In mabafaba/msni19:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

For instructions on using this package, please see thepackage Manual.

Rationale

• Wash, fsl and capacity gaps can each drive up the msni by themselved (i.e. if Wash is 3, the msni will be at least 3 no matter what)
  • capacity gaps is included to take into account that households may have low living standard gaps at the expense of extreme coping strategies
• Health, protection and shelter drive up the msni in combinations of two.
• education: Education living standard gaps contribute to the msni, but do not produce "extreme" (4) situations if needs are already high in for example food security and wash
• impact: this is used as a "contributing factor"; with high impact scores, any sectoral sub index can drive up the msni

Implementation

Description

The calculation can be described as follows:

For each household individually...

1. set msni to the largest value of health, protection and shelter
2. set msni to the impact score, if impact is lower than the previous msni.
3. set msni to the largets value of msni, wash, fsl, capacity gaps if that is larger than the previous msni
4. set msni to 2 if and only if
   1. msni is currently 1
   2. and one of health, protection, shelter and education (or one of the additionally supplied indices) is larger or equal 3
5. set msni to NA if any of the input scores was NA
6. done!

Code

Here is the actual code.

#  1. set `msni` to the largest value of `health`, `protection` and `shelter`
msni <- pmax(health_lsg,protection_lsg,shelter_lsg)

#  2. set `msni` to the `impact` score, if `impact` is lower than the previous `msni`. 
msni <- pmin(msni, impact)

#  3. set `msni` to the largets value of `msni`, `wash`, `fsl`, `capacity gaps` if that is larger than the previous `msni`
largest_lsg_combination_hps <- pmax(
  pmin(health_lsg,protection_lsg),
  pmin(health_lsg, shelter_lsg),
  pmin(protection_lsg,shelter_lsg)
)

msni <- pmax(msni, largest_lsg_combination_hps)

# If higher than current, replace with highest from wash, FSL or capacity gap
msni <- pmax(msni, wash_lsg,fsl_lsg,capacity_gaps)


#4.A is `msni` is currently `1` _and_ one of health, protection, shelter and education is larger than `3` ?

max_of_remaining <- c(custom_input_indices,list(health_lsg,protection_lsg,shelter_lsg,education_lsg)) %>%
  as.data.frame(stringsAsFactors = FALSE) %>%
  apply(1,max,na.rm = FALSE)
max_of_remaining_larger_equal_3 <- max_of_remaining >= 3
note_applies<-(msni ==1) & max_of_remaining_larger_equal_3

# 4.B set `msni` to `2` where condition above applies
msni[note_applies]<- 2

Dictionary for non-programmers:

• <- means "resplace" or "assign" the value to the name
• pmin() the smallest of the values in parenthesis (per record)
• pmax() the largest of the values in parenthesis (per record)
• variable[condition] <- replace / assign only if/where condition is met

mabafaba/msni19 documentation built on Aug. 28, 2019, 9:51 a.m.