Compares the partial bivariate patterns B1, B2, and B3 using AIC and returns the selected pattern.
Arguments
- empirical
An empirical bivariate count matrix with 16 rows and 2 columns, as returned by
countEmpBivariate.
Value
A list with class c("dyadic_pattern", "list") containing
components aic (a data frame with candidate patterns and AIC
values), pattern (the selected pattern label), and call. It
remains usable as an ordinary list. When empirical was created by
countEmpBivariate(), its stored sequence metadata is propagated so
that plot() can display the four observed state strips.
Details
Conditional on the partial bivariate case, the G-squared deviance is computed for each B1, B2, and B3 candidate before calculating $$AIC = G^2 + 2k,$$ where $$G^2 = 2 \sum_{ij} O_{ij} \log(O_{ij} / E_{ij}).$$ The candidate with the smallest AIC is selected. If candidates have exactly equal AIC values, the first candidate in the documented comparison order is selected.
Examples
chainFM_V1 <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM_V1 <- c(2L, 1L, 2L, 1L, 1L, 2L)
chainFM_V2 <- c(1L, 1L, 2L, 2L, 1L, 2L)
chainSM_V2 <- c(2L, 2L, 1L, 1L, 2L, 1L)
emp <- countEmpBivariate(
chainFM_V1, chainSM_V1, chainFM_V2, chainSM_V2,
states = 2L
)
partialPattern(emp)
#> Dyadic interaction pattern
#> Pattern: partial actor only (B2)