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

Usage

estimate_probit(
  x,
  w,
  gam.stat = probit_gam,
  sam.stat = probit_sam,
  params = "MUAC",
  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".
gam.stat: A function operating on data in x to estimate GAM prevalence for older people. Fixed to probit_gam().
sam.stat: A function operating on data in x to estimate SAM prevalence for older people. Fixed to probit_sam().
params: Parameters (named columns in x) passed to the function specified in statistic; fixed to "MUAC" as indicator amenable to probit estimation.
outputColumns: Names of columns in output data frame.
replicates: Number of bootstrap replicate case and non-case.

Value

A tibble::tibble() of boot estimates using PROBIT.

Examples

test <- estimate_probit(x = indicators.ALL, w = testPSU, replicates = 3)
#> ✔ x has the appropriate/expected data structure
#> ✔ x has the appropriate/expected data structure
#> ✔ x has the appropriate/expected data structure
#> ✔ x has the appropriate/expected data structure
#> ✔ x has the appropriate/expected data structure
#> ✔ x has the appropriate/expected data structure

test
#> # A tibble: 3 × 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 GAM       0.0419     9.65e-3 4.57e-2  6.65e- 3  3.33e- 4   1.24e-2     0.0439 
#> 2 MAM       0.0419     9.65e-3 4.57e-2  6.65e- 3  3.33e- 4   1.24e-2     0.0411 
#> 3 SAM       0.0000187  9.33e-7 2.26e-5  3.25e-19  1.63e-20   1.08e-7     0.00282
#> # ℹ 2 more variables: LCL.FEMALES <dbl>, UCL.FEMALES <dbl>