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

Source: R/plausibility_check_wfhz.R
mw_plausibility_check_wfhz.Rd

Check the overall plausibility and acceptability of WFHZ data through a structured test suite encompassing sampling and measurement-related biases checks in the data set. The test suite, including the criteria and corresponding rating of acceptability, follows the standards in the SMART plausibility check. The only exception is the exclusion of MUAC checks. MUAC is checked separately using more comprehensive test suite as well.

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

Usage

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

Arguments

df

A data set object of class data.frame to check.

sex

A vector of class numeric of child's sex.

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 vector of class numeric of flagged records.

Value

A summarized table of class data.frame, of length 19 and width 1, for the plausibility test results and their respective acceptability rates.

References

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

See also

mw_plausibility_check_mfaz() mw_plausibility_check_muac() mw_wrangle_age()

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
)
#> # A tibble: 1 × 19
#>       n flagged flagged_class sex_ratio sex_ratio_class age_ratio
#>   <int>   <dbl> <fct>             <dbl> <chr>               <dbl>
#> 1  1191  0.0101 Excellent         0.297 Excellent           0.409
#> # 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>