Check the plausibility and acceptability of raw MUAC data
Source:R/plausibility_check_muac.R
mw_plausibility_check_muac.Rd
Check the overall plausibility and acceptability of raw MUAC data through a structured test suite encompassing sampling and measurement-related biases checks in the data set. The test suite in this function follows the recommendation made by Bilukha, O., & Kianian, B. (2023).
Value
A summarized table of class data.frame
, of length 9 and width 1, for
the plausibility test results and their respective acceptability ratings.
Details
Cut-off points used for the percent of flagged records:
Excellent | Good | Acceptable | Problematic |
0.0 - 1.0 | >1.0 - 1.5 | >1.5 - 2.0 | >2.0 |
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.
Examples
## First wranlge MUAC data ----
df_muac <- mw_wrangle_muac(
df = anthro.01,
sex = sex,
muac = muac,
age = NULL,
.recode_sex = TRUE,
.recode_muac = FALSE,
.to = "none"
)
## Then run the plausibility check ----
mw_plausibility_check_muac(
df = df_muac,
flags = flag_muac,
sex = sex,
muac = muac
)
#> # A tibble: 1 × 9
#> n flagged flagged_class sex_ratio sex_ratio_class dps dps_class sd
#> <int> <dbl> <fct> <dbl> <chr> <dbl> <chr> <dbl>
#> 1 1191 0.00252 Excellent 0.297 Excellent 5.39 Excellent 11.1
#> # ℹ 1 more variable: sd_class <fct>