Identifies the bivariate case as "trivial", "univariate",
"partial", or "complete" using two likelihood-ratio tests
against constrained bivariate structures.
Arguments
- empirical
An empirical bivariate count matrix with 16 rows and 2 columns, as returned by
countEmpBivariate.- alpha
A single number in (0, 1) giving the significance level. Default is 0.05.
Value
A list with class c("dyadic_case", "list") containing
components testUnivariate, testPartial, case, and
metadata fields alpha and call. It remains usable as an
ordinary list.
Details
The returned case corresponds to the global approach of the bivariate method. It determines whether the sequence analyzed is treated as trivial, univariate, partial bivariate, or complete bivariate before the local identification of the pattern of interaction.
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
)
bivariateCase(emp, alpha = 0.05)
#> Bivariate dyadic case
#> Case: trivial
#> Alpha: 0.05