---
title: "iron inadequacy -2 without maize flour fortification"
author: "R Goto"
date: "2023-12-03"
output: html_document
---
## 9.1. Settings
```{r eval=FALSE}
library(tidyverse)
library(summarytools)
library(survey)
library(srvyr)
```

## 9.2. Dataset
```{r eval=FALSE}
nafetF2 <- read_csv("nafetF2.csv")  # estimated micronutrient intake per day per AFE in status quo and full fortification scenarios WITHOUT maize flour fortification
dataAw <- read_csv("dataAw.csv")  # data including weighting factors
```
options(scipen = 10, digits=3)

## 9.3. Full probability approach for estimaing iron inadequecy (see Allen et al. 2006, pages 156-159)

```{r eval=FALSE}
names(nafetF2)
nafetF2Aw <- left_join(nafetF2, dataAw, by = "y4_hhid")

## full fortification without maize
# in total
fe_probffm <- nafetF2Aw %>%
  mutate(feprob = case_when(
    feafetff <= 15 ~ 1,
    feafetff <= 16.7 & feafetff > 15 ~ 0.96,
    feafetff <= 18.7 & feafetff > 16.7 ~ 0.93,
    feafetff <= 21.4 & feafetff > 18.7 ~ 0.85,
    feafetff <= 23.6 & feafetff > 21.4 ~ 0.75,
    feafetff <= 25.7 & feafetff > 23.6 ~ 0.65,
    feafetff <= 27.8 & feafetff > 25.7 ~ 0.55,
    feafetff <= 30.2 & feafetff > 27.8 ~ 0.45,
    feafetff <= 33.2 & feafetff > 30.2 ~ 0.35,
    feafetff <= 37.3 & feafetff > 33.2 ~ 0.25,
    feafetff <= 45.0 & feafetff > 37.3 ~ 0.15,
    feafetff <= 53.5 & feafetff > 45.0 ~ 0.08,
    feafetff <= 63.0 & feafetff > 53.5 ~ 0.04,
    feafetff > 63 ~ 0)) %>% 
  count(feprob) %>% 
  mutate(feprev=feprob*n) %>% 
  summarise(sumprov=sum(feprev))
  
# by urban/rural
fe_probffmur <- nafetF2Aw %>%
  mutate(feprob = case_when(
    feafetff <= 15 ~ 1,
    feafetff <= 16.7 & feafetff > 15 ~ 0.96,
    feafetff <= 18.7 & feafetff > 16.7 ~ 0.93,
    feafetff <= 21.4 & feafetff > 18.7 ~ 0.85,
    feafetff <= 23.6 & feafetff > 21.4 ~ 0.75,
    feafetff <= 25.7 & feafetff > 23.6 ~ 0.65,
    feafetff <= 27.8 & feafetff > 25.7 ~ 0.55,
    feafetff <= 30.2 & feafetff > 27.8 ~ 0.45,
    feafetff <= 33.2 & feafetff > 30.2 ~ 0.35,
    feafetff <= 37.3 & feafetff > 33.2 ~ 0.25,
    feafetff <= 45.0 & feafetff > 37.3 ~ 0.15,
    feafetff <= 53.5 & feafetff > 45.0 ~ 0.08,
    feafetff <= 63.0 & feafetff > 53.5 ~ 0.04,
    feafetff > 63 ~ 0)) %>% 
  group_by(clustertype) %>% 
  count(feprob) %>% 
  mutate(feprev=feprob*n) %>% 
  summarise(sumprov=sum(feprev)) 

# by strata
fe_probffmdo <- nafetF2Aw %>%
  mutate(feprob = case_when(
    feafetff <= 15 ~ 1,
    feafetff <= 16.7 & feafetff > 15 ~ 0.96,
    feafetff <= 18.7 & feafetff > 16.7 ~ 0.93,
    feafetff <= 21.4 & feafetff > 18.7 ~ 0.85,
    feafetff <= 23.6 & feafetff > 21.4 ~ 0.75,
    feafetff <= 25.7 & feafetff > 23.6 ~ 0.65,
    feafetff <= 27.8 & feafetff > 25.7 ~ 0.55,
    feafetff <= 30.2 & feafetff > 27.8 ~ 0.45,
    feafetff <= 33.2 & feafetff > 30.2 ~ 0.35,
    feafetff <= 37.3 & feafetff > 33.2 ~ 0.25,
    feafetff <= 45.0 & feafetff > 37.3 ~ 0.15,
    feafetff <= 53.5 & feafetff > 45.0 ~ 0.08,
    feafetff <= 63.0 & feafetff > 53.5 ~ 0.04,
    feafetff > 63 ~ 0)) %>% 
  group_by(domain) %>% 
  count(feprob) %>% 
  mutate(feprev=feprob*n) %>% 
  summarise(sumprov=sum(feprev)) 
```