as_networks() is the psychometric-network counterpart of
as_tna. It promotes the cluster-level (macro) and
within-cluster networks produced by build_mcml_pc into a
single netobject_group, so the result flows into the same
downstream verbs as any other group of networks (print(),
summary(), plot(), net_centrality).
Usage
as_networks(x)
# S3 method for class 'mcml_pc'
as_networks(x)
# Default S3 method
as_networks(x)Arguments
- x
An object to convert. The
mcml_pcmethod (frombuild_mcml_pc) is the primary path.
Value
A netobject_group: a named list whose first element is
macro (the cluster-level network), followed by one netobject per
non-singleton cluster.
The mcml_pc method returns a netobject_group;
singleton clusters (no within-network) are dropped with a
warning().
The default method returns the input unchanged if it is already a
netobject_group, otherwise it errors.
Details
Where as_tna() promotes transition networks (directed,
row-normalised, with initial probabilities) and re-wraps raw matrices,
as_networks() promotes psychometric networks (undirected;
correlation / partial-correlation / glasso). The macro and within-cluster
components of an mcml_pc object are already full netobjects carrying
their estimator, directedness and data, so this function assembles them
into a group rather than re-wrapping matrices.
See also
build_mcml_pc to create the input,
as_tna for the transition-network counterpart.
Examples
set.seed(1)
f <- stats::rnorm(200)
g <- stats::rnorm(200)
df <- data.frame(a1 = f + stats::rnorm(200), a2 = f + stats::rnorm(200),
a3 = f + stats::rnorm(200), b1 = g + stats::rnorm(200),
b2 = g + stats::rnorm(200), b3 = g + stats::rnorm(200))
clusters <- list(A = c("a1", "a2", "a3"), B = c("b1", "b2", "b3"))
fit <- build_mcml_pc(df, clusters, aggregation = "composite", method = "cor")
nets <- as_networks(fit)
nets
#> Group Networks (3 groups)
#>
#> Group Nodes Edges Weights
#> macro 2 1 [0.007, 0.007]
#> A 3 3 [0.443, 0.472]
#> B 3 3 [0.462, 0.540]
summary(nets)
#> Network metrics by group:
#> metric macro A B
#> Node Count 2 3 3
#> Edge Count 2 6 6
#> Network Density 1 1 1
#> Mean Distance 0.00676 0.4597 0.5117
#> Mean Out-Strength 0.00676 0.9193 1.023
#> SD Out-Strength 0 0.01531 0.04279
#> Mean In-Strength 0.00676 0.9193 1.023
#> SD In-Strength 0 0.01531 0.04279
#> Mean Out-Degree 1 2 2
#> SD Out-Degree 0 0 0
#> Centralization (Out-Degree) 0 0 0
#> Centralization (In-Degree) 0 0 0
#> Reciprocity 1 1 1