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Assess the quality of mean estimations

assess.Rd

assess evaluates the quality of mean estimations using the methodology created by INE Chile, which considers sample size, degrees of freedom, and coefficient of variation.

Usage

assess(
  table,
  publish = FALSE,
  scheme = c("chile", "eclac_2020", "eclac_2023", "chile_economics"),
  domain_info = FALSE,
  low_df_justified = FALSE,
  table_n_obj = NULL,
  ratio_between_0_1 = TRUE,
  ...
)

Arguments

table

dataframe created by crear_insumos_media.

publish

boolean indicating if the evaluation of the complete table must be added. If TRUE, the function adds a new column to the dataframe.

scheme

character variable indicating the evaluation protocol to use. Options are "chile", "eclac_2020", "eclac_2023", "chile_economics".

domain_info

Logical. If TRUE, indicates that the study domain information is available and will be used for assessment. This affects how the evaluation is conducted, leveraging specific domain-level data to refine the assessment results. When FALSE, domain-specific adjustments are omitted, and a generalized assessment is performed.

low_df_justified

Logical. If TRUE the low degrees of freedom are justified and will be used for assessment. By default FALSE.

table_n_obj

Default NULL. Dataframe with the target sample size column n_obj and columns with the domains to evaluate. Its important check the domain columns type with table.

ratio_between_0_1

boolean. If TRUE, indicates that the estimator is a ratio between 0 and 1.

...

additional parameters for the evaluation. The complete list of parameters is: 1. General Parameters

  • df degrees of freedom. Default: 9.

  • n sample size. Default for chile scheme: 60. Default for CEPAL schemes: 100. Default for chile economic standard scheme: 30.

2. chile Parameters

  • cv_lower_ine lower limit for CV. Default: 0.15.

  • cv_upper_ine upper limit for CV. Default: 0.3.

3. CEPAL 2020 Parameters

  • cv_cepal limit for CV. Default: 0.2.

  • ess effective sample size. Default: 140.

  • unweighted unweighted count. Default: 50.

  • log_cv logarithmic coefficient of variation. Default: 0.175.

4. CEPAL 2023 Parameters

  • cv_lower_cepal lower limit for CV. Default: 0.2.

  • cv_upper_cepal upper limit for CV. Default: 0.3.

  • ess effective sample size. Default: 60.

  • cvlog_max maximum logarithmic coefficient of variation. Default: 0.175.

  • CCNP_b unweighted count before adjustment. Default: 50.

  • CCNP_a unweighted count after adjustment. Default: 30.

5. Chile Economic Survey Standard Parameters

  • cv_lower_econ lower limit for CV. Default: 0.2.

  • cv_upper_econ upper limit for CV. Default: 0.3.

Value

dataframe with all the columns included in the input table, plus a new column containing a label indicating the evaluation of each estimation: reliable, bit reliable, or unreliable.

Examples

dc <- survey::svydesign(ids = ~varunit, strata = ~varstrat, data = epf_personas, weights = ~fe)
assess(create_mean("gastot_hd", domains = "zona+sexo", design = dc))
#>   zona sexo    stat       se  df     n         cv                 eval_n
#> 1    1    1 1378016 69421.07 127 12300 0.05037756 sufficient sample size
#> 2    2    1 1082227 36324.22 121 10393 0.03356434 sufficient sample size
#> 3    1    2 1325015 68510.58 127 13729 0.05170551 sufficient sample size
#> 4    2    2 1088658 35343.76 121 11886 0.03246545 sufficient sample size
#>         eval_df    eval_cv    label
#> 1 sufficient df cv <= 0.15 reliable
#> 2 sufficient df cv <= 0.15 reliable
#> 3 sufficient df cv <= 0.15 reliable
#> 4 sufficient df cv <= 0.15 reliable