nipnTK (version 0.2.0)

greensIndex: Green's Index of Dispersion

Description

Implementation of the Green's Index of Dispersion by bootstrap. The sampling distribution of the Green's Index is not well described hence bootstrapping is used to test whether the distribution of cases across primary sampling units is random.

Usage

greensIndex(data, psu, case, replicates = 999)

Value

A list of class GI with names:

Variable	Description
GI	Estimate of Green's index
LCL	95\% LCL for GI
UCL	95\% UCL for GI
minGI	Minimum possible GI (maximum uniformity) for the data
p	p-value (H0: = Random distribution of cases across PSUs)

Arguments

data

Survey dataset (as an R data.frame)

psu

Name of variable holding PSU (cluster) data as a character vector of length = 1 (e.g. psu)

case

Name of variable holding case status as a character vector of length = 1 (e.g. GAM). The function assumes that case status is coded with 1 = case

replicates

Number of bootstrap replicates (default is 9999)

Details

The value of Green's Index can range between -1/(n - 1) for maximum uniformity (specific to the dataset) and one for maximum clumping. The interpretation of Green’s Index is straightforward:

Green's Index Value	Interpretation
Green's Index close to 0	Random
Green's Index greater than 0	Clumped (i.e. more clumped than random)
Green’s Index less than 0	Uniform (i.e. more uniform than random)

Examples

# Apply Green's Index using anthropometric data from a SMART survey in Sudan
# (flag.ex01)
svy <- flag.ex01
svy$flag <- 0
svy$flag <- ifelse(!is.na(svy$haz) & (svy$haz < -6 | svy$haz > 6),
                   svy$flag + 1, svy$flag)
svy$flag <- ifelse(!is.na(svy$whz) & (svy$whz < -5 | svy$whz > 5),
                   svy$flag + 2, svy$flag)
svy$flag <- ifelse(!is.na(svy$waz) & (svy$waz < -6 | svy$waz > 5),
                   svy$flag + 4, svy$flag)
svy <- svy[svy$flag == 0, ]
svy$stunted <- ifelse(svy$haz < -2, 1, 2)

## set seed to 0 to replicate results
set.seed(0)
greensIndex(data = svy, psu = "psu", case = "stunted")