Detect hierarchical structure in data

Usage

detect_hierarchical_structure(data, max_levels = 5, method = "auto")

Arguments

data: A data frame containing outcome and potential grouping variables.
max_levels: Integer specifying the maximum number of hierarchical levels to detect. Default is 5.
method: Character string specifying the detection method. One of "auto", "variance", or "information".

Value

A list with components levels, level_names, level_variables, n_levels, units_per_level, nesting_structure, cross_classification, recommendations, and icc_values.

Examples

d <- data.frame(
  case = rep(c("C1", "C2", "C3"), each = 8),
  phase = rep(c("A", "B"), 12),
  time = rep(1:8, 3),
  outcome = rnorm(24)
)
detect_hierarchical_structure(d)
#> $levels
#> $levels[[1]]
#> [1] "case"
#> 
#> 
#> $level_names
#> [1] "Level 2"
#> 
#> $level_variables
#> [1] "case"
#> 
#> $n_levels
#> [1] 1
#> 
#> $units_per_level
#> [1] 3
#> 
#> $nesting_structure
#> list()
#> 
#> $cross_classification
#> [1] FALSE
#> 
#> $recommendations
#> $recommendations$model_type
#> [1] "nested_random_effects"
#> 
#> $recommendations$estimation
#> [1] "bayesian"
#> 
#> $recommendations$estimation_message
#> [1] "Small number of units at higher levels ( 3 ). Bayesian estimation recommended."
#> 
#> 
#> $variance_based
#> $variance_based$case
#> $variance_based$case$variable
#> [1] "case"
#> 
#> $variance_based$case$level
#> [1] "level5"
#> 
#> $variance_based$case$between_variance
#> [1] 0.09516285
#> 
#> $variance_based$case$prop_variance
#> [1] 0.09053894
#> 
#> $variance_based$case$n_groups
#> [1] 3
#> 
#> 
#> 
#> $ic_based
#> $ic_based$case
#> $ic_based$case$variable
#> [1] "case"
#> 
#> $ic_based$case$n_groups
#> [1] 3
#> 
#> $ic_based$case$ic_score
#> [1] 3.534161
#> 
#> $ic_based$case$effective_sample_per_group
#> [1] 8
#> 
#> 
#> 
#> $crossed_factors
#> list()
#> 
#> $icc_values
#> $icc_values$case
#> [1] 0
#> 
#>