make_projection() outputs the agent or symbolic network corresponding
to a survey, i.e. the row or column projection.
make_projection(
data,
layer = NULL,
method = NULL,
methodval = NULL,
comparisons = NULL,
metric = NULL,
limits = NULL,
dummycode = NULL,
bootreps = NULL,
bootval = NULL,
bootseed = NULL,
centre = NULL,
...
)A data frame corresponding to the edge list of the specified network. It contains three columns named
u, the first node adjacent to the edge
v, the second node adjacent to the edge, and
weight, the similarity between nodes u and v
A data frame corresponding to a survey
A string flag specifying which layer to project
"agent" produces the network corresponding to the agents, which we assume
to be rows in data
"symbolic" produces the network corresponding to the symbols, or items,
which we assume to be columns in data
A string flag specifying how edges are thresholded in the network representation.
"similarity" means we remove all edges whose weight, meaning node
similarity, is below a threshold specified by methodval.
"lcc" finds the value of the threshold that results in the network
whose largest connected component is as close as possible to a specified
value. In general a range of thresholds will satisfy this condition, and we
choose the upper limit of this range. As such, "lcc" provided is a target.
"avgdegree" finds the value of the threshold that results in the network
whose average degree is as close as possible to a specified value. Like
"lcc", this is a target.
A utility variable that we interpret according to the
method chosen.
If method = "similarity", then methodval is
interpreted as the similarity threshold, and thus is in the range [0, 1].
A value of 0 means no edges are removed, and a value of 1 means all edges
are removed.
If method = "lcc", then methodval is interpreted as
the desired fractional size of the largest connected component, in the range
[0, 1]. E.g., when set to 0, no nodes are connected, and if set to 1, the
network is as sparse as possible while remaining fully connected.
If method = "avgdegree", then methodval is interpreted as
the desired average degree. We assume that methodval is normalised to
the range [0, 1] When method_value = 0, then no nodes are connected, and
if method_value = 1, the network is complete, meaning it contains every
possible edge.
The minimum number of valid comparisons that must be
made when computing the similarity between rows or columns in the data. If at
least one of the entries in the fields being compared is NA, then the
comparison is invalid.
This currently has just one allowed value, namely the Manhattan distance, which is the default.
Specifies the limits of the Likert scale contained in data.
flag that indicates whether we dummycode data.
The number of bootstrap realisations to perform. If not specified, bootstrapping is not carried out.
A sampling probability used when bootstraping. In particular, it provides the probability of sampling a given survey entry in a given bootstrapping step. With probability 1 - bootval, that entry is set to NA.
A random number generator seed used when bootstrapping. Mainly used for testing, but maybe useful for reproducibility in general.
If TRUE, we shift edge weights from [0, 1] to [-1, 1].
Defaults to FALSE, as most network analysis applications require positive
edge weights.
Mostly used to handle deprecated arguments, and arguments with alternative spellings.
S <- make_synthetic_data(20, 5)
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