Package index • mwana

Description

mwana mwana-package: mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R

Built-in data sets

anthro.01: A sample data of district level SMART surveys with location anonymised

anthro.02: A sample of an already wrangled survey data

anthro.03: A sample data of district level SMART surveys conducted in Mozambique

anthro.04: A sample data of a community-based sentinel site from an anonymized location

mfaz.01: A sample MUAC screening data from an anonymized setting

mfaz.02: A sample SMART survey data with MUAC

wfhz.01: A sample SMART survey data with WFHZ standard deviation rated as problematic

Wrangle data

mw_wrangle_age(): Wrangle child's age

mw_wrangle_wfhz(): Wrangle weight-for-height data

mw_wrangle_muac(): Wrangle MUAC data

Statistical tests

mw_stattest_ageratio(): Test for statistical difference between the proportion of children aged 24 to 59 months old over those aged 6 to 23 months old

IPC Acute Malnutrition checks

mw_check_ipcamn_ssreq(): Check whether IPC Acute Malnutrition (IPC AMN) sample size requirements were met

Plausibility check

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

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

mw_plausibility_check_muac(): Check the plausibility and acceptability of raw MUAC data

Neat output tables

mw_neat_output_wfhz(): Clean and format the output table returned from the WFHZ plausibility check for improved clarity and readability

mw_neat_output_mfaz(): Clean and format the output table returned from the MFAZ plausibility check for improved clarity and readability

mw_neat_output_muac(): Clean and format the output table returned from the MUAC plausibility check for improved clarity and readability.

Estimate prevalence of wasting

mw_estimate_prevalence_wfhz(): Estimate the prevalence of wasting based on z-scores of weight-for-height (WFHZ)

mw_estimate_prevalence_muac() mw_estimate_smart_age_wt(): Estimate the prevalence of wasting based on MUAC for survey data

mw_estimate_prevalence_mfaz(): Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ)

mw_estimate_prevalence_combined(): Estimate the prevalence of combined wasting

mw_estimate_prevalence_screening(): Estimate the prevalence of wasting based on MUAC for non survey data

Utilities

get_age_months(): Calculate child's age in months

recode_muac(): Convert MUAC values to either centimeters or millimeters

flag_outliers() remove_flags(): Identify, flag outliers and remove them

define_wasting(): Define wasting