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','16-65','>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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