=layout.circle)
# plot edit graph, just the connections caused by 'salary'
plot(E,vars='salary')
## End(Not run)
# extract edit graph
as.igraph(E)
# extract edit graph, just the connections caused by 'salary'
as.igraph(E,vars='salary')
# extract adjacency matrix
adjacency(E)
# extract adjacency matrix, only caused by 'employment'
adjacency(E,vars='employment')

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