Transforms netify network objects into statnet's network
objects (also available as netify_to_network,
to_statnet, and to_network),
providing access to the extensive statistical modeling capabilities
of the statnet suite, including ERGMs (Exponential Random Graph Models),
descriptive statistics, and network visualization tools.
Usage
netify_to_statnet(netlet, add_nodal_attribs = TRUE, add_dyad_attribs = TRUE)
netify_to_network(netlet, add_nodal_attribs = TRUE, add_dyad_attribs = TRUE)
to_statnet(netlet, add_nodal_attribs = TRUE, add_dyad_attribs = TRUE)
to_network(netlet, add_nodal_attribs = TRUE, add_dyad_attribs = TRUE)Arguments
- netlet
A netify object containing network data. Currently supports single-layer networks only. For multilayer networks, use
subset_netifyto extract individual layers first.- add_nodal_attribs
Logical. If TRUE (default), includes nodal attributes from the netify object as vertex attributes in the network object. Set to FALSE to create a network with structure only.
- add_dyad_attribs
Logical. If TRUE (default), includes dyadic attributes from the netify object as edge attributes in the network object. Set to FALSE to exclude edge covariates.
Value
A network object or list of network objects:
- Cross-sectional networks
Returns a single network object
- Longitudinal networks
Returns a named list of network objects, with names corresponding to time periods
The resulting network object(s) will have:
Vertices named according to actors in the netify object
Edge weights from the netify weight variable stored as "weight" attribute
Vertex attributes for each nodal variable (if add_nodal_attribs = TRUE)
Edge attributes for each dyadic variable (if add_dyad_attribs = TRUE)
Proper directedness based on the symmetric parameter of the netify object
Details
The conversion process handles different netify structures:
Cross-sectional: Direct conversion to a single network object
Longitudinal arrays: Internally converted to list format, then each time slice becomes a separate network object
Longitudinal lists: Each time period converted to separate network object
The statnet network format stores networks as an edgelist with attributes, making it memory-efficient for sparse networks. All nodal and dyadic attributes from the netify object are preserved and can be used in subsequent ERGM modeling or network analysis.
For longitudinal data, each time period results in an independent network object. This format is suitable for discrete-time network analysis or pooled ERGM estimation across time periods.
Note
This function requires the network package (part of statnet) to be installed.
For ERGM modeling, the ergm package (also part of statnet) should be loaded after creating the network objects.
Examples
# Load example data
data(icews)
# Cross-sectional example
icews_10 <- icews[icews$year == 2010,]
# Create netify object with attributes
dvars <- c('matlCoop', 'verbConf', 'matlConf')
nvars <- c('i_polity2', 'i_log_gdp', 'i_log_pop')
verbCoop_net <- netify(
icews_10,
actor1 = 'i', actor2 = 'j',
symmetric = FALSE,
weight = 'verbCoop',
dyad_vars = dvars,
dyad_vars_symmetric = rep(FALSE, length(dvars)),
nodal_vars = nvars
)
# Convert to statnet network object
ntwk <- netify_to_statnet(verbCoop_net)
# Examine the result
ntwk
#> Network attributes:
#> vertices = 152
#> directed = TRUE
#> hyper = FALSE
#> loops = FALSE
#> multiple = FALSE
#> bipartite = FALSE
#> verbCoop: 152x152 matrix
#> matlCoop: 152x152 matrix
#> verbConf: 152x152 matrix
#> matlConf: 152x152 matrix
#> total edges= 9976
#> missing edges= 0
#> non-missing edges= 9976
#>
#> Vertex attribute names:
#> i_log_gdp i_log_pop i_polity2 vertex.names
#>
#> Edge attribute names not shown
network::network.size(ntwk) # number of vertices
#> [1] 152
network::network.edgecount(ntwk) # number of edges
#> [1] 9976
network::list.vertex.attributes(ntwk) # nodal attributes
#> [1] "i_log_gdp" "i_log_pop" "i_polity2" "na" "vertex.names"
network::list.edge.attributes(ntwk) # edge attributes
#> [1] "matlConf_e" "matlCoop_e" "na" "verbConf_e" "verbCoop"
# Access specific attributes
network::get.vertex.attribute(ntwk, 'i_polity2') # polity scores
#> [1] NA 9 2 -2 8 5 10 10 -7 -5 5 -7 8 7 7 NA 8 9
#> [19] 0 6 2 -4 10 10 -1 -2 10 -7 7 9 -4 5 10 0 9 -7
#> [37] 10 10 2 8 5 -3 8 -6 9 -3 -4 10 10 3 -5 6 10 8
#> [55] 10 8 1 6 6 0 7 10 9 8 -7 3 10 6 10 9 10 -3
#> [73] -6 8 -10 8 -7 4 -7 8 6 6 -7 10 10 9 0 6 6 7
#> [91] -2 10 8 9 10 -6 5 -6 6 10 10 9 3 4 10 -8 6 9
#> [109] 4 8 9 8 10 10 -10 9 4 -4 -10 7 7 -2 10 10 8 0
#> [127] 9 10 3 -2 5 10 10 -7 -3 -1 4 -2 10 -4 -9 -1 6 -8
#> [145] 10 10 10 -9 -7 -2 7 1
network::get.edge.attribute(ntwk, 'matlCoop') # material cooperation
#> NULL
# Check network properties
network::is.directed(ntwk) # TRUE for this example
#> [1] TRUE
network::has.loops(ntwk) # FALSE (no self-ties)
#> [1] FALSE
# Longitudinal example
verbCoop_longit <- netify(
icews,
actor1 = 'i', actor2 = 'j', time = 'year',
symmetric = FALSE,
weight = 'verbCoop',
dyad_vars = dvars,
dyad_vars_symmetric = rep(FALSE, length(dvars)),
nodal_vars = nvars
)
# Convert to list of network objects
ntwk_list <- netify_to_statnet(verbCoop_longit)
# Examine structure
length(ntwk_list) # number of time periods
#> [1] 13
names(ntwk_list) # time period labels
#> [1] "2002" "2003" "2004" "2005" "2006" "2007" "2008" "2009" "2010" "2011"
#> [11] "2012" "2013" "2014"
# Access specific time period
ntwk_2002 <- ntwk_list[['2002']]
ntwk_2002
#> Network attributes:
#> vertices = 152
#> directed = TRUE
#> hyper = FALSE
#> loops = FALSE
#> multiple = FALSE
#> bipartite = FALSE
#> verbCoop: 152x152 matrix
#> matlCoop: 152x152 matrix
#> verbConf: 152x152 matrix
#> matlConf: 152x152 matrix
#> total edges= 8692
#> missing edges= 0
#> non-missing edges= 8692
#>
#> Vertex attribute names:
#> i_log_gdp i_log_pop i_polity2 vertex.names
#>
#> Edge attribute names not shown
if (FALSE) { # \dontrun{
# Use with ergm for modeling (requires ergm package)
library(ergm)
model <- ergm(ntwk ~ edges + mutual + nodematch("i_polity2"))
} # }