Check the plausibility and acceptability of muac-for-age z-score (MFAZ) data

Check the overall plausibility and acceptability of MFAZ data through a structured test suite encompassing sampling and measurement-related biases checks in the dataset. The test suite in this function follows the recommendation made by Bilukha, O., & Kianian, B. (2023) on the plausibility of constructing a comprehensive plausibility check similar to WFHZ to evaluate the acceptability of MUAC data when the variable age exists in the dataset.

The function works on a data frame returned from this package's wrangling function for age and for MFAZ data.

Usage

mw_plausibility_check_mfaz(df, sex, muac, age, flags)

Arguments

df

A dataset object of class data.frame to check.

sex

A vector of class numeric of child's sex.

muac

A vector of class numeric of child's MUAC in centimeters.

age

A vector of class double of child's age in months.

flags

A vector of class numeric of flagged records.

Value

A summarised table of class data.frame, of length 17 and width 1, for the plausibility test results and their respective acceptability ratings.

References

Bilukha, O., & Kianian, B. (2023). Considerations for assessment of measurement quality of mid‐upper arm circumference data in anthropometric surveys and mass nutritional screenings conducted in humanitarian and refugee settings. Maternal & Child Nutrition, 19, e13478. https://doi.org/10.1111/mcn.13478

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

See also

mw_wrangle_age() mw_wrangle_muac() mw_stattest_ageratio() flag_outliers()

Examples

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

## Then wrangle MUAC data ----
data_muac <- mw_wrangle_muac(
  df = data,
  sex = sex,
  age = age,
  muac = muac,
  .recode_sex = TRUE,
  .recode_muac = TRUE,
  .to = "cm"
)
#> ================================================================================

## And finally run plausibility check ----
mw_plausibility_check_mfaz(
  df = data_muac,
  flags = flag_mfaz,
  sex = sex,
  muac = muac,
  age = age
)
#> # A tibble: 1 × 17
#>       n flagged flagged_class sex_ratio sex_ratio_class age_ratio
#>   <int>   <dbl> <fct>             <dbl> <chr>               <dbl>
#> 1  1191 0.00504 Excellent         0.297 Excellent           0.636
#> # ℹ 11 more variables: age_ratio_class <chr>, dps <dbl>, dps_class <chr>,
#> #   sd <dbl>, sd_class <chr>, skew <dbl>, skew_class <fct>, kurt <dbl>,
#> #   kurt_class <fct>, quality_score <dbl>, quality_class <fct>