Accept or reject sampling units against population density of potential sampling units

accept_reject_psu(
  x,
  svy,
  psu = "psu",
  match = NULL,
  pop = "pop",
  verbose = TRUE,
  show_plot = TRUE,
  save_plot = FALSE
)

Arguments

x	A data.frame of all potential primary sampling units (PSUs) to sample from each uniquely identified and with corresponding populations.
svy	A data.frame of survey data drawn via probability proportional to population size (PPS) approach from all the potential PSUs in x.
psu	A single character value or vector of values corresponding to the variable name/s for the primary sampling unit in x and svy. Default is "psu".
match	A single character value corresponding to the variable name for the primary sampling unit in x that matches the primary sampling unit in svy. Default is NULL which indicates that primary sampling unit in x is the same as primary sampling unit in svy.
pop	A single character value corresponding to the variable name for the population figures in x. Default is "pop".
verbose	Logical. Should text of sample acceptance or rejection be printed/shown? Default to TRUE.
show_plot	Logical. Should plot of sample acceptance or rejection be shown? Default to TRUE.
save_plot	Logical. Should plot of sample acceptance or rejection be saved? Default to FALSE. If set to TRUE when show_plot is FALSE, no plot will be saved.

Value

A data.frame drawn from svy that contains the primary sampling units selected by the acceptance and rejection sampling.

Author

Mark Myatt and Ernest Guevarra

Examples

accept_reject_psu(
  x = village_list,
  svy = sample_data,
  psu = c("id", "psu"),
  match = "cluster",
  pop = "population",
  verbose = FALSE,
  show_plot = FALSE
 )
#> # A tibble: 400 × 11
#>    surveydate   psu   sex birthdate    age weight height oedema  muac measure
#>    <date>     <dbl> <int> <date>     <int>  <dbl>  <dbl>  <int> <int> <chr>  
#>  1 2020-12-19     1     2 NA             8    8.8   66.1      0   163 l      
#>  2 2020-12-19     1     1 2020-03-20     9    8.6   72.4      0   149 l      
#>  3 2020-12-19     1     2 2019-04-16    20   10.1   79.6      0   142 l      
#>  4 2020-12-19     1     2 2018-12-05    24    9     79.2      0   136 h      
#>  5 2020-12-19     1     1 2018-11-12    25   13.3   86.8      0   157 h      
#>  6 2020-12-19     1     2 NA            28    9.2   85.9      0   118 h      
#>  7 2020-12-19     1     1 2018-01-16    35   15.1   95.6      0   147 h      
#>  8 2020-12-19     1     2 2017-12-02    36   12.6   94.5      0   148 h      
#>  9 2020-12-19     1     1 2017-08-17    40   13.2   86.1      0   166 h      
#> 10 2020-12-19     1     1 2017-02-09    46   13.9   91.3      0   170 h      
#> # … with 390 more rows, and 1 more variable: clothes <chr>