Check the plausibility and acceptability of weight-for-height z-score (WFHZ) data

Check the overall plausibility and acceptability of WFHZ data through a structured test suite encompassing checks for sampling and measurement-related biases in the dataset. The test suite, including the criteria and corresponding rating of acceptability, follows the standards in the SMART plausibility check.

The function works on a data frame returned by this package's wrangling functions for age and for WFHZ data.

Usage

mw_plausibility_check_wfhz(df, sex, age, weight, height, flags, ...)

Arguments

df: A tibble object to check.
sex: A numeric vector for child's sex with 1 = males and 2 = females.
age: A vector of class double of child's age in months.
weight: A vector of class double of child's weight in kilograms.
height: A vector of class double of child's height in centimeters.
flags: A numeric vector of flagged records.
...: A vector of class character, specifying the categories for which the analysis should be summarised for. Usually geographical areas. More than one vector can be specified.

Value

A single-row summary tibble with columns containing the plausibility check results. If ungrouped analysis, the output will consist of 19 columns and one row; otherwise, the number of columns will vary according to the number vectors specified, and the number of rows to the categories within the grouping variables.

References

SMART Initiative (2017). Standardized Monitoring and Assessment for Relief and Transition. Manual 2.0. Available at: https://smartmethodology.org.

Examples

## First wrangle age data ----
data <- mw_wrangle_age(
  df = anthro.01,
  dos = dos,
  dob = dob,
  age = age,
  .decimals = 2
)

## Then wrangle WFHZ data ----
data_wfhz <- mw_wrangle_wfhz(
  df = data,
  sex = sex,
  weight = weight,
  height = height,
  .recode_sex = TRUE
)
#> ================================================================================

## Now run the plausibility check ----
mw_plausibility_check_wfhz(
  df = data_wfhz,
  sex = sex,
  age = age,
  weight = weight,
  height = height,
  flags = flag_wfhz,
  area, team
)
#> # A tibble: 8 × 21
#> # Groups:   area, team [8]
#>   area      team     n flagged flagged_class sex_ratio sex_ratio_class age_ratio
#>   <chr>    <int> <int>   <dbl> <fct>             <dbl> <chr>               <dbl>
#> 1 Distric…     1   120  0      Excellent         0.784 Excellent           0.125
#> 2 Distric…     2   216  0.0139 Excellent         0.838 Excellent           0.919
#> 3 Distric…     3   104  0      Excellent         0.281 Excellent           1    
#> 4 Distric…     4    65  0.0154 Excellent         0.457 Excellent           0.249
#> 5 Distric…     1   200  0      Excellent         0.724 Excellent           0.183
#> 6 Distric…     6   140  0.0143 Excellent         0.447 Excellent           1    
#> 7 Distric…     7   188  0.0160 Excellent         0.512 Excellent           0.552
#> 8 Distric…    10   158  0.0190 Excellent         0.474 Excellent           0.270
#> # ℹ 13 more variables: age_ratio_class <chr>, dps_wgt <dbl>,
#> #   dps_wgt_class <chr>, dps_hgt <dbl>, dps_hgt_class <chr>, sd <dbl>,
#> #   sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>, kurt_class <fct>,
#> #   quality_score <dbl>, quality_class <fct>