Function to 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_all 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 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

Tibble of boot estimates using bootClassic mean function

Examples

  #
  test <- estimate_classic(x = indicators.ALL,
                           w = testPSU,
                           replicates = 9)

  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>