Apply bootstrap to RAM-OP indicators using a classical estimator.

Usage

estimate_classic(
  x,
  w,
  statistic = bbw::bootClassic,
  indicators = c("demo", "food", "hunger", "adl", "disability", "mental", "dementia",
    "health", "oedema", "screening", "income", "wash", "visual", "misc"),
  params = get_variables(indicators),
  outputColumns = params,
  replicates = 399
)

Arguments

x: Indicators dataset produced by create_op() with primary sampling unit (PSU) in column named "psu".
w: A data frame with primary sampling unit (PSU) in column named "psu" and survey weight (i.e. PSU population) in column named "pop".
statistic: A function operating on data in x. Fixed to bbw::bootClassic() function for means.
indicators: A character vector of indicator set names to estimate. Indicator set names are "demo", "food", "hunger", "disability", "adl", "mental", "dementia", "health", "income", "wash", "visual", and "misc". Default is all indicator sets.
params: Parameters (named columns in x) passed to the function specified in statistic. This is equivalent to variables corresponding to the indicator sets specified in indicators. The function get_variables() is used to specify these variables.
outputColumns: Names of columns in output data frame. This defaults to values specified in params.
replicates: Number of bootstrap replicates

Value

A tibble::tibble() of boot estimates using bbw::bootClassic() mean function

Examples

test <- estimate_classic(
  x = indicators.ALL, w = testPSU, replicates = 9
)
#> ✔ x has the appropriate/expected data structure
#> ✔ x has the appropriate/expected data structure
#> ✔ x has the appropriate/expected data structure

test
#> # A tibble: 136 × 10
#>    INDICATOR EST.ALL LCL.ALL UCL.ALL EST.MALES LCL.MALES UCL.MALES EST.FEMALES
#>    <chr>       <dbl>   <dbl>   <dbl>     <dbl>     <dbl>     <dbl>       <dbl>
#>  1 resp1      0.859   0.794   0.880     0.803     0.743     0.835      0.849  
#>  2 resp2      0.0885  0.075   0.152     0.1       0.0308    0.125      0.123  
#>  3 resp3      0.0417  0.0219  0.0729    0.0658    0.0309    0.166      0.0169 
#>  4 resp4      0       0       0.0229    0.0132    0         0.0889     0.00826
#>  5 age       70.7    69.4    72.1      71.0      69.3      72.5       71.1    
#>  6 ageGrp1    0       0       0         0         0         0          0      
#>  7 ageGrp2    0.536   0.441   0.586     0.470     0.423     0.609      0.517  
#>  8 ageGrp3    0.229   0.181   0.328     0.325     0.211     0.380      0.218  
#>  9 ageGrp4    0.198   0.123   0.277     0.153     0.0890    0.268      0.272  
#> 10 ageGrp5    0.0365  0.0219  0.0573    0.0488    0.0236    0.0747     0.0391 
#> # ℹ 126 more rows
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>