b09 aggregate Ruhela: Female -> individuals in segregation

Usage

morie_otis_analyze_b09_ruhela_aggregate(data, out_dir = NULL)

morie_otis_analyze_b09_mrm_aggregate(data, out_dir = NULL)

Arguments

data: b09 data.frame.
out_dir: Optional.

Value

morie_otis_analysis_result.

Examples

# \donttest{
otis_b09 <- morie_synth_otis("b09", n = 120L, seed = 1L)
morie_otis_analyze_b09_ruhela_aggregate(otis_b09)
#> OTIS b09 -- Aggregate Ruhela formulation: Female -> Number of individuals in segregation
#> ========================================================================================
#>   Source file                            b09_individuals_in_segregation_number_of_times_in_segregation.csv
#>   Dataset id                             b09
#>   Treatment column                       T_female
#>   Outcome (count)                        NumberIndividuals_Segregation
#>   Year FE                                EndFiscalYear
#>   Covariate FEs                          NumberPlacements_Segregation
#>   Cluster group (GEE)                    --
#>   Cells                                  120
#>   Total outcome count                    4987
#>   Overdispersion (Poisson AIC - NB AIC)  1194.6
#> 
#> Aggregate Ruhela formulation -- Poisson + NB IRR for treatment effect on count outcome:
#>   Family   Coefficient        IRR     95% CI          p-value   AIC     Notes
#>   -----------------------------------------------------------------------------------------
#>   Poisson  factor(T_female)1  0.8495  [0.799, 0.903]  1.66e-07  2342.7  n=120, total_y=4987
#>   NB       factor(T_female)1  0.832   [0.632, 1.094]  1.89e-01  1148.1  n=120, total_y=4987
#> 
#> Aggregate RF on b09 testing gender disparity in the individuals-in-segregation count distribution.
#> 
#> IRR > 1 ==> treatment increases the count rate; IRR < 1 ==> treatment decreases it. Concordance between Poisson and NB indicates equidispersion; large gap (Poisson AIC > NB AIC) indicates overdispersion -- trust NB. 
# }