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Convert netify objects to amen format

Source: R/to_amen.R
netify_to_amen.Rd

netify_to_amen (also available as to_amen) transforms netify network objects into the data structure required by the amen package for advanced network modeling. This enables the use of Social Relations Models (SRM), Additive and Multiplicative Effects (AME) models, and other network regression approaches implemented in amen.

Usage

netify_to_amen(netlet, lame = FALSE)

to_amen(netlet, lame = FALSE)

Arguments

netlet

A netify object (class "netify") containing network data. Must be a single-layer network. For multilayer networks, first extract individual layers using subset_netify.

lame

Logical. Controls the output format for longitudinal data:

  • FALSE (default): Formats output for compatibility with the standard version of the amen package, which uses array structures

  • TRUE: Formats output for compatibility with the netify-verse version called lame, which supports longitudinal network modeling with time-varying actor compositions and other features

This parameter is ignored for cross-sectional data.

Value

The structure of the returned list depends on the data type and lame parameter:

For cross-sectional data or longitudinal with lame = FALSE (standard amen):

Y

Network adjacency data as a numeric matrix or array:

  • Cross-sectional: Matrix of dimensions [n_actors × n_actors]

  • Longitudinal: Array of dimensions [n_actors × n_actors × n_time]

Contains edge weights or binary indicators. Missing edges are preserved as NA.

Xdyad

Dyadic covariates as an array, or NULL if none exist:

  • Cross-sectional: [n_actors × n_actors × n_covariates]

  • Longitudinal: [n_actors × n_actors × n_covariates × n_time]

Each slice contains one dyadic covariate across all actor pairs.

Xrow

Sender/row actor attributes as a matrix or array, or NULL if none exist:

  • Cross-sectional: [n_actors × n_attributes]

  • Longitudinal: [n_actors × n_attributes × n_time]

Contains numeric attributes for actors when they act as senders.

Xcol

Receiver/column actor attributes, structured identically to Xrow. For symmetric networks, Xcol is identical to Xrow. For bipartite networks, contains attributes for the second mode.

For longitudinal data with lame = TRUE (lame package):

Y

A list of length T (time periods), where each element is an n × n relational matrix. Actor sets can vary across time periods.

Xdyad

A list of length T, where each element is an n × n × pd array of dyadic covariates, or NULL if none exist

Xrow

A list of length T, where each element is an n × pr matrix of nodal row covariates, or NULL if none exist

Xcol

A list of length T, where each element is an n × pc matrix of nodal column covariates, or NULL if none exist

Details

Variable requirements:

  • All nodal attributes must be numeric (integer or double)

  • Character or factor variables must be converted before using this function

  • Missing values (NA) are preserved and can be handled by amen's models

When to use each format:

  • Use lame = FALSE when:

    • Actor composition is constant across time

  • Use lame = TRUE when:

    • Actors enter/exit the network over time

    • Want access to other features in lame

Note

The function performs several validation checks:

  • Ensures single-layer networks (multilayer not supported)

  • Verifies all nodal attributes are numeric

  • Maintains actor ordering from the original netify object

Author

Ha Eun Choi, Cassy Dorff, Colin Henry, Shahryar Minhas

Examples

# Load example data
data(icews)

# Create a netify object
net <- netify(
  icews[icews$year == 2010,], 
  actor1 = 'i', actor2 = 'j',
  symmetric = FALSE, 
  weight = 'verbCoop'
)

# Convert to amen format (standard)
amen_data <- netify_to_amen(net)
names(amen_data)  # Y, Xdyad, Xrow, Xcol
#> [1] "Y"     "Xdyad" "Xrow"  "Xcol" 

if (FALSE) { # \dontrun{
# For longitudinal data with time-varying composition
longit_net <- netify(
  icews, 
  actor1 = 'i', actor2 = 'j', time = 'year',
  symmetric = FALSE, 
  weight = 'verbCoop'
)

# Convert to lame format
lame_data <- netify_to_amen(longit_net, lame = TRUE)
} # }