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Calculate SRM actor, partner, or unique effects

Source: R/srm_effects.R
srm_effects.Rd

Decomposes a round-robin network into actor (sender), partner (receiver), and unique (dyadic) effects using the Social Relations Model framework. Requires a square (unipartite) adjacency matrix with at least 3 actors; matrices with fewer than 3 actors return NA values. For bipartite networks, use srm() instead.

Usage

srm_effects(mat, type = c("actor", "partner", "unique"), time = NULL)

Arguments

mat

A square numeric matrix (or list of square matrices for longitudinal data). Diagonal values are set to zero internally. NAs are replaced with zero. Must not be an srm object (effects are already stored in fit$actor_effects, etc.).

type

One of "actor", "partner", or "unique":

  • "actor": sender effects, returned as an n x 1 matrix.

  • "partner": receiver effects, returned as an n x 1 matrix.

  • "unique": dyadic residuals, returned as an n x n matrix with zero diagonal.

time

Optional character vector of time-point names to subset when mat is a named list.

Value

For a single matrix, an effect matrix (with class actor_effect, partner_effect, or unique_effect). For a list of matrices, a list of such matrices. See srm() for the unified interface.

Details

When the input matrix is symmetric (undirected), actor and partner effects are identical by construction. The decomposition is most informative for directed (asymmetric) networks where sender and receiver roles are distinct.

Examples

# simple inline example
test_matrix = matrix(c(0, 1, 2, 2, 0, 1, 1, 2, 0), nrow = 3, ncol = 3)
rownames(test_matrix) = colnames(test_matrix) = c("A", "B", "C")
actor_effect = srm_effects(test_matrix, type = "actor")

# cross-sectional with included dataset
data(classroom)
actor_eff = srm_effects(classroom, type = "actor")
head(actor_eff)
#>          actor_effect
#> Cady        1.0130833
#> Aaron       0.2873333
#> Gretchen   -0.2332500
#> Karen       1.0540833
#> Janis      -0.4112500
#> Damian      2.3567500

partner_eff = srm_effects(classroom, type = "partner")
unique_eff = srm_effects(classroom, type = "unique")

# longitudinal
data(trade_net)
actor_long = srm_effects(trade_net, type = "actor")
actor_2017 = srm_effects(trade_net, type = "actor", time = "2017")